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Add NT v2 100m model for sequence conservation prediction

Browse files
Files changed (8) hide show
  1. README.md +62 -3
  2. config.json +45 -0
  3. esm_config.py +379 -0
  4. model.safetensors +3 -0
  5. modeling_esm.py +1446 -0
  6. special_tokens_map.json +6 -0
  7. tokenizer_config.json +44 -0
  8. vocab.txt +4107 -0
README.md CHANGED
@@ -1,3 +1,62 @@
1
- ---
2
- license: cc-by-nc-sa-4.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-sa-4.0
3
+ widget:
4
+ - text: ATTTTGGAAATCGGAAATCTTCTGTGTTTATCCGTACCAATCCCTGGATCAGTAGTTGGTACACACATACATAGTTGGATCACAACATATCACGAATGAATTTAGGCTAAAAGAGTTAAATACTTACATTAGGGCCAGGTAGGCCAACAACTATCAGAGAACAACAGCGGAAGACAAAATAATATAAGGGCCCGGTTAACATGCCACAAGCAGTCGACTGGGGAACGAGACCTAGAACAAGACCGCACTCCGATCATCTTGTGGGATACGCAAGCGTACCGACAAGGGCTTCTCTTCAACACTCTCCTAAAAGATATATAAATAGCAAGGGTGAGTACCAACCGTACTCAGCAAGCCACCACAACAACAATGCGTATGATAGAGGGTATTTCAAGGAATGGCTTCAGGTTCTTTTGCATAAAGCTAATTTTACAATTCTTTTCACAAGCCTAAAACCTAGCATAGACTGATCAAATTTTAGTACCAGTGTTCACTTTAAACAACGACGGTTCTGTCCACCATCCATTGTGATCCCAAGGATAGCTTCCCGCCATTGAATCGTCATGGTTTTCTAAGGATGTCCACCTTCCCTCCTCTCGGGAAGTGGCTCCATCAGCATAAAATTCATCATGCAATATCCCATCCCCCACAAGTTAAAAATTTAGAGTCTAGCCAAGTGTAATACATGTCCCGGTGCTCAATAACCGCGAGCACGGCTATTCGAATAGATTTGGTTTACTCACACTGCAGTGGATGTACACTTTACCCGCACTCCGCAACTGCCCAACACATGAGCCTCGTCCGAACACATGGGACGCGTCACGGCAAAGCTTTTCGATAACCTCGCATTGGTAGTACCCGCTCCATGAACTTAAATCCTCATGCACTCTAGGCGTCCATGTTTCTAGCAGTGAGAGGAGTTCTGGCGCTCCCGGGAAAGAGAAGTCTCACACGCATATTAAATTATGGTTCAAGTTAAGTTCTCTCTCTCACACACTCA
5
+ tags:
6
+ - DNA
7
+ - biology
8
+ - genomics
9
+ ---
10
+ # Plant foundation DNA large language models
11
+
12
+ The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
13
+ All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.
14
+
15
+
16
+ **Developed by:** zhangtaolab
17
+
18
+ ### Model Sources
19
+
20
+ - **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
21
+ - **Manuscript:** [Versatile applications of foundation DNA large language models in plant genomes]()
22
+
23
+ ### Architecture
24
+
25
+ The model is trained based on the InstaDeepAI/nucleotide-transformer-v2-100m-multi-species model with modified tokenizer that replaces k-mer to BPE.
26
+
27
+ This model is fine-tuned for predicting sequence conservation.
28
+
29
+ ### How to use
30
+
31
+ Install the runtime library first:
32
+ ```bash
33
+ pip install transformers
34
+ ```
35
+
36
+ Here is a simple code for inference:
37
+ ```python
38
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
39
+
40
+ model_name = 'nucleotide-transformer-v2-100m-conservation'
41
+ # load model and tokenizer
42
+ model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
43
+ tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
44
+
45
+ # inference
46
+ sequences = ['ACATGCTAAATTAGTTGGCAATTTTTTCTCAGGTAGCTGGGCACAATTTGGTAGTCCAGTTGAACAAAATCCATTAGCTTCTTTTAGCAAGTCCCCTGGTTTGGGCCCTGCCAGTCCCATTAATACCAACCATTTGTCTGGATTGGCTGCAATTCTTTCCCCACAAGCAACAACCTCTACCAAGATTGCACCGATTGGCAAGGACCCTGGAAGGGCTGCAAATCAGATGTTTTCTAACTCTGGATCAACACAAGGAGCAGCTTTTCAGCATTCTATATCCTTTCCTGAGCAAAATGTAAAGGCAAGTCCTAGGCCTATATCTACTTTTGGTGAATCAAGTTCTAGTGCATCAAGTATTGGAACACTGTCCGGTCCTCAATTTCTTTGGGGAAGCCCAACTCCTTACTCTGAGCATTCAAACACTTCTGCCTGGTCTTCATCTTCGGTGGGGCTTCCATTTACATCTAGTGTCCAAAGGCAGGGTTTCCCATATACTAGTAATCACAGTCCTTTTCTTGGCTCCCACTCTCATCATCATGTTGGATCTGCTCCATCTGGCCTTCCGCTTGATAGGCATTTTAGCTACTTCCCTGAGTCACCTGAAGCTTCTCTCATGAGCCCGGTTGCATTTGGGAATTTAAATCACGGTGATGGGAATTTTATGATGAACAACATTAGTGCTCGTGCATCTGTAGGAGCCGGTGTTGGTCTTTCTGGAAATACCCCTGAAATTAGTTCACCCAATTTCAGAATGATGTCTCTGCCTAGGCATGGTTCCTTGTTCCATGGAAATAGTTTGTATTCTGGACCTGGAGCAACTAACATTGAGGGATTAGCTGAACGTGGACGAAGTAGACGACCTGAAAATGGTGGGAACCAAATTGATAGTAAGAAGCTGTACCAGCTTGATCTTGACAAAATCGTCTGTGGTGAAGATACAAGGACTACTTTAATGATTAAAAACATTCCTAACAAGTAAGAATAACTAAACATCTATCCT',
47
+ 'GTCGCAAAAATTGGGCCACTTGCAGTTCAATCTGTTTAATCAAAATTGCATGTGTATCAACTTTTTGCCCAATACTAGCTATATCACACCTCAACTCTTTAATGTGTTCATCACTAGTGTCGAACCTCCTCATCATTTTGTCCAACATATCCTCAACTCGCGCCATACTATCTCCACCATCCCTAGGAGTAACTTCACGATTTTGAGGAGGGACATAGGGCCCATTCCTGTCGTTTCTATTAGCATAGTTACTCCTGTTAAAGTTGTTGTCGCGGTTGTAGTTTCCATCACGTACATAATGACTCTCACGGTTGTAGTTACCATAGTTCCGACCTGGGTTCCCTTGAACTTGGCGCCAGTTATCCTGATTTGAGCCTTGGGCGCTTGGTCGGAAACCCCCTGTCTGCTCATTTACTGCATAAGTGTCCTCCGCGTAACATCATTAGGAGGTGGTGGTTTAGCAAAGTAGTTGACTGCATTTATCTTTTCTGCACCCCCTGTGACATTTTTTAGTACCAACCCAAGCTCAGTTCTCATCTGAGACATTTCTTCTCGAATCTCATCTGTGGCTCGGTTGTGAGTGGACTGCACTACGAAGGTGTTTTTCCCTGTATCAAACTTCCTAGTACTCCAAGCTTTGTTATTTCGGGAGATTTTCTCTAGTTTTTCTGCAATCTCAACATAAGTGCATTCTCCATAAGATCCACCTGCTATAGTGTCCAACACCGCTTTATTGTTATCATCCTGTCCCCGATAGAAGTATTCCTTCAGTGACTCATCATCTATACGGTGATTTAGAACACTTCTCAAGAATGAGGTGAATCTATCCCAAGAACTACTAACTAACTCTCCTGGTAGTGCCACAAAGCTGTTCACCCTTTCTTTGTGGTTTAACTTCTTGGAGATCGGATAGTAGCGTGCTAAGAAGACATCCCTTAGTTGGTTCCAAGTGAATATGGAGTTGTATGCGAGCTTAGTGAACCACATTGCAGCCTCTCCC']
48
+ pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
49
+ trust_remote_code=True, top_k=None)
50
+ results = pipe(sequences)
51
+ print(results)
52
+
53
+ ```
54
+
55
+
56
+ ### Training data
57
+ We use EsmForSequenceClassification to fine-tune the model.
58
+ Detailed training procedure can be found in our manuscript.
59
+
60
+
61
+ #### Hardware
62
+ Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).
config.json ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "NT_v2_100m_conservation",
3
+ "add_bias_fnn": false,
4
+ "architectures": [
5
+ "EsmForSequenceClassification"
6
+ ],
7
+ "attention_probs_dropout_prob": 0.0,
8
+ "auto_map": {
9
+ "AutoConfig": "esm_config.EsmConfig",
10
+ "AutoModel": "modeling_esm.EsmModel",
11
+ "AutoModelForMaskedLM": "modeling_esm.EsmForMaskedLM",
12
+ "AutoModelForSequenceClassification": "modeling_esm.EsmForSequenceClassification"
13
+ },
14
+ "emb_layer_norm_before": false,
15
+ "esmfold_config": null,
16
+ "hidden_dropout_prob": 0.0,
17
+ "hidden_size": 512,
18
+ "id2label": {
19
+ "0": "Not conserved",
20
+ "1": "Conserved"
21
+ },
22
+ "initializer_range": 0.02,
23
+ "intermediate_size": 2048,
24
+ "is_folding_model": false,
25
+ "label2id": {
26
+ "Not conserved": 0,
27
+ "Conserved": 1
28
+ },
29
+ "layer_norm_eps": 1e-12,
30
+ "mask_token_id": 2,
31
+ "max_position_embeddings": 2050,
32
+ "model_type": "esm",
33
+ "num_attention_heads": 16,
34
+ "num_hidden_layers": 22,
35
+ "pad_token_id": 1,
36
+ "position_embedding_type": "rotary",
37
+ "problem_type": "single_label_classification",
38
+ "tie_word_embeddings": false,
39
+ "token_dropout": false,
40
+ "torch_dtype": "float32",
41
+ "transformers_version": "4.39.1",
42
+ "use_cache": false,
43
+ "vocab_list": null,
44
+ "vocab_size": 4107
45
+ }
esm_config.py ADDED
@@ -0,0 +1,379 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ ESM model configuration"""
16
+
17
+ from dataclasses import asdict, dataclass
18
+ from typing import Optional
19
+
20
+ from transformers import PretrainedConfig, logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ # TODO Update this
25
+ ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
26
+ "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
27
+ # See all ESM models at https://huggingface.co/models?filter=esm
28
+ }
29
+
30
+
31
+ class EsmConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
34
+ according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the ESM
36
+ [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
37
+
38
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
39
+ documentation from [`PretrainedConfig`] for more information.
40
+
41
+
42
+ Args:
43
+ vocab_size (`int`, *optional*):
44
+ Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
45
+ `inputs_ids` passed when calling [`ESMModel`].
46
+ mask_token_id (`int`, *optional*):
47
+ The index of the mask token in the vocabulary. This must be included in the config because of the
48
+ "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
49
+ pad_token_id (`int`, *optional*):
50
+ The index of the padding token in the vocabulary. This must be included in the config because certain parts
51
+ of the ESM code use this instead of the attention mask.
52
+ hidden_size (`int`, *optional*, defaults to 768):
53
+ Dimensionality of the encoder layers and the pooler layer.
54
+ num_hidden_layers (`int`, *optional*, defaults to 12):
55
+ Number of hidden layers in the Transformer encoder.
56
+ num_attention_heads (`int`, *optional*, defaults to 12):
57
+ Number of attention heads for each attention layer in the Transformer encoder.
58
+ intermediate_size (`int`, *optional*, defaults to 3072):
59
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
60
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
61
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
62
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
63
+ The dropout ratio for the attention probabilities.
64
+ max_position_embeddings (`int`, *optional*, defaults to 1026):
65
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
66
+ just in case (e.g., 512 or 1024 or 2048).
67
+ initializer_range (`float`, *optional*, defaults to 0.02):
68
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
69
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
70
+ The epsilon used by the layer normalization layers.
71
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
72
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
73
+ For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
74
+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
75
+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
76
+ with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
77
+ is_decoder (`bool`, *optional*, defaults to `False`):
78
+ Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
79
+ use_cache (`bool`, *optional*, defaults to `True`):
80
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
81
+ relevant if `config.is_decoder=True`.
82
+ emb_layer_norm_before (`bool`, *optional*):
83
+ Whether to apply layer normalization after embeddings but before the main stem of the network.
84
+ token_dropout (`bool`, defaults to `False`):
85
+ When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
86
+
87
+ Examples:
88
+
89
+ ```python
90
+ >>> from transformers import EsmModel, EsmConfig
91
+
92
+ >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
93
+
94
+ >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
95
+
96
+ >>> # Accessing the model configuration >>> configuration = model.config
97
+ ```"""
98
+ model_type = "esm"
99
+
100
+ def __init__(
101
+ self,
102
+ vocab_size=None,
103
+ mask_token_id=None,
104
+ pad_token_id=None,
105
+ hidden_size=768,
106
+ num_hidden_layers=12,
107
+ num_attention_heads=12,
108
+ intermediate_size=3072,
109
+ hidden_dropout_prob=0.1,
110
+ attention_probs_dropout_prob=0.1,
111
+ max_position_embeddings=1026,
112
+ initializer_range=0.02,
113
+ layer_norm_eps=1e-12,
114
+ position_embedding_type="absolute",
115
+ use_cache=True,
116
+ emb_layer_norm_before=None,
117
+ token_dropout=False,
118
+ is_folding_model=False,
119
+ esmfold_config=None,
120
+ vocab_list=None,
121
+ add_bias_fnn=True,
122
+ **kwargs,
123
+ ):
124
+ super().__init__(
125
+ pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
126
+ )
127
+
128
+ self.vocab_size = vocab_size
129
+ self.hidden_size = hidden_size
130
+ self.num_hidden_layers = num_hidden_layers
131
+ self.num_attention_heads = num_attention_heads
132
+ self.intermediate_size = intermediate_size
133
+ self.hidden_dropout_prob = hidden_dropout_prob
134
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
135
+ self.max_position_embeddings = max_position_embeddings
136
+ self.initializer_range = initializer_range
137
+ self.layer_norm_eps = layer_norm_eps
138
+ self.position_embedding_type = position_embedding_type
139
+ self.use_cache = use_cache
140
+ self.emb_layer_norm_before = emb_layer_norm_before
141
+ self.token_dropout = token_dropout
142
+ self.is_folding_model = is_folding_model
143
+ # Arguments needed for Dalmatian
144
+ self.add_bias_fnn = add_bias_fnn
145
+ if is_folding_model:
146
+ if esmfold_config is None:
147
+ logger.info(
148
+ "No esmfold_config supplied for folding model, using default values."
149
+ )
150
+ esmfold_config = EsmFoldConfig()
151
+ elif isinstance(esmfold_config, dict):
152
+ esmfold_config = EsmFoldConfig(**esmfold_config)
153
+ self.esmfold_config = esmfold_config
154
+ if vocab_list is None:
155
+ logger.warning(
156
+ "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
157
+ )
158
+ self.vocab_list = get_default_vocab_list()
159
+ else:
160
+ self.vocab_list = vocab_list
161
+ else:
162
+ self.esmfold_config = None
163
+ self.vocab_list = None
164
+ if self.esmfold_config is not None and getattr(
165
+ self.esmfold_config, "use_esm_attn_map", False
166
+ ):
167
+ raise ValueError(
168
+ "The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"
169
+ )
170
+
171
+ def to_dict(self):
172
+ """
173
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
174
+
175
+ Returns:
176
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
177
+ """
178
+ output = super().to_dict()
179
+ if isinstance(self.esmfold_config, EsmFoldConfig):
180
+ output["esmfold_config"] = self.esmfold_config.to_dict()
181
+ return output
182
+
183
+
184
+ @dataclass
185
+ class EsmFoldConfig:
186
+ esm_type: str = None
187
+ fp16_esm: bool = True
188
+ use_esm_attn_map: bool = False
189
+ esm_ablate_pairwise: bool = False
190
+ esm_ablate_sequence: bool = False
191
+ esm_input_dropout: float = 0
192
+
193
+ embed_aa: bool = True
194
+ bypass_lm: bool = False
195
+
196
+ lddt_head_hid_dim: int = 128
197
+ trunk: "TrunkConfig" = None
198
+
199
+ def __post_init__(self):
200
+ if self.trunk is None:
201
+ self.trunk = TrunkConfig()
202
+ elif isinstance(self.trunk, dict):
203
+ self.trunk = TrunkConfig(**self.trunk)
204
+
205
+ def to_dict(self):
206
+ """
207
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
208
+
209
+ Returns:
210
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
211
+ """
212
+ output = asdict(self)
213
+ output["trunk"] = self.trunk.to_dict()
214
+ return output
215
+
216
+
217
+ @dataclass
218
+ class TrunkConfig:
219
+ num_blocks: int = 48
220
+ sequence_state_dim: int = 1024
221
+ pairwise_state_dim: int = 128
222
+ sequence_head_width: int = 32
223
+ pairwise_head_width: int = 32
224
+ position_bins: int = 32
225
+ dropout: float = 0
226
+ layer_drop: float = 0
227
+ cpu_grad_checkpoint: bool = False
228
+ max_recycles: int = 4
229
+ chunk_size: Optional[int] = 128
230
+ structure_module: "StructureModuleConfig" = None
231
+
232
+ def __post_init__(self):
233
+ if self.structure_module is None:
234
+ self.structure_module = StructureModuleConfig()
235
+ elif isinstance(self.structure_module, dict):
236
+ self.structure_module = StructureModuleConfig(**self.structure_module)
237
+
238
+ if self.max_recycles <= 0:
239
+ raise ValueError(
240
+ f"`max_recycles` should be positive, got {self.max_recycles}."
241
+ )
242
+ if self.sequence_state_dim % self.sequence_state_dim != 0:
243
+ raise ValueError(
244
+ "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
245
+ f" {self.sequence_state_dim} and {self.sequence_state_dim}."
246
+ )
247
+ if self.pairwise_state_dim % self.pairwise_state_dim != 0:
248
+ raise ValueError(
249
+ "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
250
+ f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
251
+ )
252
+
253
+ sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
254
+ pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
255
+
256
+ if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
257
+ raise ValueError(
258
+ "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
259
+ f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
260
+ )
261
+ if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
262
+ raise ValueError(
263
+ "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
264
+ f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
265
+ )
266
+ if self.pairwise_state_dim % 2 != 0:
267
+ raise ValueError(
268
+ f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
269
+ )
270
+
271
+ if self.dropout >= 0.4:
272
+ raise ValueError(
273
+ f"`dropout` should not be greater than 0.4, got {self.dropout}."
274
+ )
275
+
276
+ def to_dict(self):
277
+ """
278
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
279
+
280
+ Returns:
281
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
282
+ """
283
+ output = asdict(self)
284
+ output["structure_module"] = self.structure_module.to_dict()
285
+ return output
286
+
287
+
288
+ @dataclass
289
+ class StructureModuleConfig:
290
+ """
291
+ Args:
292
+ sequence_dim:
293
+ Single representation channel dimension
294
+ pairwise_dim:
295
+ Pair representation channel dimension
296
+ ipa_dim:
297
+ IPA hidden channel dimension
298
+ resnet_dim:
299
+ Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
300
+ num_heads_ipa:
301
+ Number of IPA heads
302
+ num_qk_points:
303
+ Number of query/key points to generate during IPA
304
+ num_v_points:
305
+ Number of value points to generate during IPA
306
+ dropout_rate:
307
+ Dropout rate used throughout the layer
308
+ num_blocks:
309
+ Number of structure module blocks
310
+ num_transition_layers:
311
+ Number of layers in the single representation transition (Alg. 23 lines 8-9)
312
+ num_resnet_blocks:
313
+ Number of blocks in the angle resnet
314
+ num_angles:
315
+ Number of angles to generate in the angle resnet
316
+ trans_scale_factor:
317
+ Scale of single representation transition hidden dimension
318
+ epsilon:
319
+ Small number used in angle resnet normalization
320
+ inf:
321
+ Large number used for attention masking
322
+ """
323
+
324
+ sequence_dim: int = 384
325
+ pairwise_dim: int = 128
326
+ ipa_dim: int = 16
327
+ resnet_dim: int = 128
328
+ num_heads_ipa: int = 12
329
+ num_qk_points: int = 4
330
+ num_v_points: int = 8
331
+ dropout_rate: float = 0.1
332
+ num_blocks: int = 8
333
+ num_transition_layers: int = 1
334
+ num_resnet_blocks: int = 2
335
+ num_angles: int = 7
336
+ trans_scale_factor: int = 10
337
+ epsilon: float = 1e-8
338
+ inf: float = 1e5
339
+
340
+ def to_dict(self):
341
+ return asdict(self)
342
+
343
+
344
+ def get_default_vocab_list():
345
+ return (
346
+ "<cls>",
347
+ "<pad>",
348
+ "<eos>",
349
+ "<unk>",
350
+ "L",
351
+ "A",
352
+ "G",
353
+ "V",
354
+ "S",
355
+ "E",
356
+ "R",
357
+ "T",
358
+ "I",
359
+ "D",
360
+ "P",
361
+ "K",
362
+ "Q",
363
+ "N",
364
+ "F",
365
+ "Y",
366
+ "M",
367
+ "H",
368
+ "W",
369
+ "C",
370
+ "X",
371
+ "B",
372
+ "U",
373
+ "Z",
374
+ "O",
375
+ ".",
376
+ "-",
377
+ "<null_1>",
378
+ "<mask>",
379
+ )
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:67e4303c7a98bbd8b0a4963ec1c398950a981e4ff703f87653f948247ea38198
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+ size 383169868
modeling_esm.py ADDED
@@ -0,0 +1,1446 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ PyTorch ESM model."""
16
+
17
+ import math
18
+ from typing import List, Optional, Tuple, Union
19
+
20
+ import torch
21
+ import torch.utils.checkpoint
22
+ from torch import nn
23
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, SiLU
24
+ from transformers.file_utils import (
25
+ add_code_sample_docstrings,
26
+ add_start_docstrings,
27
+ add_start_docstrings_to_model_forward,
28
+ )
29
+ from transformers.modeling_outputs import (
30
+ BaseModelOutputWithPastAndCrossAttentions,
31
+ BaseModelOutputWithPoolingAndCrossAttentions,
32
+ MaskedLMOutput,
33
+ SequenceClassifierOutput,
34
+ TokenClassifierOutput,
35
+ )
36
+ from transformers.modeling_utils import (
37
+ PreTrainedModel,
38
+ find_pruneable_heads_and_indices,
39
+ prune_linear_layer,
40
+ )
41
+ from transformers.utils import logging
42
+
43
+ from .esm_config import EsmConfig
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
48
+ _CONFIG_FOR_DOC = "EsmConfig"
49
+
50
+ ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
51
+ "facebook/esm2_t6_8M_UR50D",
52
+ "facebook/esm2_t12_35M_UR50D",
53
+ # This is not a complete list of all ESM models!
54
+ # See all ESM models at https://huggingface.co/models?filter=esm
55
+ ]
56
+
57
+
58
+ def rotate_half(x):
59
+ x1, x2 = x.chunk(2, dim=-1)
60
+ return torch.cat((-x2, x1), dim=-1)
61
+
62
+
63
+ def apply_rotary_pos_emb(x, cos, sin):
64
+ cos = cos[:, :, : x.shape[-2], :]
65
+ sin = sin[:, :, : x.shape[-2], :]
66
+
67
+ return (x * cos) + (rotate_half(x) * sin)
68
+
69
+
70
+ def gelu(x):
71
+ """
72
+ This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
73
+ """
74
+ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
75
+
76
+
77
+ def symmetrize(x):
78
+ "Make layer symmetric in final two dimensions, used for contact prediction."
79
+ return x + x.transpose(-1, -2)
80
+
81
+
82
+ def average_product_correct(x):
83
+ "Perform average product correct, used for contact prediction."
84
+ a1 = x.sum(-1, keepdims=True)
85
+ a2 = x.sum(-2, keepdims=True)
86
+ a12 = x.sum((-1, -2), keepdims=True)
87
+
88
+ avg = a1 * a2
89
+ avg.div_(a12) # in-place to reduce memory
90
+ normalized = x - avg
91
+ return normalized
92
+
93
+
94
+ class RotaryEmbedding(torch.nn.Module):
95
+ """
96
+ Rotary position embeddings based on those in
97
+ [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
98
+ matrices which depend on their relative positions.
99
+ """
100
+
101
+ def __init__(self, dim: int):
102
+ super().__init__()
103
+ # Generate and save the inverse frequency buffer (non trainable)
104
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
105
+ inv_freq = inv_freq
106
+ self.register_buffer("inv_freq", inv_freq)
107
+
108
+ self._seq_len_cached = None
109
+ self._cos_cached = None
110
+ self._sin_cached = None
111
+
112
+ def _update_cos_sin_tables(self, x, seq_dimension=2):
113
+ seq_len = x.shape[seq_dimension]
114
+
115
+ # Reset the tables if the sequence length has changed,
116
+ # or if we're on a new device (possibly due to tracing for instance)
117
+ if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
118
+ self._seq_len_cached = seq_len
119
+ t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(
120
+ self.inv_freq
121
+ )
122
+ freqs = torch.outer(t, self.inv_freq)
123
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
124
+
125
+ self._cos_cached = emb.cos()[None, None, :, :]
126
+ self._sin_cached = emb.sin()[None, None, :, :]
127
+
128
+ return self._cos_cached, self._sin_cached
129
+
130
+ def forward(
131
+ self, q: torch.Tensor, k: torch.Tensor
132
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
133
+ self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
134
+ k, seq_dimension=-2
135
+ )
136
+
137
+ return (
138
+ apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
139
+ apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
140
+ )
141
+
142
+
143
+ class EsmContactPredictionHead(nn.Module):
144
+ """Performs symmetrization, apc, and computes a logistic regression on the output features"""
145
+
146
+ def __init__(
147
+ self,
148
+ in_features: int,
149
+ bias=True,
150
+ eos_idx: int = 2,
151
+ ):
152
+ super().__init__()
153
+ self.in_features = in_features
154
+ self.eos_idx = eos_idx
155
+ self.regression = nn.Linear(in_features, 1, bias)
156
+ self.activation = nn.Sigmoid()
157
+
158
+ def forward(self, tokens, attentions):
159
+ # remove eos token attentions
160
+ eos_mask = tokens.ne(self.eos_idx).to(attentions)
161
+ eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
162
+ attentions = attentions * eos_mask[:, None, None, :, :]
163
+ attentions = attentions[..., :-1, :-1]
164
+ # remove cls token attentions
165
+ attentions = attentions[..., 1:, 1:]
166
+ batch_size, layers, heads, seqlen, _ = attentions.size()
167
+ attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
168
+
169
+ # features: batch x channels x tokens x tokens (symmetric)
170
+ attentions = attentions.to(
171
+ self.regression.weight.device
172
+ ) # attentions always float32, may need to convert to float16
173
+ attentions = average_product_correct(symmetrize(attentions))
174
+ attentions = attentions.permute(0, 2, 3, 1)
175
+ return self.activation(self.regression(attentions).squeeze(3))
176
+
177
+
178
+ class EsmEmbeddings(nn.Module):
179
+ """
180
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
181
+ """
182
+
183
+ def __init__(self, config):
184
+ super().__init__()
185
+ self.word_embeddings = nn.Embedding(
186
+ config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
187
+ )
188
+
189
+ if config.emb_layer_norm_before:
190
+ self.layer_norm = nn.LayerNorm(
191
+ config.hidden_size, eps=config.layer_norm_eps
192
+ )
193
+ else:
194
+ self.layer_norm = None
195
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
196
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
197
+ self.position_embedding_type = getattr(
198
+ config, "position_embedding_type", "absolute"
199
+ )
200
+ self.register_buffer(
201
+ "position_ids",
202
+ torch.arange(config.max_position_embeddings).expand((1, -1)),
203
+ persistent=False,
204
+ )
205
+
206
+ self.padding_idx = config.pad_token_id
207
+ self.position_embeddings = nn.Embedding(
208
+ config.max_position_embeddings,
209
+ config.hidden_size,
210
+ padding_idx=self.padding_idx,
211
+ )
212
+ self.token_dropout = config.token_dropout
213
+ self.mask_token_id = config.mask_token_id
214
+
215
+ def forward(
216
+ self,
217
+ input_ids=None,
218
+ attention_mask=None,
219
+ position_ids=None,
220
+ inputs_embeds=None,
221
+ past_key_values_length=0,
222
+ ):
223
+ if position_ids is None:
224
+ if input_ids is not None:
225
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
226
+ position_ids = create_position_ids_from_input_ids(
227
+ input_ids, self.padding_idx, past_key_values_length
228
+ )
229
+ else:
230
+ position_ids = self.create_position_ids_from_inputs_embeds(
231
+ inputs_embeds
232
+ )
233
+
234
+ if inputs_embeds is None:
235
+ inputs_embeds = self.word_embeddings(input_ids)
236
+
237
+ # Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
238
+ # embedding_scale factor here.
239
+ embeddings = inputs_embeds
240
+
241
+ # Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
242
+ # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
243
+ # masked tokens are treated as if they were selected for input dropout and zeroed out.
244
+ # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
245
+ # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
246
+ # This is analogous to the way that dropout layers scale down outputs during evaluation when not
247
+ # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
248
+ if self.token_dropout:
249
+ embeddings.masked_fill_(
250
+ (input_ids == self.mask_token_id).unsqueeze(-1), 0.0
251
+ )
252
+ mask_ratio_train = (
253
+ 0.15 * 0.8
254
+ ) # Hardcoded as the ratio used in all ESM model training runs
255
+ src_lengths = attention_mask.sum(-1)
256
+ mask_ratio_observed = (input_ids == self.mask_token_id).sum(
257
+ -1
258
+ ).float() / src_lengths
259
+ embeddings = (
260
+ embeddings
261
+ * (1 - mask_ratio_train)
262
+ / (1 - mask_ratio_observed)[:, None, None]
263
+ ).to(embeddings.dtype)
264
+
265
+ if self.position_embedding_type == "absolute":
266
+ position_embeddings = self.position_embeddings(position_ids)
267
+ embeddings += position_embeddings
268
+
269
+ if self.layer_norm is not None:
270
+ embeddings = self.layer_norm(embeddings)
271
+ if attention_mask is not None:
272
+ embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(
273
+ embeddings.dtype
274
+ )
275
+ # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
276
+ # embeddings = self.dropout(embeddings)
277
+ return embeddings
278
+
279
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
280
+ """
281
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
282
+
283
+ Args:
284
+ inputs_embeds: torch.Tensor
285
+
286
+ Returns: torch.Tensor
287
+ """
288
+ input_shape = inputs_embeds.size()[:-1]
289
+ sequence_length = input_shape[1]
290
+
291
+ position_ids = torch.arange(
292
+ self.padding_idx + 1,
293
+ sequence_length + self.padding_idx + 1,
294
+ dtype=torch.long,
295
+ device=inputs_embeds.device,
296
+ )
297
+ return position_ids.unsqueeze(0).expand(input_shape)
298
+
299
+
300
+ class EsmSelfAttention(nn.Module):
301
+ def __init__(self, config, position_embedding_type=None):
302
+ super().__init__()
303
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
304
+ config, "embedding_size"
305
+ ):
306
+ raise ValueError(
307
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
308
+ f"heads ({config.num_attention_heads})"
309
+ )
310
+
311
+ self.num_attention_heads = config.num_attention_heads
312
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
313
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
314
+
315
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
316
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
317
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
318
+
319
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
320
+ self.position_embedding_type = position_embedding_type or getattr(
321
+ config, "position_embedding_type", "absolute"
322
+ )
323
+ self.rotary_embeddings = None
324
+ if (
325
+ self.position_embedding_type == "relative_key"
326
+ or self.position_embedding_type == "relative_key_query"
327
+ ):
328
+ self.max_position_embeddings = config.max_position_embeddings
329
+ self.distance_embedding = nn.Embedding(
330
+ 2 * config.max_position_embeddings - 1, self.attention_head_size
331
+ )
332
+ elif self.position_embedding_type == "rotary":
333
+ self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
334
+
335
+ self.is_decoder = config.is_decoder
336
+
337
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
338
+ new_x_shape = x.size()[:-1] + (
339
+ self.num_attention_heads,
340
+ self.attention_head_size,
341
+ )
342
+ x = x.view(new_x_shape)
343
+ return x.permute(0, 2, 1, 3)
344
+
345
+ def forward(
346
+ self,
347
+ hidden_states: torch.Tensor,
348
+ attention_mask: Optional[torch.FloatTensor] = None,
349
+ head_mask: Optional[torch.FloatTensor] = None,
350
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
351
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
352
+ past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
353
+ output_attentions: Optional[bool] = False,
354
+ ) -> Tuple[torch.Tensor]:
355
+ mixed_query_layer = self.query(hidden_states)
356
+
357
+ # If this is instantiated as a cross-attention module, the keys
358
+ # and values come from an encoder; the attention mask needs to be
359
+ # such that the encoder's padding tokens are not attended to.
360
+ is_cross_attention = encoder_hidden_states is not None
361
+
362
+ if is_cross_attention and past_key_value is not None:
363
+ # reuse k,v, cross_attentions
364
+ key_layer = past_key_value[0]
365
+ value_layer = past_key_value[1]
366
+ attention_mask = encoder_attention_mask
367
+ elif is_cross_attention:
368
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
369
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
370
+ attention_mask = encoder_attention_mask
371
+ elif past_key_value is not None:
372
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
373
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
374
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
375
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
376
+ else:
377
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
378
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
379
+
380
+ query_layer = self.transpose_for_scores(mixed_query_layer)
381
+
382
+ # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
383
+ # ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
384
+ # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
385
+ # ESM code and fix rotary embeddings.
386
+ query_layer = query_layer * self.attention_head_size**-0.5
387
+
388
+ if self.is_decoder:
389
+ # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
390
+ # Further calls to cross_attention layer can then reuse all cross-attention
391
+ # key/value_states (first "if" case)
392
+ # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
393
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
394
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
395
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
396
+ past_key_value = (key_layer, value_layer)
397
+
398
+ if self.position_embedding_type == "rotary":
399
+ query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
400
+
401
+ # Take the dot product between "query" and "key" to get the raw attention scores.
402
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
403
+
404
+ if (
405
+ self.position_embedding_type == "relative_key"
406
+ or self.position_embedding_type == "relative_key_query"
407
+ ):
408
+ seq_length = hidden_states.size()[1]
409
+ position_ids_l = torch.arange(
410
+ seq_length, dtype=torch.long, device=hidden_states.device
411
+ ).view(-1, 1)
412
+ position_ids_r = torch.arange(
413
+ seq_length, dtype=torch.long, device=hidden_states.device
414
+ ).view(1, -1)
415
+ distance = position_ids_l - position_ids_r
416
+ positional_embedding = self.distance_embedding(
417
+ distance + self.max_position_embeddings - 1
418
+ )
419
+ positional_embedding = positional_embedding.to(
420
+ dtype=query_layer.dtype
421
+ ) # fp16 compatibility
422
+
423
+ if self.position_embedding_type == "relative_key":
424
+ relative_position_scores = torch.einsum(
425
+ "bhld,lrd->bhlr", query_layer, positional_embedding
426
+ )
427
+ attention_scores = attention_scores + relative_position_scores
428
+ elif self.position_embedding_type == "relative_key_query":
429
+ relative_position_scores_query = torch.einsum(
430
+ "bhld,lrd->bhlr", query_layer, positional_embedding
431
+ )
432
+ relative_position_scores_key = torch.einsum(
433
+ "bhrd,lrd->bhlr", key_layer, positional_embedding
434
+ )
435
+ attention_scores = (
436
+ attention_scores
437
+ + relative_position_scores_query
438
+ + relative_position_scores_key
439
+ )
440
+
441
+ if attention_mask is not None:
442
+ # Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
443
+ attention_scores = attention_scores + attention_mask
444
+
445
+ # Normalize the attention scores to probabilities.
446
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
447
+
448
+ # This is actually dropping out entire tokens to attend to, which might
449
+ # seem a bit unusual, but is taken from the original Transformer paper.
450
+ attention_probs = self.dropout(attention_probs)
451
+
452
+ # Mask heads if we want to
453
+ if head_mask is not None:
454
+ attention_probs = attention_probs * head_mask
455
+
456
+ context_layer = torch.matmul(attention_probs, value_layer)
457
+
458
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
459
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
460
+ context_layer = context_layer.view(new_context_layer_shape)
461
+
462
+ outputs = (
463
+ (context_layer, attention_probs) if output_attentions else (context_layer,)
464
+ )
465
+
466
+ if self.is_decoder:
467
+ outputs = outputs + (past_key_value,)
468
+ return outputs
469
+
470
+
471
+ class EsmSelfOutput(nn.Module):
472
+ def __init__(self, config):
473
+ super().__init__()
474
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
475
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
476
+
477
+ def forward(self, hidden_states, input_tensor):
478
+ hidden_states = self.dense(hidden_states)
479
+ hidden_states = self.dropout(hidden_states)
480
+ hidden_states += input_tensor
481
+ return hidden_states
482
+
483
+
484
+ class EsmAttention(nn.Module):
485
+ def __init__(self, config):
486
+ super().__init__()
487
+ self.self = EsmSelfAttention(config)
488
+ self.output = EsmSelfOutput(config)
489
+ self.pruned_heads = set()
490
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
491
+
492
+ def prune_heads(self, heads):
493
+ if len(heads) == 0:
494
+ return
495
+ heads, index = find_pruneable_heads_and_indices(
496
+ heads,
497
+ self.self.num_attention_heads,
498
+ self.self.attention_head_size,
499
+ self.pruned_heads,
500
+ )
501
+
502
+ # Prune linear layers
503
+ self.self.query = prune_linear_layer(self.self.query, index)
504
+ self.self.key = prune_linear_layer(self.self.key, index)
505
+ self.self.value = prune_linear_layer(self.self.value, index)
506
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
507
+
508
+ # Update hyper params and store pruned heads
509
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
510
+ self.self.all_head_size = (
511
+ self.self.attention_head_size * self.self.num_attention_heads
512
+ )
513
+ self.pruned_heads = self.pruned_heads.union(heads)
514
+
515
+ def forward(
516
+ self,
517
+ hidden_states,
518
+ attention_mask=None,
519
+ head_mask=None,
520
+ encoder_hidden_states=None,
521
+ encoder_attention_mask=None,
522
+ past_key_value=None,
523
+ output_attentions=False,
524
+ ):
525
+ hidden_states_ln = self.LayerNorm(hidden_states)
526
+ self_outputs = self.self(
527
+ hidden_states_ln,
528
+ attention_mask,
529
+ head_mask,
530
+ encoder_hidden_states,
531
+ encoder_attention_mask,
532
+ past_key_value,
533
+ output_attentions,
534
+ )
535
+ attention_output = self.output(self_outputs[0], hidden_states)
536
+ outputs = (attention_output,) + self_outputs[
537
+ 1:
538
+ ] # add attentions if we output them
539
+ return outputs
540
+
541
+
542
+ class EsmIntermediate(nn.Module):
543
+ def __init__(self, config):
544
+ super().__init__()
545
+
546
+ self.dense = nn.Linear(
547
+ config.hidden_size,
548
+ int(config.intermediate_size * 2),
549
+ bias=config.add_bias_fnn,
550
+ )
551
+ self.activation_fn = SiLU()
552
+
553
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
554
+ hidden_states = self.dense(hidden_states)
555
+
556
+ # GLU
557
+ x1, x2 = hidden_states.split(int(hidden_states.size(-1) / 2), -1)
558
+ hidden_states = self.activation_fn(x1) * x2
559
+
560
+ return hidden_states
561
+
562
+
563
+ class EsmOutput(nn.Module):
564
+ def __init__(self, config):
565
+ super().__init__()
566
+ self.dense = nn.Linear(
567
+ config.intermediate_size, config.hidden_size, bias=config.add_bias_fnn
568
+ )
569
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
570
+
571
+ def forward(self, hidden_states, input_tensor):
572
+ hidden_states = self.dense(hidden_states)
573
+ hidden_states = self.dropout(hidden_states)
574
+ hidden_states += input_tensor
575
+ return hidden_states
576
+
577
+
578
+ class EsmLayer(nn.Module):
579
+ def __init__(self, config):
580
+ super().__init__()
581
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
582
+ self.seq_len_dim = 1
583
+ self.attention = EsmAttention(config)
584
+ self.is_decoder = config.is_decoder
585
+ self.add_cross_attention = config.add_cross_attention
586
+ if self.add_cross_attention:
587
+ if not self.is_decoder:
588
+ raise RuntimeError(
589
+ f"{self} should be used as a decoder model if cross attention is added"
590
+ )
591
+ self.crossattention = EsmAttention(config)
592
+ self.intermediate = EsmIntermediate(config)
593
+ self.output = EsmOutput(config)
594
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
595
+
596
+ def forward(
597
+ self,
598
+ hidden_states,
599
+ attention_mask=None,
600
+ head_mask=None,
601
+ encoder_hidden_states=None,
602
+ encoder_attention_mask=None,
603
+ past_key_value=None,
604
+ output_attentions=False,
605
+ ):
606
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
607
+ self_attn_past_key_value = (
608
+ past_key_value[:2] if past_key_value is not None else None
609
+ )
610
+ self_attention_outputs = self.attention(
611
+ hidden_states,
612
+ attention_mask,
613
+ head_mask,
614
+ output_attentions=output_attentions,
615
+ past_key_value=self_attn_past_key_value,
616
+ )
617
+ attention_output = self_attention_outputs[0]
618
+
619
+ # if decoder, the last output is tuple of self-attn cache
620
+ if self.is_decoder:
621
+ outputs = self_attention_outputs[1:-1]
622
+ present_key_value = self_attention_outputs[-1]
623
+ else:
624
+ outputs = self_attention_outputs[
625
+ 1:
626
+ ] # add self attentions if we output attention weights
627
+
628
+ cross_attn_present_key_value = None
629
+ if self.is_decoder and encoder_hidden_states is not None:
630
+ if not hasattr(self, "crossattention"):
631
+ raise AttributeError(
632
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
633
+ " with cross-attention layers by setting `config.add_cross_attention=True`"
634
+ )
635
+
636
+ # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
637
+ cross_attn_past_key_value = (
638
+ past_key_value[-2:] if past_key_value is not None else None
639
+ )
640
+ cross_attention_outputs = self.crossattention(
641
+ attention_output,
642
+ attention_mask,
643
+ head_mask,
644
+ encoder_hidden_states,
645
+ encoder_attention_mask,
646
+ cross_attn_past_key_value,
647
+ output_attentions,
648
+ )
649
+ attention_output = cross_attention_outputs[0]
650
+ outputs = (
651
+ outputs + cross_attention_outputs[1:-1]
652
+ ) # add cross attentions if we output attention weights
653
+
654
+ # add cross-attn cache to positions 3,4 of present_key_value tuple
655
+ cross_attn_present_key_value = cross_attention_outputs[-1]
656
+ present_key_value = present_key_value + cross_attn_present_key_value
657
+
658
+ layer_output = self.feed_forward_chunk(attention_output)
659
+
660
+ outputs = (layer_output,) + outputs
661
+
662
+ # if decoder, return the attn key/values as the last output
663
+ if self.is_decoder:
664
+ outputs = outputs + (present_key_value,)
665
+ return outputs
666
+
667
+ def feed_forward_chunk(self, attention_output):
668
+ attention_output_ln = self.LayerNorm(attention_output)
669
+ intermediate_output = self.intermediate(attention_output_ln)
670
+ layer_output = self.output(intermediate_output, attention_output)
671
+ return layer_output
672
+
673
+
674
+ class EsmEncoder(nn.Module):
675
+ def __init__(self, config):
676
+ super().__init__()
677
+ self.config = config
678
+ self.layer = nn.ModuleList(
679
+ [EsmLayer(config) for _ in range(config.num_hidden_layers)]
680
+ )
681
+ self.emb_layer_norm_after = nn.LayerNorm(
682
+ config.hidden_size, eps=config.layer_norm_eps
683
+ )
684
+ self.gradient_checkpointing = False
685
+
686
+ def forward(
687
+ self,
688
+ hidden_states,
689
+ attention_mask=None,
690
+ head_mask=None,
691
+ encoder_hidden_states=None,
692
+ encoder_attention_mask=None,
693
+ past_key_values=None,
694
+ use_cache=None,
695
+ output_attentions=False,
696
+ output_hidden_states=False,
697
+ return_dict=True,
698
+ ):
699
+ if self.gradient_checkpointing and self.training:
700
+ if use_cache:
701
+ logger.warning_once(
702
+ "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
703
+ "`use_cache=False`..."
704
+ )
705
+ use_cache = False
706
+ all_hidden_states = () if output_hidden_states else None
707
+ all_self_attentions = () if output_attentions else None
708
+ all_cross_attentions = (
709
+ () if output_attentions and self.config.add_cross_attention else None
710
+ )
711
+
712
+ next_decoder_cache = () if use_cache else None
713
+ for i, layer_module in enumerate(self.layer):
714
+ if output_hidden_states:
715
+ all_hidden_states = all_hidden_states + (hidden_states,)
716
+
717
+ layer_head_mask = head_mask[i] if head_mask is not None else None
718
+ past_key_value = past_key_values[i] if past_key_values is not None else None
719
+
720
+ if self.gradient_checkpointing and self.training:
721
+
722
+ def create_custom_forward(module):
723
+ def custom_forward(*inputs):
724
+ return module(*inputs, past_key_value, output_attentions)
725
+
726
+ return custom_forward
727
+
728
+ layer_outputs = torch.utils.checkpoint.checkpoint(
729
+ create_custom_forward(layer_module),
730
+ hidden_states,
731
+ attention_mask,
732
+ layer_head_mask,
733
+ encoder_hidden_states,
734
+ encoder_attention_mask,
735
+ )
736
+ else:
737
+ layer_outputs = layer_module(
738
+ hidden_states,
739
+ attention_mask,
740
+ layer_head_mask,
741
+ encoder_hidden_states,
742
+ encoder_attention_mask,
743
+ past_key_value,
744
+ output_attentions,
745
+ )
746
+
747
+ hidden_states = layer_outputs[0]
748
+ if use_cache:
749
+ next_decoder_cache += (layer_outputs[-1],)
750
+ if output_attentions:
751
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
752
+ if self.config.add_cross_attention:
753
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
754
+
755
+ if self.emb_layer_norm_after:
756
+ hidden_states = self.emb_layer_norm_after(hidden_states)
757
+
758
+ if output_hidden_states:
759
+ all_hidden_states = all_hidden_states + (hidden_states,)
760
+
761
+ if not return_dict:
762
+ return tuple(
763
+ v
764
+ for v in [
765
+ hidden_states,
766
+ next_decoder_cache,
767
+ all_hidden_states,
768
+ all_self_attentions,
769
+ all_cross_attentions,
770
+ ]
771
+ if v is not None
772
+ )
773
+ return BaseModelOutputWithPastAndCrossAttentions(
774
+ last_hidden_state=hidden_states,
775
+ past_key_values=next_decoder_cache,
776
+ hidden_states=all_hidden_states,
777
+ attentions=all_self_attentions,
778
+ cross_attentions=all_cross_attentions,
779
+ )
780
+
781
+
782
+ # Copied from transformers.models.bert.modeling_bert.BertPooler
783
+ class EsmPooler(nn.Module):
784
+ def __init__(self, config):
785
+ super().__init__()
786
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
787
+ self.activation = nn.Tanh()
788
+
789
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
790
+ # We "pool" the model by simply taking the hidden state corresponding
791
+ # to the first token.
792
+ first_token_tensor = hidden_states[:, 0]
793
+ pooled_output = self.dense(first_token_tensor)
794
+ pooled_output = self.activation(pooled_output)
795
+ return pooled_output
796
+
797
+
798
+ class EsmPreTrainedModel(PreTrainedModel):
799
+ """
800
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
801
+ models.
802
+ """
803
+
804
+ config_class = EsmConfig
805
+ base_model_prefix = "esm"
806
+ _no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock"]
807
+
808
+ # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
809
+ def _init_weights(self, module):
810
+ """Initialize the weights"""
811
+ if isinstance(module, nn.Linear):
812
+ # Slightly different from the TF version which uses truncated_normal for initialization
813
+ # cf https://github.com/pytorch/pytorch/pull/5617
814
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
815
+ if module.bias is not None:
816
+ module.bias.data.zero_()
817
+ elif isinstance(module, nn.Embedding):
818
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
819
+ if module.padding_idx is not None:
820
+ module.weight.data[module.padding_idx].zero_()
821
+ elif isinstance(module, nn.LayerNorm):
822
+ module.bias.data.zero_()
823
+ module.weight.data.fill_(1.0)
824
+
825
+
826
+ ESM_START_DOCSTRING = r"""
827
+
828
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
829
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
830
+ etc.)
831
+
832
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
833
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
834
+ and behavior.
835
+
836
+ Parameters:
837
+ config ([`EsmConfig`]): Model configuration class with all the parameters of the
838
+ model. Initializing with a config file does not load the weights associated with the model, only the
839
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
840
+ """
841
+
842
+ ESM_INPUTS_DOCSTRING = r"""
843
+ Args:
844
+ input_ids (`torch.LongTensor` of shape `({0})`):
845
+ Indices of input sequence tokens in the vocabulary.
846
+
847
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
848
+ [`PreTrainedTokenizer.__call__`] for details.
849
+
850
+ [What are input IDs?](../glossary#input-ids)
851
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
852
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
853
+
854
+ - 1 for tokens that are **not masked**,
855
+ - 0 for tokens that are **masked**.
856
+
857
+ [What are attention masks?](../glossary#attention-mask)
858
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
859
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
860
+ config.max_position_embeddings - 1]`.
861
+
862
+ [What are position IDs?](../glossary#position-ids)
863
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
864
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
865
+
866
+ - 1 indicates the head is **not masked**,
867
+ - 0 indicates the head is **masked**.
868
+
869
+ inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
870
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
871
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
872
+ model's internal embedding lookup matrix.
873
+ output_attentions (`bool`, *optional*):
874
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
875
+ tensors for more detail.
876
+ output_hidden_states (`bool`, *optional*):
877
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
878
+ more detail.
879
+ return_dict (`bool`, *optional*):
880
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
881
+ """
882
+
883
+
884
+ @add_start_docstrings(
885
+ "The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
886
+ ESM_START_DOCSTRING,
887
+ )
888
+ class EsmModel(EsmPreTrainedModel):
889
+ """
890
+
891
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
892
+ cross-attention is added between the self-attention layers, following the architecture described in [Attention is
893
+ all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
894
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
895
+
896
+ To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
897
+ to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
898
+ `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
899
+ """
900
+
901
+ supports_gradient_checkpointing = False
902
+
903
+ def __init__(self, config, add_pooling_layer=True):
904
+ super().__init__(config)
905
+ self.config = config
906
+
907
+ self.embeddings = EsmEmbeddings(config)
908
+ self.encoder = EsmEncoder(config)
909
+
910
+ self.pooler = EsmPooler(config) if add_pooling_layer else None
911
+
912
+ self.contact_head = EsmContactPredictionHead(
913
+ in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
914
+ )
915
+
916
+ # Initialize weights and apply final processing
917
+ self.post_init()
918
+
919
+ def _set_gradient_checkpointing(self, module, value=False):
920
+ if isinstance(module, EsmEncoder):
921
+ module.gradient_checkpointing = value
922
+
923
+ def get_input_embeddings(self):
924
+ return self.embeddings.word_embeddings
925
+
926
+ def set_input_embeddings(self, value):
927
+ self.embeddings.word_embeddings = value
928
+
929
+ def _prune_heads(self, heads_to_prune):
930
+ """
931
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
932
+ class PreTrainedModel
933
+ """
934
+ for layer, heads in heads_to_prune.items():
935
+ self.encoder.layer[layer].attention.prune_heads(heads)
936
+
937
+ @add_start_docstrings_to_model_forward(
938
+ ESM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")
939
+ )
940
+ @add_code_sample_docstrings(
941
+ checkpoint=_CHECKPOINT_FOR_DOC,
942
+ output_type=BaseModelOutputWithPoolingAndCrossAttentions,
943
+ config_class=_CONFIG_FOR_DOC,
944
+ )
945
+ def forward(
946
+ self,
947
+ input_ids: Optional[torch.Tensor] = None,
948
+ attention_mask: Optional[torch.Tensor] = None,
949
+ position_ids: Optional[torch.Tensor] = None,
950
+ head_mask: Optional[torch.Tensor] = None,
951
+ inputs_embeds: Optional[torch.Tensor] = None,
952
+ encoder_hidden_states: Optional[torch.Tensor] = None,
953
+ encoder_attention_mask: Optional[torch.Tensor] = None,
954
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
955
+ use_cache: Optional[bool] = None,
956
+ output_attentions: Optional[bool] = None,
957
+ output_hidden_states: Optional[bool] = None,
958
+ return_dict: Optional[bool] = None,
959
+ ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
960
+ r"""
961
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
962
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
963
+ the model is configured as a decoder.
964
+ encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
965
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
966
+ the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
967
+
968
+ - 1 for tokens that are **not masked**,
969
+ - 0 for tokens that are **masked**.
970
+ past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
971
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
972
+
973
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
974
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
975
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
976
+ use_cache (`bool`, *optional*):
977
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
978
+ `past_key_values`).
979
+ """
980
+ output_attentions = (
981
+ output_attentions
982
+ if output_attentions is not None
983
+ else self.config.output_attentions
984
+ )
985
+ output_hidden_states = (
986
+ output_hidden_states
987
+ if output_hidden_states is not None
988
+ else self.config.output_hidden_states
989
+ )
990
+ return_dict = (
991
+ return_dict if return_dict is not None else self.config.use_return_dict
992
+ )
993
+
994
+ if self.config.is_decoder:
995
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
996
+ else:
997
+ use_cache = False
998
+
999
+ if input_ids is not None and inputs_embeds is not None:
1000
+ raise ValueError(
1001
+ "You cannot specify both input_ids and inputs_embeds at the same time"
1002
+ )
1003
+ elif input_ids is not None:
1004
+ input_shape = input_ids.size()
1005
+ elif inputs_embeds is not None:
1006
+ input_shape = inputs_embeds.size()[:-1]
1007
+ else:
1008
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1009
+
1010
+ batch_size, seq_length = input_shape
1011
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1012
+
1013
+ # past_key_values_length
1014
+ past_key_values_length = (
1015
+ past_key_values[0][0].shape[2] if past_key_values is not None else 0
1016
+ )
1017
+
1018
+ if attention_mask is None:
1019
+ attention_mask = torch.ones(
1020
+ ((batch_size, seq_length + past_key_values_length)), device=device
1021
+ )
1022
+
1023
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
1024
+ # ourselves in which case we just need to make it broadcastable to all heads.
1025
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
1026
+ attention_mask, input_shape
1027
+ )
1028
+
1029
+ # If a 2D or 3D attention mask is provided for the cross-attention
1030
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
1031
+ if self.config.is_decoder and encoder_hidden_states is not None:
1032
+ (
1033
+ encoder_batch_size,
1034
+ encoder_sequence_length,
1035
+ _,
1036
+ ) = encoder_hidden_states.size()
1037
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
1038
+ if encoder_attention_mask is None:
1039
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
1040
+ encoder_extended_attention_mask = self.invert_attention_mask(
1041
+ encoder_attention_mask
1042
+ )
1043
+ else:
1044
+ encoder_extended_attention_mask = None
1045
+
1046
+ # Prepare head mask if needed
1047
+ # 1.0 in head_mask indicate we keep the head
1048
+ # attention_probs has shape bsz x n_heads x N x N
1049
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
1050
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
1051
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
1052
+
1053
+ embedding_output = self.embeddings(
1054
+ input_ids=input_ids,
1055
+ position_ids=position_ids,
1056
+ attention_mask=attention_mask,
1057
+ inputs_embeds=inputs_embeds,
1058
+ past_key_values_length=past_key_values_length,
1059
+ )
1060
+ encoder_outputs = self.encoder(
1061
+ embedding_output,
1062
+ attention_mask=extended_attention_mask,
1063
+ head_mask=head_mask,
1064
+ encoder_hidden_states=encoder_hidden_states,
1065
+ encoder_attention_mask=encoder_extended_attention_mask,
1066
+ past_key_values=past_key_values,
1067
+ use_cache=use_cache,
1068
+ output_attentions=output_attentions,
1069
+ output_hidden_states=output_hidden_states,
1070
+ return_dict=return_dict,
1071
+ )
1072
+ sequence_output = encoder_outputs[0]
1073
+ pooled_output = (
1074
+ self.pooler(sequence_output) if self.pooler is not None else None
1075
+ )
1076
+
1077
+ if not return_dict:
1078
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
1079
+
1080
+ return BaseModelOutputWithPoolingAndCrossAttentions(
1081
+ last_hidden_state=sequence_output,
1082
+ pooler_output=pooled_output,
1083
+ past_key_values=encoder_outputs.past_key_values,
1084
+ hidden_states=encoder_outputs.hidden_states,
1085
+ attentions=encoder_outputs.attentions,
1086
+ cross_attentions=encoder_outputs.cross_attentions,
1087
+ )
1088
+
1089
+ def predict_contacts(self, tokens, attention_mask):
1090
+ attns = self(
1091
+ tokens,
1092
+ attention_mask=attention_mask,
1093
+ return_dict=True,
1094
+ output_attentions=True,
1095
+ ).attentions
1096
+ attns = torch.stack(attns, dim=1) # Matches the original model layout
1097
+ # In the original model, attentions for padding tokens are completely zeroed out.
1098
+ # This makes no difference most of the time because the other tokens won't attend to them,
1099
+ # but it does for the contact prediction task, which takes attentions as input,
1100
+ # so we have to mimic that here.
1101
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
1102
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
1103
+ return self.contact_head(tokens, attns)
1104
+
1105
+
1106
+ @add_start_docstrings(
1107
+ """ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING
1108
+ )
1109
+ class EsmForMaskedLM(EsmPreTrainedModel):
1110
+ _tied_weights_keys = ["lm_head.decoder.weight"]
1111
+
1112
+ def __init__(self, config):
1113
+ super().__init__(config)
1114
+
1115
+ if config.is_decoder:
1116
+ logger.warning(
1117
+ "If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
1118
+ "bi-directional self-attention."
1119
+ )
1120
+
1121
+ self.esm = EsmModel(config, add_pooling_layer=False)
1122
+ self.lm_head = EsmLMHead(config)
1123
+
1124
+ self.init_weights()
1125
+
1126
+ def get_output_embeddings(self):
1127
+ return self.lm_head.decoder
1128
+
1129
+ def set_output_embeddings(self, new_embeddings):
1130
+ self.lm_head.decoder = new_embeddings
1131
+
1132
+ @add_start_docstrings_to_model_forward(
1133
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1134
+ )
1135
+ @add_code_sample_docstrings(
1136
+ checkpoint=_CHECKPOINT_FOR_DOC,
1137
+ output_type=MaskedLMOutput,
1138
+ config_class=_CONFIG_FOR_DOC,
1139
+ mask="<mask>",
1140
+ )
1141
+ def forward(
1142
+ self,
1143
+ input_ids: Optional[torch.LongTensor] = None,
1144
+ attention_mask: Optional[torch.Tensor] = None,
1145
+ position_ids: Optional[torch.LongTensor] = None,
1146
+ head_mask: Optional[torch.Tensor] = None,
1147
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1148
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
1149
+ encoder_attention_mask: Optional[torch.Tensor] = None,
1150
+ labels: Optional[torch.LongTensor] = None,
1151
+ output_attentions: Optional[bool] = None,
1152
+ output_hidden_states: Optional[bool] = None,
1153
+ return_dict: Optional[bool] = None,
1154
+ ) -> Union[Tuple, MaskedLMOutput]:
1155
+ r"""
1156
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1157
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
1158
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
1159
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
1160
+ kwargs (`Dict[str, any]`, optional, defaults to *{}*):
1161
+ Used to hide legacy arguments that have been deprecated.
1162
+ """
1163
+ return_dict = (
1164
+ return_dict if return_dict is not None else self.config.use_return_dict
1165
+ )
1166
+
1167
+ outputs = self.esm(
1168
+ input_ids,
1169
+ attention_mask=attention_mask,
1170
+ position_ids=position_ids,
1171
+ head_mask=head_mask,
1172
+ inputs_embeds=inputs_embeds,
1173
+ encoder_hidden_states=encoder_hidden_states,
1174
+ encoder_attention_mask=encoder_attention_mask,
1175
+ output_attentions=output_attentions,
1176
+ output_hidden_states=output_hidden_states,
1177
+ return_dict=return_dict,
1178
+ )
1179
+ sequence_output = outputs[0]
1180
+ prediction_scores = self.lm_head(sequence_output)
1181
+
1182
+ masked_lm_loss = None
1183
+ if labels is not None:
1184
+ loss_fct = CrossEntropyLoss()
1185
+
1186
+ labels = labels.to(prediction_scores.device)
1187
+ masked_lm_loss = loss_fct(
1188
+ prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
1189
+ )
1190
+
1191
+ if not return_dict:
1192
+ output = (prediction_scores,) + outputs[2:]
1193
+ return (
1194
+ ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
1195
+ )
1196
+
1197
+ return MaskedLMOutput(
1198
+ loss=masked_lm_loss,
1199
+ logits=prediction_scores,
1200
+ hidden_states=outputs.hidden_states,
1201
+ attentions=outputs.attentions,
1202
+ )
1203
+
1204
+ def predict_contacts(self, tokens, attention_mask):
1205
+ return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
1206
+
1207
+
1208
+ class EsmLMHead(nn.Module):
1209
+ """ESM Head for masked language modeling."""
1210
+
1211
+ def __init__(self, config):
1212
+ super().__init__()
1213
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
1214
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
1215
+
1216
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1217
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
1218
+
1219
+ def forward(self, features, **kwargs):
1220
+ x = self.dense(features)
1221
+ x = gelu(x)
1222
+ x = self.layer_norm(x)
1223
+
1224
+ # project back to size of vocabulary with bias
1225
+ x = self.decoder(x) + self.bias
1226
+ return x
1227
+
1228
+
1229
+ @add_start_docstrings(
1230
+ """
1231
+ ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
1232
+ output) e.g. for GLUE tasks.
1233
+ """,
1234
+ ESM_START_DOCSTRING,
1235
+ )
1236
+ class EsmForSequenceClassification(EsmPreTrainedModel):
1237
+ def __init__(self, config):
1238
+ super().__init__(config)
1239
+ self.num_labels = config.num_labels
1240
+ self.config = config
1241
+
1242
+ self.esm = EsmModel(config, add_pooling_layer=False)
1243
+ self.classifier = EsmClassificationHead(config)
1244
+
1245
+ self.init_weights()
1246
+
1247
+ @add_start_docstrings_to_model_forward(
1248
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1249
+ )
1250
+ @add_code_sample_docstrings(
1251
+ checkpoint=_CHECKPOINT_FOR_DOC,
1252
+ output_type=SequenceClassifierOutput,
1253
+ config_class=_CONFIG_FOR_DOC,
1254
+ )
1255
+ def forward(
1256
+ self,
1257
+ input_ids: Optional[torch.LongTensor] = None,
1258
+ attention_mask: Optional[torch.Tensor] = None,
1259
+ position_ids: Optional[torch.LongTensor] = None,
1260
+ head_mask: Optional[torch.Tensor] = None,
1261
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1262
+ labels: Optional[torch.LongTensor] = None,
1263
+ output_attentions: Optional[bool] = None,
1264
+ output_hidden_states: Optional[bool] = None,
1265
+ return_dict: Optional[bool] = None,
1266
+ ) -> Union[Tuple, SequenceClassifierOutput]:
1267
+ r"""
1268
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1269
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1270
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1271
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1272
+ """
1273
+ return_dict = (
1274
+ return_dict if return_dict is not None else self.config.use_return_dict
1275
+ )
1276
+
1277
+ outputs = self.esm(
1278
+ input_ids,
1279
+ attention_mask=attention_mask,
1280
+ position_ids=position_ids,
1281
+ head_mask=head_mask,
1282
+ inputs_embeds=inputs_embeds,
1283
+ output_attentions=output_attentions,
1284
+ output_hidden_states=output_hidden_states,
1285
+ return_dict=return_dict,
1286
+ )
1287
+ sequence_output = outputs[0]
1288
+ logits = self.classifier(sequence_output)
1289
+
1290
+ loss = None
1291
+ if labels is not None:
1292
+ labels = labels.to(logits.device)
1293
+
1294
+ if self.config.problem_type is None:
1295
+ if self.num_labels == 1:
1296
+ self.config.problem_type = "regression"
1297
+ elif self.num_labels > 1 and (
1298
+ labels.dtype == torch.long or labels.dtype == torch.int
1299
+ ):
1300
+ self.config.problem_type = "single_label_classification"
1301
+ else:
1302
+ self.config.problem_type = "multi_label_classification"
1303
+
1304
+ if self.config.problem_type == "regression":
1305
+ loss_fct = MSELoss()
1306
+ if self.num_labels == 1:
1307
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
1308
+ else:
1309
+ loss = loss_fct(logits, labels)
1310
+ elif self.config.problem_type == "single_label_classification":
1311
+ loss_fct = CrossEntropyLoss()
1312
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1313
+ elif self.config.problem_type == "multi_label_classification":
1314
+ loss_fct = BCEWithLogitsLoss()
1315
+ loss = loss_fct(logits, labels)
1316
+
1317
+ if not return_dict:
1318
+ output = (logits,) + outputs[2:]
1319
+ return ((loss,) + output) if loss is not None else output
1320
+
1321
+ return SequenceClassifierOutput(
1322
+ loss=loss,
1323
+ logits=logits,
1324
+ hidden_states=outputs.hidden_states,
1325
+ attentions=outputs.attentions,
1326
+ )
1327
+
1328
+
1329
+ @add_start_docstrings(
1330
+ """
1331
+ ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
1332
+ Named-Entity-Recognition (NER) tasks.
1333
+ """,
1334
+ ESM_START_DOCSTRING,
1335
+ )
1336
+ class EsmForTokenClassification(EsmPreTrainedModel):
1337
+ def __init__(self, config):
1338
+ super().__init__(config)
1339
+ self.num_labels = config.num_labels
1340
+
1341
+ self.esm = EsmModel(config, add_pooling_layer=False)
1342
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
1343
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
1344
+
1345
+ self.init_weights()
1346
+
1347
+ @add_start_docstrings_to_model_forward(
1348
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1349
+ )
1350
+ @add_code_sample_docstrings(
1351
+ checkpoint=_CHECKPOINT_FOR_DOC,
1352
+ output_type=TokenClassifierOutput,
1353
+ config_class=_CONFIG_FOR_DOC,
1354
+ )
1355
+ def forward(
1356
+ self,
1357
+ input_ids: Optional[torch.LongTensor] = None,
1358
+ attention_mask: Optional[torch.Tensor] = None,
1359
+ position_ids: Optional[torch.LongTensor] = None,
1360
+ head_mask: Optional[torch.Tensor] = None,
1361
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1362
+ labels: Optional[torch.LongTensor] = None,
1363
+ output_attentions: Optional[bool] = None,
1364
+ output_hidden_states: Optional[bool] = None,
1365
+ return_dict: Optional[bool] = None,
1366
+ ) -> Union[Tuple, TokenClassifierOutput]:
1367
+ r"""
1368
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1369
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
1370
+ """
1371
+ return_dict = (
1372
+ return_dict if return_dict is not None else self.config.use_return_dict
1373
+ )
1374
+
1375
+ outputs = self.esm(
1376
+ input_ids,
1377
+ attention_mask=attention_mask,
1378
+ position_ids=position_ids,
1379
+ head_mask=head_mask,
1380
+ inputs_embeds=inputs_embeds,
1381
+ output_attentions=output_attentions,
1382
+ output_hidden_states=output_hidden_states,
1383
+ return_dict=return_dict,
1384
+ )
1385
+
1386
+ sequence_output = outputs[0]
1387
+
1388
+ sequence_output = self.dropout(sequence_output)
1389
+ logits = self.classifier(sequence_output)
1390
+
1391
+ loss = None
1392
+ if labels is not None:
1393
+ loss_fct = CrossEntropyLoss()
1394
+
1395
+ labels = labels.to(logits.device)
1396
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1397
+
1398
+ if not return_dict:
1399
+ output = (logits,) + outputs[2:]
1400
+ return ((loss,) + output) if loss is not None else output
1401
+
1402
+ return TokenClassifierOutput(
1403
+ loss=loss,
1404
+ logits=logits,
1405
+ hidden_states=outputs.hidden_states,
1406
+ attentions=outputs.attentions,
1407
+ )
1408
+
1409
+
1410
+ class EsmClassificationHead(nn.Module):
1411
+ """Head for sentence-level classification tasks."""
1412
+
1413
+ def __init__(self, config):
1414
+ super().__init__()
1415
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
1416
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
1417
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
1418
+
1419
+ def forward(self, features, **kwargs):
1420
+ x = features[:, 0, :] # take <s> token (equiv. to [CLS])
1421
+ x = self.dropout(x)
1422
+ x = self.dense(x)
1423
+ x = torch.tanh(x)
1424
+ x = self.dropout(x)
1425
+ x = self.out_proj(x)
1426
+ return x
1427
+
1428
+
1429
+ def create_position_ids_from_input_ids(
1430
+ input_ids, padding_idx, past_key_values_length=0
1431
+ ):
1432
+ """
1433
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
1434
+ are ignored. This is modified from fairseq's `utils.make_positions`.
1435
+
1436
+ Args:
1437
+ x: torch.Tensor x:
1438
+
1439
+ Returns: torch.Tensor
1440
+ """
1441
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
1442
+ mask = input_ids.ne(padding_idx).int()
1443
+ incremental_indices = (
1444
+ torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
1445
+ ) * mask
1446
+ return incremental_indices.long() + padding_idx
special_tokens_map.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "<cls>",
3
+ "mask_token": "<mask>",
4
+ "pad_token": "<pad>",
5
+ "unk_token": "<unk>"
6
+ }
tokenizer_config.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<unk>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "<mask>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<cls>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ }
35
+ },
36
+ "clean_up_tokenization_spaces": true,
37
+ "cls_token": "<cls>",
38
+ "eos_token": null,
39
+ "mask_token": "<mask>",
40
+ "model_max_length": 512,
41
+ "pad_token": "<pad>",
42
+ "tokenizer_class": "EsmTokenizer",
43
+ "unk_token": "<unk>"
44
+ }
vocab.txt ADDED
@@ -0,0 +1,4107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <unk>
2
+ <pad>
3
+ <mask>
4
+ <cls>
5
+ <eos>
6
+ <bos>
7
+ AAAAAA
8
+ AAAAAT
9
+ AAAAAC
10
+ AAAAAG
11
+ AAAATA
12
+ AAAATT
13
+ AAAATC
14
+ AAAATG
15
+ AAAACA
16
+ AAAACT
17
+ AAAACC
18
+ AAAACG
19
+ AAAAGA
20
+ AAAAGT
21
+ AAAAGC
22
+ AAAAGG
23
+ AAATAA
24
+ AAATAT
25
+ AAATAC
26
+ AAATAG
27
+ AAATTA
28
+ AAATTT
29
+ AAATTC
30
+ AAATTG
31
+ AAATCA
32
+ AAATCT
33
+ AAATCC
34
+ AAATCG
35
+ AAATGA
36
+ AAATGT
37
+ AAATGC
38
+ AAATGG
39
+ AAACAA
40
+ AAACAT
41
+ AAACAC
42
+ AAACAG
43
+ AAACTA
44
+ AAACTT
45
+ AAACTC
46
+ AAACTG
47
+ AAACCA
48
+ AAACCT
49
+ AAACCC
50
+ AAACCG
51
+ AAACGA
52
+ AAACGT
53
+ AAACGC
54
+ AAACGG
55
+ AAAGAA
56
+ AAAGAT
57
+ AAAGAC
58
+ AAAGAG
59
+ AAAGTA
60
+ AAAGTT
61
+ AAAGTC
62
+ AAAGTG
63
+ AAAGCA
64
+ AAAGCT
65
+ AAAGCC
66
+ AAAGCG
67
+ AAAGGA
68
+ AAAGGT
69
+ AAAGGC
70
+ AAAGGG
71
+ AATAAA
72
+ AATAAT
73
+ AATAAC
74
+ AATAAG
75
+ AATATA
76
+ AATATT
77
+ AATATC
78
+ AATATG
79
+ AATACA
80
+ AATACT
81
+ AATACC
82
+ AATACG
83
+ AATAGA
84
+ AATAGT
85
+ AATAGC
86
+ AATAGG
87
+ AATTAA
88
+ AATTAT
89
+ AATTAC
90
+ AATTAG
91
+ AATTTA
92
+ AATTTT
93
+ AATTTC
94
+ AATTTG
95
+ AATTCA
96
+ AATTCT
97
+ AATTCC
98
+ AATTCG
99
+ AATTGA
100
+ AATTGT
101
+ AATTGC
102
+ AATTGG
103
+ AATCAA
104
+ AATCAT
105
+ AATCAC
106
+ AATCAG
107
+ AATCTA
108
+ AATCTT
109
+ AATCTC
110
+ AATCTG
111
+ AATCCA
112
+ AATCCT
113
+ AATCCC
114
+ AATCCG
115
+ AATCGA
116
+ AATCGT
117
+ AATCGC
118
+ AATCGG
119
+ AATGAA
120
+ AATGAT
121
+ AATGAC
122
+ AATGAG
123
+ AATGTA
124
+ AATGTT
125
+ AATGTC
126
+ AATGTG
127
+ AATGCA
128
+ AATGCT
129
+ AATGCC
130
+ AATGCG
131
+ AATGGA
132
+ AATGGT
133
+ AATGGC
134
+ AATGGG
135
+ AACAAA
136
+ AACAAT
137
+ AACAAC
138
+ AACAAG
139
+ AACATA
140
+ AACATT
141
+ AACATC
142
+ AACATG
143
+ AACACA
144
+ AACACT
145
+ AACACC
146
+ AACACG
147
+ AACAGA
148
+ AACAGT
149
+ AACAGC
150
+ AACAGG
151
+ AACTAA
152
+ AACTAT
153
+ AACTAC
154
+ AACTAG
155
+ AACTTA
156
+ AACTTT
157
+ AACTTC
158
+ AACTTG
159
+ AACTCA
160
+ AACTCT
161
+ AACTCC
162
+ AACTCG
163
+ AACTGA
164
+ AACTGT
165
+ AACTGC
166
+ AACTGG
167
+ AACCAA
168
+ AACCAT
169
+ AACCAC
170
+ AACCAG
171
+ AACCTA
172
+ AACCTT
173
+ AACCTC
174
+ AACCTG
175
+ AACCCA
176
+ AACCCT
177
+ AACCCC
178
+ AACCCG
179
+ AACCGA
180
+ AACCGT
181
+ AACCGC
182
+ AACCGG
183
+ AACGAA
184
+ AACGAT
185
+ AACGAC
186
+ AACGAG
187
+ AACGTA
188
+ AACGTT
189
+ AACGTC
190
+ AACGTG
191
+ AACGCA
192
+ AACGCT
193
+ AACGCC
194
+ AACGCG
195
+ AACGGA
196
+ AACGGT
197
+ AACGGC
198
+ AACGGG
199
+ AAGAAA
200
+ AAGAAT
201
+ AAGAAC
202
+ AAGAAG
203
+ AAGATA
204
+ AAGATT
205
+ AAGATC
206
+ AAGATG
207
+ AAGACA
208
+ AAGACT
209
+ AAGACC
210
+ AAGACG
211
+ AAGAGA
212
+ AAGAGT
213
+ AAGAGC
214
+ AAGAGG
215
+ AAGTAA
216
+ AAGTAT
217
+ AAGTAC
218
+ AAGTAG
219
+ AAGTTA
220
+ AAGTTT
221
+ AAGTTC
222
+ AAGTTG
223
+ AAGTCA
224
+ AAGTCT
225
+ AAGTCC
226
+ AAGTCG
227
+ AAGTGA
228
+ AAGTGT
229
+ AAGTGC
230
+ AAGTGG
231
+ AAGCAA
232
+ AAGCAT
233
+ AAGCAC
234
+ AAGCAG
235
+ AAGCTA
236
+ AAGCTT
237
+ AAGCTC
238
+ AAGCTG
239
+ AAGCCA
240
+ AAGCCT
241
+ AAGCCC
242
+ AAGCCG
243
+ AAGCGA
244
+ AAGCGT
245
+ AAGCGC
246
+ AAGCGG
247
+ AAGGAA
248
+ AAGGAT
249
+ AAGGAC
250
+ AAGGAG
251
+ AAGGTA
252
+ AAGGTT
253
+ AAGGTC
254
+ AAGGTG
255
+ AAGGCA
256
+ AAGGCT
257
+ AAGGCC
258
+ AAGGCG
259
+ AAGGGA
260
+ AAGGGT
261
+ AAGGGC
262
+ AAGGGG
263
+ ATAAAA
264
+ ATAAAT
265
+ ATAAAC
266
+ ATAAAG
267
+ ATAATA
268
+ ATAATT
269
+ ATAATC
270
+ ATAATG
271
+ ATAACA
272
+ ATAACT
273
+ ATAACC
274
+ ATAACG
275
+ ATAAGA
276
+ ATAAGT
277
+ ATAAGC
278
+ ATAAGG
279
+ ATATAA
280
+ ATATAT
281
+ ATATAC
282
+ ATATAG
283
+ ATATTA
284
+ ATATTT
285
+ ATATTC
286
+ ATATTG
287
+ ATATCA
288
+ ATATCT
289
+ ATATCC
290
+ ATATCG
291
+ ATATGA
292
+ ATATGT
293
+ ATATGC
294
+ ATATGG
295
+ ATACAA
296
+ ATACAT
297
+ ATACAC
298
+ ATACAG
299
+ ATACTA
300
+ ATACTT
301
+ ATACTC
302
+ ATACTG
303
+ ATACCA
304
+ ATACCT
305
+ ATACCC
306
+ ATACCG
307
+ ATACGA
308
+ ATACGT
309
+ ATACGC
310
+ ATACGG
311
+ ATAGAA
312
+ ATAGAT
313
+ ATAGAC
314
+ ATAGAG
315
+ ATAGTA
316
+ ATAGTT
317
+ ATAGTC
318
+ ATAGTG
319
+ ATAGCA
320
+ ATAGCT
321
+ ATAGCC
322
+ ATAGCG
323
+ ATAGGA
324
+ ATAGGT
325
+ ATAGGC
326
+ ATAGGG
327
+ ATTAAA
328
+ ATTAAT
329
+ ATTAAC
330
+ ATTAAG
331
+ ATTATA
332
+ ATTATT
333
+ ATTATC
334
+ ATTATG
335
+ ATTACA
336
+ ATTACT
337
+ ATTACC
338
+ ATTACG
339
+ ATTAGA
340
+ ATTAGT
341
+ ATTAGC
342
+ ATTAGG
343
+ ATTTAA
344
+ ATTTAT
345
+ ATTTAC
346
+ ATTTAG
347
+ ATTTTA
348
+ ATTTTT
349
+ ATTTTC
350
+ ATTTTG
351
+ ATTTCA
352
+ ATTTCT
353
+ ATTTCC
354
+ ATTTCG
355
+ ATTTGA
356
+ ATTTGT
357
+ ATTTGC
358
+ ATTTGG
359
+ ATTCAA
360
+ ATTCAT
361
+ ATTCAC
362
+ ATTCAG
363
+ ATTCTA
364
+ ATTCTT
365
+ ATTCTC
366
+ ATTCTG
367
+ ATTCCA
368
+ ATTCCT
369
+ ATTCCC
370
+ ATTCCG
371
+ ATTCGA
372
+ ATTCGT
373
+ ATTCGC
374
+ ATTCGG
375
+ ATTGAA
376
+ ATTGAT
377
+ ATTGAC
378
+ ATTGAG
379
+ ATTGTA
380
+ ATTGTT
381
+ ATTGTC
382
+ ATTGTG
383
+ ATTGCA
384
+ ATTGCT
385
+ ATTGCC
386
+ ATTGCG
387
+ ATTGGA
388
+ ATTGGT
389
+ ATTGGC
390
+ ATTGGG
391
+ ATCAAA
392
+ ATCAAT
393
+ ATCAAC
394
+ ATCAAG
395
+ ATCATA
396
+ ATCATT
397
+ ATCATC
398
+ ATCATG
399
+ ATCACA
400
+ ATCACT
401
+ ATCACC
402
+ ATCACG
403
+ ATCAGA
404
+ ATCAGT
405
+ ATCAGC
406
+ ATCAGG
407
+ ATCTAA
408
+ ATCTAT
409
+ ATCTAC
410
+ ATCTAG
411
+ ATCTTA
412
+ ATCTTT
413
+ ATCTTC
414
+ ATCTTG
415
+ ATCTCA
416
+ ATCTCT
417
+ ATCTCC
418
+ ATCTCG
419
+ ATCTGA
420
+ ATCTGT
421
+ ATCTGC
422
+ ATCTGG
423
+ ATCCAA
424
+ ATCCAT
425
+ ATCCAC
426
+ ATCCAG
427
+ ATCCTA
428
+ ATCCTT
429
+ ATCCTC
430
+ ATCCTG
431
+ ATCCCA
432
+ ATCCCT
433
+ ATCCCC
434
+ ATCCCG
435
+ ATCCGA
436
+ ATCCGT
437
+ ATCCGC
438
+ ATCCGG
439
+ ATCGAA
440
+ ATCGAT
441
+ ATCGAC
442
+ ATCGAG
443
+ ATCGTA
444
+ ATCGTT
445
+ ATCGTC
446
+ ATCGTG
447
+ ATCGCA
448
+ ATCGCT
449
+ ATCGCC
450
+ ATCGCG
451
+ ATCGGA
452
+ ATCGGT
453
+ ATCGGC
454
+ ATCGGG
455
+ ATGAAA
456
+ ATGAAT
457
+ ATGAAC
458
+ ATGAAG
459
+ ATGATA
460
+ ATGATT
461
+ ATGATC
462
+ ATGATG
463
+ ATGACA
464
+ ATGACT
465
+ ATGACC
466
+ ATGACG
467
+ ATGAGA
468
+ ATGAGT
469
+ ATGAGC
470
+ ATGAGG
471
+ ATGTAA
472
+ ATGTAT
473
+ ATGTAC
474
+ ATGTAG
475
+ ATGTTA
476
+ ATGTTT
477
+ ATGTTC
478
+ ATGTTG
479
+ ATGTCA
480
+ ATGTCT
481
+ ATGTCC
482
+ ATGTCG
483
+ ATGTGA
484
+ ATGTGT
485
+ ATGTGC
486
+ ATGTGG
487
+ ATGCAA
488
+ ATGCAT
489
+ ATGCAC
490
+ ATGCAG
491
+ ATGCTA
492
+ ATGCTT
493
+ ATGCTC
494
+ ATGCTG
495
+ ATGCCA
496
+ ATGCCT
497
+ ATGCCC
498
+ ATGCCG
499
+ ATGCGA
500
+ ATGCGT
501
+ ATGCGC
502
+ ATGCGG
503
+ ATGGAA
504
+ ATGGAT
505
+ ATGGAC
506
+ ATGGAG
507
+ ATGGTA
508
+ ATGGTT
509
+ ATGGTC
510
+ ATGGTG
511
+ ATGGCA
512
+ ATGGCT
513
+ ATGGCC
514
+ ATGGCG
515
+ ATGGGA
516
+ ATGGGT
517
+ ATGGGC
518
+ ATGGGG
519
+ ACAAAA
520
+ ACAAAT
521
+ ACAAAC
522
+ ACAAAG
523
+ ACAATA
524
+ ACAATT
525
+ ACAATC
526
+ ACAATG
527
+ ACAACA
528
+ ACAACT
529
+ ACAACC
530
+ ACAACG
531
+ ACAAGA
532
+ ACAAGT
533
+ ACAAGC
534
+ ACAAGG
535
+ ACATAA
536
+ ACATAT
537
+ ACATAC
538
+ ACATAG
539
+ ACATTA
540
+ ACATTT
541
+ ACATTC
542
+ ACATTG
543
+ ACATCA
544
+ ACATCT
545
+ ACATCC
546
+ ACATCG
547
+ ACATGA
548
+ ACATGT
549
+ ACATGC
550
+ ACATGG
551
+ ACACAA
552
+ ACACAT
553
+ ACACAC
554
+ ACACAG
555
+ ACACTA
556
+ ACACTT
557
+ ACACTC
558
+ ACACTG
559
+ ACACCA
560
+ ACACCT
561
+ ACACCC
562
+ ACACCG
563
+ ACACGA
564
+ ACACGT
565
+ ACACGC
566
+ ACACGG
567
+ ACAGAA
568
+ ACAGAT
569
+ ACAGAC
570
+ ACAGAG
571
+ ACAGTA
572
+ ACAGTT
573
+ ACAGTC
574
+ ACAGTG
575
+ ACAGCA
576
+ ACAGCT
577
+ ACAGCC
578
+ ACAGCG
579
+ ACAGGA
580
+ ACAGGT
581
+ ACAGGC
582
+ ACAGGG
583
+ ACTAAA
584
+ ACTAAT
585
+ ACTAAC
586
+ ACTAAG
587
+ ACTATA
588
+ ACTATT
589
+ ACTATC
590
+ ACTATG
591
+ ACTACA
592
+ ACTACT
593
+ ACTACC
594
+ ACTACG
595
+ ACTAGA
596
+ ACTAGT
597
+ ACTAGC
598
+ ACTAGG
599
+ ACTTAA
600
+ ACTTAT
601
+ ACTTAC
602
+ ACTTAG
603
+ ACTTTA
604
+ ACTTTT
605
+ ACTTTC
606
+ ACTTTG
607
+ ACTTCA
608
+ ACTTCT
609
+ ACTTCC
610
+ ACTTCG
611
+ ACTTGA
612
+ ACTTGT
613
+ ACTTGC
614
+ ACTTGG
615
+ ACTCAA
616
+ ACTCAT
617
+ ACTCAC
618
+ ACTCAG
619
+ ACTCTA
620
+ ACTCTT
621
+ ACTCTC
622
+ ACTCTG
623
+ ACTCCA
624
+ ACTCCT
625
+ ACTCCC
626
+ ACTCCG
627
+ ACTCGA
628
+ ACTCGT
629
+ ACTCGC
630
+ ACTCGG
631
+ ACTGAA
632
+ ACTGAT
633
+ ACTGAC
634
+ ACTGAG
635
+ ACTGTA
636
+ ACTGTT
637
+ ACTGTC
638
+ ACTGTG
639
+ ACTGCA
640
+ ACTGCT
641
+ ACTGCC
642
+ ACTGCG
643
+ ACTGGA
644
+ ACTGGT
645
+ ACTGGC
646
+ ACTGGG
647
+ ACCAAA
648
+ ACCAAT
649
+ ACCAAC
650
+ ACCAAG
651
+ ACCATA
652
+ ACCATT
653
+ ACCATC
654
+ ACCATG
655
+ ACCACA
656
+ ACCACT
657
+ ACCACC
658
+ ACCACG
659
+ ACCAGA
660
+ ACCAGT
661
+ ACCAGC
662
+ ACCAGG
663
+ ACCTAA
664
+ ACCTAT
665
+ ACCTAC
666
+ ACCTAG
667
+ ACCTTA
668
+ ACCTTT
669
+ ACCTTC
670
+ ACCTTG
671
+ ACCTCA
672
+ ACCTCT
673
+ ACCTCC
674
+ ACCTCG
675
+ ACCTGA
676
+ ACCTGT
677
+ ACCTGC
678
+ ACCTGG
679
+ ACCCAA
680
+ ACCCAT
681
+ ACCCAC
682
+ ACCCAG
683
+ ACCCTA
684
+ ACCCTT
685
+ ACCCTC
686
+ ACCCTG
687
+ ACCCCA
688
+ ACCCCT
689
+ ACCCCC
690
+ ACCCCG
691
+ ACCCGA
692
+ ACCCGT
693
+ ACCCGC
694
+ ACCCGG
695
+ ACCGAA
696
+ ACCGAT
697
+ ACCGAC
698
+ ACCGAG
699
+ ACCGTA
700
+ ACCGTT
701
+ ACCGTC
702
+ ACCGTG
703
+ ACCGCA
704
+ ACCGCT
705
+ ACCGCC
706
+ ACCGCG
707
+ ACCGGA
708
+ ACCGGT
709
+ ACCGGC
710
+ ACCGGG
711
+ ACGAAA
712
+ ACGAAT
713
+ ACGAAC
714
+ ACGAAG
715
+ ACGATA
716
+ ACGATT
717
+ ACGATC
718
+ ACGATG
719
+ ACGACA
720
+ ACGACT
721
+ ACGACC
722
+ ACGACG
723
+ ACGAGA
724
+ ACGAGT
725
+ ACGAGC
726
+ ACGAGG
727
+ ACGTAA
728
+ ACGTAT
729
+ ACGTAC
730
+ ACGTAG
731
+ ACGTTA
732
+ ACGTTT
733
+ ACGTTC
734
+ ACGTTG
735
+ ACGTCA
736
+ ACGTCT
737
+ ACGTCC
738
+ ACGTCG
739
+ ACGTGA
740
+ ACGTGT
741
+ ACGTGC
742
+ ACGTGG
743
+ ACGCAA
744
+ ACGCAT
745
+ ACGCAC
746
+ ACGCAG
747
+ ACGCTA
748
+ ACGCTT
749
+ ACGCTC
750
+ ACGCTG
751
+ ACGCCA
752
+ ACGCCT
753
+ ACGCCC
754
+ ACGCCG
755
+ ACGCGA
756
+ ACGCGT
757
+ ACGCGC
758
+ ACGCGG
759
+ ACGGAA
760
+ ACGGAT
761
+ ACGGAC
762
+ ACGGAG
763
+ ACGGTA
764
+ ACGGTT
765
+ ACGGTC
766
+ ACGGTG
767
+ ACGGCA
768
+ ACGGCT
769
+ ACGGCC
770
+ ACGGCG
771
+ ACGGGA
772
+ ACGGGT
773
+ ACGGGC
774
+ ACGGGG
775
+ AGAAAA
776
+ AGAAAT
777
+ AGAAAC
778
+ AGAAAG
779
+ AGAATA
780
+ AGAATT
781
+ AGAATC
782
+ AGAATG
783
+ AGAACA
784
+ AGAACT
785
+ AGAACC
786
+ AGAACG
787
+ AGAAGA
788
+ AGAAGT
789
+ AGAAGC
790
+ AGAAGG
791
+ AGATAA
792
+ AGATAT
793
+ AGATAC
794
+ AGATAG
795
+ AGATTA
796
+ AGATTT
797
+ AGATTC
798
+ AGATTG
799
+ AGATCA
800
+ AGATCT
801
+ AGATCC
802
+ AGATCG
803
+ AGATGA
804
+ AGATGT
805
+ AGATGC
806
+ AGATGG
807
+ AGACAA
808
+ AGACAT
809
+ AGACAC
810
+ AGACAG
811
+ AGACTA
812
+ AGACTT
813
+ AGACTC
814
+ AGACTG
815
+ AGACCA
816
+ AGACCT
817
+ AGACCC
818
+ AGACCG
819
+ AGACGA
820
+ AGACGT
821
+ AGACGC
822
+ AGACGG
823
+ AGAGAA
824
+ AGAGAT
825
+ AGAGAC
826
+ AGAGAG
827
+ AGAGTA
828
+ AGAGTT
829
+ AGAGTC
830
+ AGAGTG
831
+ AGAGCA
832
+ AGAGCT
833
+ AGAGCC
834
+ AGAGCG
835
+ AGAGGA
836
+ AGAGGT
837
+ AGAGGC
838
+ AGAGGG
839
+ AGTAAA
840
+ AGTAAT
841
+ AGTAAC
842
+ AGTAAG
843
+ AGTATA
844
+ AGTATT
845
+ AGTATC
846
+ AGTATG
847
+ AGTACA
848
+ AGTACT
849
+ AGTACC
850
+ AGTACG
851
+ AGTAGA
852
+ AGTAGT
853
+ AGTAGC
854
+ AGTAGG
855
+ AGTTAA
856
+ AGTTAT
857
+ AGTTAC
858
+ AGTTAG
859
+ AGTTTA
860
+ AGTTTT
861
+ AGTTTC
862
+ AGTTTG
863
+ AGTTCA
864
+ AGTTCT
865
+ AGTTCC
866
+ AGTTCG
867
+ AGTTGA
868
+ AGTTGT
869
+ AGTTGC
870
+ AGTTGG
871
+ AGTCAA
872
+ AGTCAT
873
+ AGTCAC
874
+ AGTCAG
875
+ AGTCTA
876
+ AGTCTT
877
+ AGTCTC
878
+ AGTCTG
879
+ AGTCCA
880
+ AGTCCT
881
+ AGTCCC
882
+ AGTCCG
883
+ AGTCGA
884
+ AGTCGT
885
+ AGTCGC
886
+ AGTCGG
887
+ AGTGAA
888
+ AGTGAT
889
+ AGTGAC
890
+ AGTGAG
891
+ AGTGTA
892
+ AGTGTT
893
+ AGTGTC
894
+ AGTGTG
895
+ AGTGCA
896
+ AGTGCT
897
+ AGTGCC
898
+ AGTGCG
899
+ AGTGGA
900
+ AGTGGT
901
+ AGTGGC
902
+ AGTGGG
903
+ AGCAAA
904
+ AGCAAT
905
+ AGCAAC
906
+ AGCAAG
907
+ AGCATA
908
+ AGCATT
909
+ AGCATC
910
+ AGCATG
911
+ AGCACA
912
+ AGCACT
913
+ AGCACC
914
+ AGCACG
915
+ AGCAGA
916
+ AGCAGT
917
+ AGCAGC
918
+ AGCAGG
919
+ AGCTAA
920
+ AGCTAT
921
+ AGCTAC
922
+ AGCTAG
923
+ AGCTTA
924
+ AGCTTT
925
+ AGCTTC
926
+ AGCTTG
927
+ AGCTCA
928
+ AGCTCT
929
+ AGCTCC
930
+ AGCTCG
931
+ AGCTGA
932
+ AGCTGT
933
+ AGCTGC
934
+ AGCTGG
935
+ AGCCAA
936
+ AGCCAT
937
+ AGCCAC
938
+ AGCCAG
939
+ AGCCTA
940
+ AGCCTT
941
+ AGCCTC
942
+ AGCCTG
943
+ AGCCCA
944
+ AGCCCT
945
+ AGCCCC
946
+ AGCCCG
947
+ AGCCGA
948
+ AGCCGT
949
+ AGCCGC
950
+ AGCCGG
951
+ AGCGAA
952
+ AGCGAT
953
+ AGCGAC
954
+ AGCGAG
955
+ AGCGTA
956
+ AGCGTT
957
+ AGCGTC
958
+ AGCGTG
959
+ AGCGCA
960
+ AGCGCT
961
+ AGCGCC
962
+ AGCGCG
963
+ AGCGGA
964
+ AGCGGT
965
+ AGCGGC
966
+ AGCGGG
967
+ AGGAAA
968
+ AGGAAT
969
+ AGGAAC
970
+ AGGAAG
971
+ AGGATA
972
+ AGGATT
973
+ AGGATC
974
+ AGGATG
975
+ AGGACA
976
+ AGGACT
977
+ AGGACC
978
+ AGGACG
979
+ AGGAGA
980
+ AGGAGT
981
+ AGGAGC
982
+ AGGAGG
983
+ AGGTAA
984
+ AGGTAT
985
+ AGGTAC
986
+ AGGTAG
987
+ AGGTTA
988
+ AGGTTT
989
+ AGGTTC
990
+ AGGTTG
991
+ AGGTCA
992
+ AGGTCT
993
+ AGGTCC
994
+ AGGTCG
995
+ AGGTGA
996
+ AGGTGT
997
+ AGGTGC
998
+ AGGTGG
999
+ AGGCAA
1000
+ AGGCAT
1001
+ AGGCAC
1002
+ AGGCAG
1003
+ AGGCTA
1004
+ AGGCTT
1005
+ AGGCTC
1006
+ AGGCTG
1007
+ AGGCCA
1008
+ AGGCCT
1009
+ AGGCCC
1010
+ AGGCCG
1011
+ AGGCGA
1012
+ AGGCGT
1013
+ AGGCGC
1014
+ AGGCGG
1015
+ AGGGAA
1016
+ AGGGAT
1017
+ AGGGAC
1018
+ AGGGAG
1019
+ AGGGTA
1020
+ AGGGTT
1021
+ AGGGTC
1022
+ AGGGTG
1023
+ AGGGCA
1024
+ AGGGCT
1025
+ AGGGCC
1026
+ AGGGCG
1027
+ AGGGGA
1028
+ AGGGGT
1029
+ AGGGGC
1030
+ AGGGGG
1031
+ TAAAAA
1032
+ TAAAAT
1033
+ TAAAAC
1034
+ TAAAAG
1035
+ TAAATA
1036
+ TAAATT
1037
+ TAAATC
1038
+ TAAATG
1039
+ TAAACA
1040
+ TAAACT
1041
+ TAAACC
1042
+ TAAACG
1043
+ TAAAGA
1044
+ TAAAGT
1045
+ TAAAGC
1046
+ TAAAGG
1047
+ TAATAA
1048
+ TAATAT
1049
+ TAATAC
1050
+ TAATAG
1051
+ TAATTA
1052
+ TAATTT
1053
+ TAATTC
1054
+ TAATTG
1055
+ TAATCA
1056
+ TAATCT
1057
+ TAATCC
1058
+ TAATCG
1059
+ TAATGA
1060
+ TAATGT
1061
+ TAATGC
1062
+ TAATGG
1063
+ TAACAA
1064
+ TAACAT
1065
+ TAACAC
1066
+ TAACAG
1067
+ TAACTA
1068
+ TAACTT
1069
+ TAACTC
1070
+ TAACTG
1071
+ TAACCA
1072
+ TAACCT
1073
+ TAACCC
1074
+ TAACCG
1075
+ TAACGA
1076
+ TAACGT
1077
+ TAACGC
1078
+ TAACGG
1079
+ TAAGAA
1080
+ TAAGAT
1081
+ TAAGAC
1082
+ TAAGAG
1083
+ TAAGTA
1084
+ TAAGTT
1085
+ TAAGTC
1086
+ TAAGTG
1087
+ TAAGCA
1088
+ TAAGCT
1089
+ TAAGCC
1090
+ TAAGCG
1091
+ TAAGGA
1092
+ TAAGGT
1093
+ TAAGGC
1094
+ TAAGGG
1095
+ TATAAA
1096
+ TATAAT
1097
+ TATAAC
1098
+ TATAAG
1099
+ TATATA
1100
+ TATATT
1101
+ TATATC
1102
+ TATATG
1103
+ TATACA
1104
+ TATACT
1105
+ TATACC
1106
+ TATACG
1107
+ TATAGA
1108
+ TATAGT
1109
+ TATAGC
1110
+ TATAGG
1111
+ TATTAA
1112
+ TATTAT
1113
+ TATTAC
1114
+ TATTAG
1115
+ TATTTA
1116
+ TATTTT
1117
+ TATTTC
1118
+ TATTTG
1119
+ TATTCA
1120
+ TATTCT
1121
+ TATTCC
1122
+ TATTCG
1123
+ TATTGA
1124
+ TATTGT
1125
+ TATTGC
1126
+ TATTGG
1127
+ TATCAA
1128
+ TATCAT
1129
+ TATCAC
1130
+ TATCAG
1131
+ TATCTA
1132
+ TATCTT
1133
+ TATCTC
1134
+ TATCTG
1135
+ TATCCA
1136
+ TATCCT
1137
+ TATCCC
1138
+ TATCCG
1139
+ TATCGA
1140
+ TATCGT
1141
+ TATCGC
1142
+ TATCGG
1143
+ TATGAA
1144
+ TATGAT
1145
+ TATGAC
1146
+ TATGAG
1147
+ TATGTA
1148
+ TATGTT
1149
+ TATGTC
1150
+ TATGTG
1151
+ TATGCA
1152
+ TATGCT
1153
+ TATGCC
1154
+ TATGCG
1155
+ TATGGA
1156
+ TATGGT
1157
+ TATGGC
1158
+ TATGGG
1159
+ TACAAA
1160
+ TACAAT
1161
+ TACAAC
1162
+ TACAAG
1163
+ TACATA
1164
+ TACATT
1165
+ TACATC
1166
+ TACATG
1167
+ TACACA
1168
+ TACACT
1169
+ TACACC
1170
+ TACACG
1171
+ TACAGA
1172
+ TACAGT
1173
+ TACAGC
1174
+ TACAGG
1175
+ TACTAA
1176
+ TACTAT
1177
+ TACTAC
1178
+ TACTAG
1179
+ TACTTA
1180
+ TACTTT
1181
+ TACTTC
1182
+ TACTTG
1183
+ TACTCA
1184
+ TACTCT
1185
+ TACTCC
1186
+ TACTCG
1187
+ TACTGA
1188
+ TACTGT
1189
+ TACTGC
1190
+ TACTGG
1191
+ TACCAA
1192
+ TACCAT
1193
+ TACCAC
1194
+ TACCAG
1195
+ TACCTA
1196
+ TACCTT
1197
+ TACCTC
1198
+ TACCTG
1199
+ TACCCA
1200
+ TACCCT
1201
+ TACCCC
1202
+ TACCCG
1203
+ TACCGA
1204
+ TACCGT
1205
+ TACCGC
1206
+ TACCGG
1207
+ TACGAA
1208
+ TACGAT
1209
+ TACGAC
1210
+ TACGAG
1211
+ TACGTA
1212
+ TACGTT
1213
+ TACGTC
1214
+ TACGTG
1215
+ TACGCA
1216
+ TACGCT
1217
+ TACGCC
1218
+ TACGCG
1219
+ TACGGA
1220
+ TACGGT
1221
+ TACGGC
1222
+ TACGGG
1223
+ TAGAAA
1224
+ TAGAAT
1225
+ TAGAAC
1226
+ TAGAAG
1227
+ TAGATA
1228
+ TAGATT
1229
+ TAGATC
1230
+ TAGATG
1231
+ TAGACA
1232
+ TAGACT
1233
+ TAGACC
1234
+ TAGACG
1235
+ TAGAGA
1236
+ TAGAGT
1237
+ TAGAGC
1238
+ TAGAGG
1239
+ TAGTAA
1240
+ TAGTAT
1241
+ TAGTAC
1242
+ TAGTAG
1243
+ TAGTTA
1244
+ TAGTTT
1245
+ TAGTTC
1246
+ TAGTTG
1247
+ TAGTCA
1248
+ TAGTCT
1249
+ TAGTCC
1250
+ TAGTCG
1251
+ TAGTGA
1252
+ TAGTGT
1253
+ TAGTGC
1254
+ TAGTGG
1255
+ TAGCAA
1256
+ TAGCAT
1257
+ TAGCAC
1258
+ TAGCAG
1259
+ TAGCTA
1260
+ TAGCTT
1261
+ TAGCTC
1262
+ TAGCTG
1263
+ TAGCCA
1264
+ TAGCCT
1265
+ TAGCCC
1266
+ TAGCCG
1267
+ TAGCGA
1268
+ TAGCGT
1269
+ TAGCGC
1270
+ TAGCGG
1271
+ TAGGAA
1272
+ TAGGAT
1273
+ TAGGAC
1274
+ TAGGAG
1275
+ TAGGTA
1276
+ TAGGTT
1277
+ TAGGTC
1278
+ TAGGTG
1279
+ TAGGCA
1280
+ TAGGCT
1281
+ TAGGCC
1282
+ TAGGCG
1283
+ TAGGGA
1284
+ TAGGGT
1285
+ TAGGGC
1286
+ TAGGGG
1287
+ TTAAAA
1288
+ TTAAAT
1289
+ TTAAAC
1290
+ TTAAAG
1291
+ TTAATA
1292
+ TTAATT
1293
+ TTAATC
1294
+ TTAATG
1295
+ TTAACA
1296
+ TTAACT
1297
+ TTAACC
1298
+ TTAACG
1299
+ TTAAGA
1300
+ TTAAGT
1301
+ TTAAGC
1302
+ TTAAGG
1303
+ TTATAA
1304
+ TTATAT
1305
+ TTATAC
1306
+ TTATAG
1307
+ TTATTA
1308
+ TTATTT
1309
+ TTATTC
1310
+ TTATTG
1311
+ TTATCA
1312
+ TTATCT
1313
+ TTATCC
1314
+ TTATCG
1315
+ TTATGA
1316
+ TTATGT
1317
+ TTATGC
1318
+ TTATGG
1319
+ TTACAA
1320
+ TTACAT
1321
+ TTACAC
1322
+ TTACAG
1323
+ TTACTA
1324
+ TTACTT
1325
+ TTACTC
1326
+ TTACTG
1327
+ TTACCA
1328
+ TTACCT
1329
+ TTACCC
1330
+ TTACCG
1331
+ TTACGA
1332
+ TTACGT
1333
+ TTACGC
1334
+ TTACGG
1335
+ TTAGAA
1336
+ TTAGAT
1337
+ TTAGAC
1338
+ TTAGAG
1339
+ TTAGTA
1340
+ TTAGTT
1341
+ TTAGTC
1342
+ TTAGTG
1343
+ TTAGCA
1344
+ TTAGCT
1345
+ TTAGCC
1346
+ TTAGCG
1347
+ TTAGGA
1348
+ TTAGGT
1349
+ TTAGGC
1350
+ TTAGGG
1351
+ TTTAAA
1352
+ TTTAAT
1353
+ TTTAAC
1354
+ TTTAAG
1355
+ TTTATA
1356
+ TTTATT
1357
+ TTTATC
1358
+ TTTATG
1359
+ TTTACA
1360
+ TTTACT
1361
+ TTTACC
1362
+ TTTACG
1363
+ TTTAGA
1364
+ TTTAGT
1365
+ TTTAGC
1366
+ TTTAGG
1367
+ TTTTAA
1368
+ TTTTAT
1369
+ TTTTAC
1370
+ TTTTAG
1371
+ TTTTTA
1372
+ TTTTTT
1373
+ TTTTTC
1374
+ TTTTTG
1375
+ TTTTCA
1376
+ TTTTCT
1377
+ TTTTCC
1378
+ TTTTCG
1379
+ TTTTGA
1380
+ TTTTGT
1381
+ TTTTGC
1382
+ TTTTGG
1383
+ TTTCAA
1384
+ TTTCAT
1385
+ TTTCAC
1386
+ TTTCAG
1387
+ TTTCTA
1388
+ TTTCTT
1389
+ TTTCTC
1390
+ TTTCTG
1391
+ TTTCCA
1392
+ TTTCCT
1393
+ TTTCCC
1394
+ TTTCCG
1395
+ TTTCGA
1396
+ TTTCGT
1397
+ TTTCGC
1398
+ TTTCGG
1399
+ TTTGAA
1400
+ TTTGAT
1401
+ TTTGAC
1402
+ TTTGAG
1403
+ TTTGTA
1404
+ TTTGTT
1405
+ TTTGTC
1406
+ TTTGTG
1407
+ TTTGCA
1408
+ TTTGCT
1409
+ TTTGCC
1410
+ TTTGCG
1411
+ TTTGGA
1412
+ TTTGGT
1413
+ TTTGGC
1414
+ TTTGGG
1415
+ TTCAAA
1416
+ TTCAAT
1417
+ TTCAAC
1418
+ TTCAAG
1419
+ TTCATA
1420
+ TTCATT
1421
+ TTCATC
1422
+ TTCATG
1423
+ TTCACA
1424
+ TTCACT
1425
+ TTCACC
1426
+ TTCACG
1427
+ TTCAGA
1428
+ TTCAGT
1429
+ TTCAGC
1430
+ TTCAGG
1431
+ TTCTAA
1432
+ TTCTAT
1433
+ TTCTAC
1434
+ TTCTAG
1435
+ TTCTTA
1436
+ TTCTTT
1437
+ TTCTTC
1438
+ TTCTTG
1439
+ TTCTCA
1440
+ TTCTCT
1441
+ TTCTCC
1442
+ TTCTCG
1443
+ TTCTGA
1444
+ TTCTGT
1445
+ TTCTGC
1446
+ TTCTGG
1447
+ TTCCAA
1448
+ TTCCAT
1449
+ TTCCAC
1450
+ TTCCAG
1451
+ TTCCTA
1452
+ TTCCTT
1453
+ TTCCTC
1454
+ TTCCTG
1455
+ TTCCCA
1456
+ TTCCCT
1457
+ TTCCCC
1458
+ TTCCCG
1459
+ TTCCGA
1460
+ TTCCGT
1461
+ TTCCGC
1462
+ TTCCGG
1463
+ TTCGAA
1464
+ TTCGAT
1465
+ TTCGAC
1466
+ TTCGAG
1467
+ TTCGTA
1468
+ TTCGTT
1469
+ TTCGTC
1470
+ TTCGTG
1471
+ TTCGCA
1472
+ TTCGCT
1473
+ TTCGCC
1474
+ TTCGCG
1475
+ TTCGGA
1476
+ TTCGGT
1477
+ TTCGGC
1478
+ TTCGGG
1479
+ TTGAAA
1480
+ TTGAAT
1481
+ TTGAAC
1482
+ TTGAAG
1483
+ TTGATA
1484
+ TTGATT
1485
+ TTGATC
1486
+ TTGATG
1487
+ TTGACA
1488
+ TTGACT
1489
+ TTGACC
1490
+ TTGACG
1491
+ TTGAGA
1492
+ TTGAGT
1493
+ TTGAGC
1494
+ TTGAGG
1495
+ TTGTAA
1496
+ TTGTAT
1497
+ TTGTAC
1498
+ TTGTAG
1499
+ TTGTTA
1500
+ TTGTTT
1501
+ TTGTTC
1502
+ TTGTTG
1503
+ TTGTCA
1504
+ TTGTCT
1505
+ TTGTCC
1506
+ TTGTCG
1507
+ TTGTGA
1508
+ TTGTGT
1509
+ TTGTGC
1510
+ TTGTGG
1511
+ TTGCAA
1512
+ TTGCAT
1513
+ TTGCAC
1514
+ TTGCAG
1515
+ TTGCTA
1516
+ TTGCTT
1517
+ TTGCTC
1518
+ TTGCTG
1519
+ TTGCCA
1520
+ TTGCCT
1521
+ TTGCCC
1522
+ TTGCCG
1523
+ TTGCGA
1524
+ TTGCGT
1525
+ TTGCGC
1526
+ TTGCGG
1527
+ TTGGAA
1528
+ TTGGAT
1529
+ TTGGAC
1530
+ TTGGAG
1531
+ TTGGTA
1532
+ TTGGTT
1533
+ TTGGTC
1534
+ TTGGTG
1535
+ TTGGCA
1536
+ TTGGCT
1537
+ TTGGCC
1538
+ TTGGCG
1539
+ TTGGGA
1540
+ TTGGGT
1541
+ TTGGGC
1542
+ TTGGGG
1543
+ TCAAAA
1544
+ TCAAAT
1545
+ TCAAAC
1546
+ TCAAAG
1547
+ TCAATA
1548
+ TCAATT
1549
+ TCAATC
1550
+ TCAATG
1551
+ TCAACA
1552
+ TCAACT
1553
+ TCAACC
1554
+ TCAACG
1555
+ TCAAGA
1556
+ TCAAGT
1557
+ TCAAGC
1558
+ TCAAGG
1559
+ TCATAA
1560
+ TCATAT
1561
+ TCATAC
1562
+ TCATAG
1563
+ TCATTA
1564
+ TCATTT
1565
+ TCATTC
1566
+ TCATTG
1567
+ TCATCA
1568
+ TCATCT
1569
+ TCATCC
1570
+ TCATCG
1571
+ TCATGA
1572
+ TCATGT
1573
+ TCATGC
1574
+ TCATGG
1575
+ TCACAA
1576
+ TCACAT
1577
+ TCACAC
1578
+ TCACAG
1579
+ TCACTA
1580
+ TCACTT
1581
+ TCACTC
1582
+ TCACTG
1583
+ TCACCA
1584
+ TCACCT
1585
+ TCACCC
1586
+ TCACCG
1587
+ TCACGA
1588
+ TCACGT
1589
+ TCACGC
1590
+ TCACGG
1591
+ TCAGAA
1592
+ TCAGAT
1593
+ TCAGAC
1594
+ TCAGAG
1595
+ TCAGTA
1596
+ TCAGTT
1597
+ TCAGTC
1598
+ TCAGTG
1599
+ TCAGCA
1600
+ TCAGCT
1601
+ TCAGCC
1602
+ TCAGCG
1603
+ TCAGGA
1604
+ TCAGGT
1605
+ TCAGGC
1606
+ TCAGGG
1607
+ TCTAAA
1608
+ TCTAAT
1609
+ TCTAAC
1610
+ TCTAAG
1611
+ TCTATA
1612
+ TCTATT
1613
+ TCTATC
1614
+ TCTATG
1615
+ TCTACA
1616
+ TCTACT
1617
+ TCTACC
1618
+ TCTACG
1619
+ TCTAGA
1620
+ TCTAGT
1621
+ TCTAGC
1622
+ TCTAGG
1623
+ TCTTAA
1624
+ TCTTAT
1625
+ TCTTAC
1626
+ TCTTAG
1627
+ TCTTTA
1628
+ TCTTTT
1629
+ TCTTTC
1630
+ TCTTTG
1631
+ TCTTCA
1632
+ TCTTCT
1633
+ TCTTCC
1634
+ TCTTCG
1635
+ TCTTGA
1636
+ TCTTGT
1637
+ TCTTGC
1638
+ TCTTGG
1639
+ TCTCAA
1640
+ TCTCAT
1641
+ TCTCAC
1642
+ TCTCAG
1643
+ TCTCTA
1644
+ TCTCTT
1645
+ TCTCTC
1646
+ TCTCTG
1647
+ TCTCCA
1648
+ TCTCCT
1649
+ TCTCCC
1650
+ TCTCCG
1651
+ TCTCGA
1652
+ TCTCGT
1653
+ TCTCGC
1654
+ TCTCGG
1655
+ TCTGAA
1656
+ TCTGAT
1657
+ TCTGAC
1658
+ TCTGAG
1659
+ TCTGTA
1660
+ TCTGTT
1661
+ TCTGTC
1662
+ TCTGTG
1663
+ TCTGCA
1664
+ TCTGCT
1665
+ TCTGCC
1666
+ TCTGCG
1667
+ TCTGGA
1668
+ TCTGGT
1669
+ TCTGGC
1670
+ TCTGGG
1671
+ TCCAAA
1672
+ TCCAAT
1673
+ TCCAAC
1674
+ TCCAAG
1675
+ TCCATA
1676
+ TCCATT
1677
+ TCCATC
1678
+ TCCATG
1679
+ TCCACA
1680
+ TCCACT
1681
+ TCCACC
1682
+ TCCACG
1683
+ TCCAGA
1684
+ TCCAGT
1685
+ TCCAGC
1686
+ TCCAGG
1687
+ TCCTAA
1688
+ TCCTAT
1689
+ TCCTAC
1690
+ TCCTAG
1691
+ TCCTTA
1692
+ TCCTTT
1693
+ TCCTTC
1694
+ TCCTTG
1695
+ TCCTCA
1696
+ TCCTCT
1697
+ TCCTCC
1698
+ TCCTCG
1699
+ TCCTGA
1700
+ TCCTGT
1701
+ TCCTGC
1702
+ TCCTGG
1703
+ TCCCAA
1704
+ TCCCAT
1705
+ TCCCAC
1706
+ TCCCAG
1707
+ TCCCTA
1708
+ TCCCTT
1709
+ TCCCTC
1710
+ TCCCTG
1711
+ TCCCCA
1712
+ TCCCCT
1713
+ TCCCCC
1714
+ TCCCCG
1715
+ TCCCGA
1716
+ TCCCGT
1717
+ TCCCGC
1718
+ TCCCGG
1719
+ TCCGAA
1720
+ TCCGAT
1721
+ TCCGAC
1722
+ TCCGAG
1723
+ TCCGTA
1724
+ TCCGTT
1725
+ TCCGTC
1726
+ TCCGTG
1727
+ TCCGCA
1728
+ TCCGCT
1729
+ TCCGCC
1730
+ TCCGCG
1731
+ TCCGGA
1732
+ TCCGGT
1733
+ TCCGGC
1734
+ TCCGGG
1735
+ TCGAAA
1736
+ TCGAAT
1737
+ TCGAAC
1738
+ TCGAAG
1739
+ TCGATA
1740
+ TCGATT
1741
+ TCGATC
1742
+ TCGATG
1743
+ TCGACA
1744
+ TCGACT
1745
+ TCGACC
1746
+ TCGACG
1747
+ TCGAGA
1748
+ TCGAGT
1749
+ TCGAGC
1750
+ TCGAGG
1751
+ TCGTAA
1752
+ TCGTAT
1753
+ TCGTAC
1754
+ TCGTAG
1755
+ TCGTTA
1756
+ TCGTTT
1757
+ TCGTTC
1758
+ TCGTTG
1759
+ TCGTCA
1760
+ TCGTCT
1761
+ TCGTCC
1762
+ TCGTCG
1763
+ TCGTGA
1764
+ TCGTGT
1765
+ TCGTGC
1766
+ TCGTGG
1767
+ TCGCAA
1768
+ TCGCAT
1769
+ TCGCAC
1770
+ TCGCAG
1771
+ TCGCTA
1772
+ TCGCTT
1773
+ TCGCTC
1774
+ TCGCTG
1775
+ TCGCCA
1776
+ TCGCCT
1777
+ TCGCCC
1778
+ TCGCCG
1779
+ TCGCGA
1780
+ TCGCGT
1781
+ TCGCGC
1782
+ TCGCGG
1783
+ TCGGAA
1784
+ TCGGAT
1785
+ TCGGAC
1786
+ TCGGAG
1787
+ TCGGTA
1788
+ TCGGTT
1789
+ TCGGTC
1790
+ TCGGTG
1791
+ TCGGCA
1792
+ TCGGCT
1793
+ TCGGCC
1794
+ TCGGCG
1795
+ TCGGGA
1796
+ TCGGGT
1797
+ TCGGGC
1798
+ TCGGGG
1799
+ TGAAAA
1800
+ TGAAAT
1801
+ TGAAAC
1802
+ TGAAAG
1803
+ TGAATA
1804
+ TGAATT
1805
+ TGAATC
1806
+ TGAATG
1807
+ TGAACA
1808
+ TGAACT
1809
+ TGAACC
1810
+ TGAACG
1811
+ TGAAGA
1812
+ TGAAGT
1813
+ TGAAGC
1814
+ TGAAGG
1815
+ TGATAA
1816
+ TGATAT
1817
+ TGATAC
1818
+ TGATAG
1819
+ TGATTA
1820
+ TGATTT
1821
+ TGATTC
1822
+ TGATTG
1823
+ TGATCA
1824
+ TGATCT
1825
+ TGATCC
1826
+ TGATCG
1827
+ TGATGA
1828
+ TGATGT
1829
+ TGATGC
1830
+ TGATGG
1831
+ TGACAA
1832
+ TGACAT
1833
+ TGACAC
1834
+ TGACAG
1835
+ TGACTA
1836
+ TGACTT
1837
+ TGACTC
1838
+ TGACTG
1839
+ TGACCA
1840
+ TGACCT
1841
+ TGACCC
1842
+ TGACCG
1843
+ TGACGA
1844
+ TGACGT
1845
+ TGACGC
1846
+ TGACGG
1847
+ TGAGAA
1848
+ TGAGAT
1849
+ TGAGAC
1850
+ TGAGAG
1851
+ TGAGTA
1852
+ TGAGTT
1853
+ TGAGTC
1854
+ TGAGTG
1855
+ TGAGCA
1856
+ TGAGCT
1857
+ TGAGCC
1858
+ TGAGCG
1859
+ TGAGGA
1860
+ TGAGGT
1861
+ TGAGGC
1862
+ TGAGGG
1863
+ TGTAAA
1864
+ TGTAAT
1865
+ TGTAAC
1866
+ TGTAAG
1867
+ TGTATA
1868
+ TGTATT
1869
+ TGTATC
1870
+ TGTATG
1871
+ TGTACA
1872
+ TGTACT
1873
+ TGTACC
1874
+ TGTACG
1875
+ TGTAGA
1876
+ TGTAGT
1877
+ TGTAGC
1878
+ TGTAGG
1879
+ TGTTAA
1880
+ TGTTAT
1881
+ TGTTAC
1882
+ TGTTAG
1883
+ TGTTTA
1884
+ TGTTTT
1885
+ TGTTTC
1886
+ TGTTTG
1887
+ TGTTCA
1888
+ TGTTCT
1889
+ TGTTCC
1890
+ TGTTCG
1891
+ TGTTGA
1892
+ TGTTGT
1893
+ TGTTGC
1894
+ TGTTGG
1895
+ TGTCAA
1896
+ TGTCAT
1897
+ TGTCAC
1898
+ TGTCAG
1899
+ TGTCTA
1900
+ TGTCTT
1901
+ TGTCTC
1902
+ TGTCTG
1903
+ TGTCCA
1904
+ TGTCCT
1905
+ TGTCCC
1906
+ TGTCCG
1907
+ TGTCGA
1908
+ TGTCGT
1909
+ TGTCGC
1910
+ TGTCGG
1911
+ TGTGAA
1912
+ TGTGAT
1913
+ TGTGAC
1914
+ TGTGAG
1915
+ TGTGTA
1916
+ TGTGTT
1917
+ TGTGTC
1918
+ TGTGTG
1919
+ TGTGCA
1920
+ TGTGCT
1921
+ TGTGCC
1922
+ TGTGCG
1923
+ TGTGGA
1924
+ TGTGGT
1925
+ TGTGGC
1926
+ TGTGGG
1927
+ TGCAAA
1928
+ TGCAAT
1929
+ TGCAAC
1930
+ TGCAAG
1931
+ TGCATA
1932
+ TGCATT
1933
+ TGCATC
1934
+ TGCATG
1935
+ TGCACA
1936
+ TGCACT
1937
+ TGCACC
1938
+ TGCACG
1939
+ TGCAGA
1940
+ TGCAGT
1941
+ TGCAGC
1942
+ TGCAGG
1943
+ TGCTAA
1944
+ TGCTAT
1945
+ TGCTAC
1946
+ TGCTAG
1947
+ TGCTTA
1948
+ TGCTTT
1949
+ TGCTTC
1950
+ TGCTTG
1951
+ TGCTCA
1952
+ TGCTCT
1953
+ TGCTCC
1954
+ TGCTCG
1955
+ TGCTGA
1956
+ TGCTGT
1957
+ TGCTGC
1958
+ TGCTGG
1959
+ TGCCAA
1960
+ TGCCAT
1961
+ TGCCAC
1962
+ TGCCAG
1963
+ TGCCTA
1964
+ TGCCTT
1965
+ TGCCTC
1966
+ TGCCTG
1967
+ TGCCCA
1968
+ TGCCCT
1969
+ TGCCCC
1970
+ TGCCCG
1971
+ TGCCGA
1972
+ TGCCGT
1973
+ TGCCGC
1974
+ TGCCGG
1975
+ TGCGAA
1976
+ TGCGAT
1977
+ TGCGAC
1978
+ TGCGAG
1979
+ TGCGTA
1980
+ TGCGTT
1981
+ TGCGTC
1982
+ TGCGTG
1983
+ TGCGCA
1984
+ TGCGCT
1985
+ TGCGCC
1986
+ TGCGCG
1987
+ TGCGGA
1988
+ TGCGGT
1989
+ TGCGGC
1990
+ TGCGGG
1991
+ TGGAAA
1992
+ TGGAAT
1993
+ TGGAAC
1994
+ TGGAAG
1995
+ TGGATA
1996
+ TGGATT
1997
+ TGGATC
1998
+ TGGATG
1999
+ TGGACA
2000
+ TGGACT
2001
+ TGGACC
2002
+ TGGACG
2003
+ TGGAGA
2004
+ TGGAGT
2005
+ TGGAGC
2006
+ TGGAGG
2007
+ TGGTAA
2008
+ TGGTAT
2009
+ TGGTAC
2010
+ TGGTAG
2011
+ TGGTTA
2012
+ TGGTTT
2013
+ TGGTTC
2014
+ TGGTTG
2015
+ TGGTCA
2016
+ TGGTCT
2017
+ TGGTCC
2018
+ TGGTCG
2019
+ TGGTGA
2020
+ TGGTGT
2021
+ TGGTGC
2022
+ TGGTGG
2023
+ TGGCAA
2024
+ TGGCAT
2025
+ TGGCAC
2026
+ TGGCAG
2027
+ TGGCTA
2028
+ TGGCTT
2029
+ TGGCTC
2030
+ TGGCTG
2031
+ TGGCCA
2032
+ TGGCCT
2033
+ TGGCCC
2034
+ TGGCCG
2035
+ TGGCGA
2036
+ TGGCGT
2037
+ TGGCGC
2038
+ TGGCGG
2039
+ TGGGAA
2040
+ TGGGAT
2041
+ TGGGAC
2042
+ TGGGAG
2043
+ TGGGTA
2044
+ TGGGTT
2045
+ TGGGTC
2046
+ TGGGTG
2047
+ TGGGCA
2048
+ TGGGCT
2049
+ TGGGCC
2050
+ TGGGCG
2051
+ TGGGGA
2052
+ TGGGGT
2053
+ TGGGGC
2054
+ TGGGGG
2055
+ CAAAAA
2056
+ CAAAAT
2057
+ CAAAAC
2058
+ CAAAAG
2059
+ CAAATA
2060
+ CAAATT
2061
+ CAAATC
2062
+ CAAATG
2063
+ CAAACA
2064
+ CAAACT
2065
+ CAAACC
2066
+ CAAACG
2067
+ CAAAGA
2068
+ CAAAGT
2069
+ CAAAGC
2070
+ CAAAGG
2071
+ CAATAA
2072
+ CAATAT
2073
+ CAATAC
2074
+ CAATAG
2075
+ CAATTA
2076
+ CAATTT
2077
+ CAATTC
2078
+ CAATTG
2079
+ CAATCA
2080
+ CAATCT
2081
+ CAATCC
2082
+ CAATCG
2083
+ CAATGA
2084
+ CAATGT
2085
+ CAATGC
2086
+ CAATGG
2087
+ CAACAA
2088
+ CAACAT
2089
+ CAACAC
2090
+ CAACAG
2091
+ CAACTA
2092
+ CAACTT
2093
+ CAACTC
2094
+ CAACTG
2095
+ CAACCA
2096
+ CAACCT
2097
+ CAACCC
2098
+ CAACCG
2099
+ CAACGA
2100
+ CAACGT
2101
+ CAACGC
2102
+ CAACGG
2103
+ CAAGAA
2104
+ CAAGAT
2105
+ CAAGAC
2106
+ CAAGAG
2107
+ CAAGTA
2108
+ CAAGTT
2109
+ CAAGTC
2110
+ CAAGTG
2111
+ CAAGCA
2112
+ CAAGCT
2113
+ CAAGCC
2114
+ CAAGCG
2115
+ CAAGGA
2116
+ CAAGGT
2117
+ CAAGGC
2118
+ CAAGGG
2119
+ CATAAA
2120
+ CATAAT
2121
+ CATAAC
2122
+ CATAAG
2123
+ CATATA
2124
+ CATATT
2125
+ CATATC
2126
+ CATATG
2127
+ CATACA
2128
+ CATACT
2129
+ CATACC
2130
+ CATACG
2131
+ CATAGA
2132
+ CATAGT
2133
+ CATAGC
2134
+ CATAGG
2135
+ CATTAA
2136
+ CATTAT
2137
+ CATTAC
2138
+ CATTAG
2139
+ CATTTA
2140
+ CATTTT
2141
+ CATTTC
2142
+ CATTTG
2143
+ CATTCA
2144
+ CATTCT
2145
+ CATTCC
2146
+ CATTCG
2147
+ CATTGA
2148
+ CATTGT
2149
+ CATTGC
2150
+ CATTGG
2151
+ CATCAA
2152
+ CATCAT
2153
+ CATCAC
2154
+ CATCAG
2155
+ CATCTA
2156
+ CATCTT
2157
+ CATCTC
2158
+ CATCTG
2159
+ CATCCA
2160
+ CATCCT
2161
+ CATCCC
2162
+ CATCCG
2163
+ CATCGA
2164
+ CATCGT
2165
+ CATCGC
2166
+ CATCGG
2167
+ CATGAA
2168
+ CATGAT
2169
+ CATGAC
2170
+ CATGAG
2171
+ CATGTA
2172
+ CATGTT
2173
+ CATGTC
2174
+ CATGTG
2175
+ CATGCA
2176
+ CATGCT
2177
+ CATGCC
2178
+ CATGCG
2179
+ CATGGA
2180
+ CATGGT
2181
+ CATGGC
2182
+ CATGGG
2183
+ CACAAA
2184
+ CACAAT
2185
+ CACAAC
2186
+ CACAAG
2187
+ CACATA
2188
+ CACATT
2189
+ CACATC
2190
+ CACATG
2191
+ CACACA
2192
+ CACACT
2193
+ CACACC
2194
+ CACACG
2195
+ CACAGA
2196
+ CACAGT
2197
+ CACAGC
2198
+ CACAGG
2199
+ CACTAA
2200
+ CACTAT
2201
+ CACTAC
2202
+ CACTAG
2203
+ CACTTA
2204
+ CACTTT
2205
+ CACTTC
2206
+ CACTTG
2207
+ CACTCA
2208
+ CACTCT
2209
+ CACTCC
2210
+ CACTCG
2211
+ CACTGA
2212
+ CACTGT
2213
+ CACTGC
2214
+ CACTGG
2215
+ CACCAA
2216
+ CACCAT
2217
+ CACCAC
2218
+ CACCAG
2219
+ CACCTA
2220
+ CACCTT
2221
+ CACCTC
2222
+ CACCTG
2223
+ CACCCA
2224
+ CACCCT
2225
+ CACCCC
2226
+ CACCCG
2227
+ CACCGA
2228
+ CACCGT
2229
+ CACCGC
2230
+ CACCGG
2231
+ CACGAA
2232
+ CACGAT
2233
+ CACGAC
2234
+ CACGAG
2235
+ CACGTA
2236
+ CACGTT
2237
+ CACGTC
2238
+ CACGTG
2239
+ CACGCA
2240
+ CACGCT
2241
+ CACGCC
2242
+ CACGCG
2243
+ CACGGA
2244
+ CACGGT
2245
+ CACGGC
2246
+ CACGGG
2247
+ CAGAAA
2248
+ CAGAAT
2249
+ CAGAAC
2250
+ CAGAAG
2251
+ CAGATA
2252
+ CAGATT
2253
+ CAGATC
2254
+ CAGATG
2255
+ CAGACA
2256
+ CAGACT
2257
+ CAGACC
2258
+ CAGACG
2259
+ CAGAGA
2260
+ CAGAGT
2261
+ CAGAGC
2262
+ CAGAGG
2263
+ CAGTAA
2264
+ CAGTAT
2265
+ CAGTAC
2266
+ CAGTAG
2267
+ CAGTTA
2268
+ CAGTTT
2269
+ CAGTTC
2270
+ CAGTTG
2271
+ CAGTCA
2272
+ CAGTCT
2273
+ CAGTCC
2274
+ CAGTCG
2275
+ CAGTGA
2276
+ CAGTGT
2277
+ CAGTGC
2278
+ CAGTGG
2279
+ CAGCAA
2280
+ CAGCAT
2281
+ CAGCAC
2282
+ CAGCAG
2283
+ CAGCTA
2284
+ CAGCTT
2285
+ CAGCTC
2286
+ CAGCTG
2287
+ CAGCCA
2288
+ CAGCCT
2289
+ CAGCCC
2290
+ CAGCCG
2291
+ CAGCGA
2292
+ CAGCGT
2293
+ CAGCGC
2294
+ CAGCGG
2295
+ CAGGAA
2296
+ CAGGAT
2297
+ CAGGAC
2298
+ CAGGAG
2299
+ CAGGTA
2300
+ CAGGTT
2301
+ CAGGTC
2302
+ CAGGTG
2303
+ CAGGCA
2304
+ CAGGCT
2305
+ CAGGCC
2306
+ CAGGCG
2307
+ CAGGGA
2308
+ CAGGGT
2309
+ CAGGGC
2310
+ CAGGGG
2311
+ CTAAAA
2312
+ CTAAAT
2313
+ CTAAAC
2314
+ CTAAAG
2315
+ CTAATA
2316
+ CTAATT
2317
+ CTAATC
2318
+ CTAATG
2319
+ CTAACA
2320
+ CTAACT
2321
+ CTAACC
2322
+ CTAACG
2323
+ CTAAGA
2324
+ CTAAGT
2325
+ CTAAGC
2326
+ CTAAGG
2327
+ CTATAA
2328
+ CTATAT
2329
+ CTATAC
2330
+ CTATAG
2331
+ CTATTA
2332
+ CTATTT
2333
+ CTATTC
2334
+ CTATTG
2335
+ CTATCA
2336
+ CTATCT
2337
+ CTATCC
2338
+ CTATCG
2339
+ CTATGA
2340
+ CTATGT
2341
+ CTATGC
2342
+ CTATGG
2343
+ CTACAA
2344
+ CTACAT
2345
+ CTACAC
2346
+ CTACAG
2347
+ CTACTA
2348
+ CTACTT
2349
+ CTACTC
2350
+ CTACTG
2351
+ CTACCA
2352
+ CTACCT
2353
+ CTACCC
2354
+ CTACCG
2355
+ CTACGA
2356
+ CTACGT
2357
+ CTACGC
2358
+ CTACGG
2359
+ CTAGAA
2360
+ CTAGAT
2361
+ CTAGAC
2362
+ CTAGAG
2363
+ CTAGTA
2364
+ CTAGTT
2365
+ CTAGTC
2366
+ CTAGTG
2367
+ CTAGCA
2368
+ CTAGCT
2369
+ CTAGCC
2370
+ CTAGCG
2371
+ CTAGGA
2372
+ CTAGGT
2373
+ CTAGGC
2374
+ CTAGGG
2375
+ CTTAAA
2376
+ CTTAAT
2377
+ CTTAAC
2378
+ CTTAAG
2379
+ CTTATA
2380
+ CTTATT
2381
+ CTTATC
2382
+ CTTATG
2383
+ CTTACA
2384
+ CTTACT
2385
+ CTTACC
2386
+ CTTACG
2387
+ CTTAGA
2388
+ CTTAGT
2389
+ CTTAGC
2390
+ CTTAGG
2391
+ CTTTAA
2392
+ CTTTAT
2393
+ CTTTAC
2394
+ CTTTAG
2395
+ CTTTTA
2396
+ CTTTTT
2397
+ CTTTTC
2398
+ CTTTTG
2399
+ CTTTCA
2400
+ CTTTCT
2401
+ CTTTCC
2402
+ CTTTCG
2403
+ CTTTGA
2404
+ CTTTGT
2405
+ CTTTGC
2406
+ CTTTGG
2407
+ CTTCAA
2408
+ CTTCAT
2409
+ CTTCAC
2410
+ CTTCAG
2411
+ CTTCTA
2412
+ CTTCTT
2413
+ CTTCTC
2414
+ CTTCTG
2415
+ CTTCCA
2416
+ CTTCCT
2417
+ CTTCCC
2418
+ CTTCCG
2419
+ CTTCGA
2420
+ CTTCGT
2421
+ CTTCGC
2422
+ CTTCGG
2423
+ CTTGAA
2424
+ CTTGAT
2425
+ CTTGAC
2426
+ CTTGAG
2427
+ CTTGTA
2428
+ CTTGTT
2429
+ CTTGTC
2430
+ CTTGTG
2431
+ CTTGCA
2432
+ CTTGCT
2433
+ CTTGCC
2434
+ CTTGCG
2435
+ CTTGGA
2436
+ CTTGGT
2437
+ CTTGGC
2438
+ CTTGGG
2439
+ CTCAAA
2440
+ CTCAAT
2441
+ CTCAAC
2442
+ CTCAAG
2443
+ CTCATA
2444
+ CTCATT
2445
+ CTCATC
2446
+ CTCATG
2447
+ CTCACA
2448
+ CTCACT
2449
+ CTCACC
2450
+ CTCACG
2451
+ CTCAGA
2452
+ CTCAGT
2453
+ CTCAGC
2454
+ CTCAGG
2455
+ CTCTAA
2456
+ CTCTAT
2457
+ CTCTAC
2458
+ CTCTAG
2459
+ CTCTTA
2460
+ CTCTTT
2461
+ CTCTTC
2462
+ CTCTTG
2463
+ CTCTCA
2464
+ CTCTCT
2465
+ CTCTCC
2466
+ CTCTCG
2467
+ CTCTGA
2468
+ CTCTGT
2469
+ CTCTGC
2470
+ CTCTGG
2471
+ CTCCAA
2472
+ CTCCAT
2473
+ CTCCAC
2474
+ CTCCAG
2475
+ CTCCTA
2476
+ CTCCTT
2477
+ CTCCTC
2478
+ CTCCTG
2479
+ CTCCCA
2480
+ CTCCCT
2481
+ CTCCCC
2482
+ CTCCCG
2483
+ CTCCGA
2484
+ CTCCGT
2485
+ CTCCGC
2486
+ CTCCGG
2487
+ CTCGAA
2488
+ CTCGAT
2489
+ CTCGAC
2490
+ CTCGAG
2491
+ CTCGTA
2492
+ CTCGTT
2493
+ CTCGTC
2494
+ CTCGTG
2495
+ CTCGCA
2496
+ CTCGCT
2497
+ CTCGCC
2498
+ CTCGCG
2499
+ CTCGGA
2500
+ CTCGGT
2501
+ CTCGGC
2502
+ CTCGGG
2503
+ CTGAAA
2504
+ CTGAAT
2505
+ CTGAAC
2506
+ CTGAAG
2507
+ CTGATA
2508
+ CTGATT
2509
+ CTGATC
2510
+ CTGATG
2511
+ CTGACA
2512
+ CTGACT
2513
+ CTGACC
2514
+ CTGACG
2515
+ CTGAGA
2516
+ CTGAGT
2517
+ CTGAGC
2518
+ CTGAGG
2519
+ CTGTAA
2520
+ CTGTAT
2521
+ CTGTAC
2522
+ CTGTAG
2523
+ CTGTTA
2524
+ CTGTTT
2525
+ CTGTTC
2526
+ CTGTTG
2527
+ CTGTCA
2528
+ CTGTCT
2529
+ CTGTCC
2530
+ CTGTCG
2531
+ CTGTGA
2532
+ CTGTGT
2533
+ CTGTGC
2534
+ CTGTGG
2535
+ CTGCAA
2536
+ CTGCAT
2537
+ CTGCAC
2538
+ CTGCAG
2539
+ CTGCTA
2540
+ CTGCTT
2541
+ CTGCTC
2542
+ CTGCTG
2543
+ CTGCCA
2544
+ CTGCCT
2545
+ CTGCCC
2546
+ CTGCCG
2547
+ CTGCGA
2548
+ CTGCGT
2549
+ CTGCGC
2550
+ CTGCGG
2551
+ CTGGAA
2552
+ CTGGAT
2553
+ CTGGAC
2554
+ CTGGAG
2555
+ CTGGTA
2556
+ CTGGTT
2557
+ CTGGTC
2558
+ CTGGTG
2559
+ CTGGCA
2560
+ CTGGCT
2561
+ CTGGCC
2562
+ CTGGCG
2563
+ CTGGGA
2564
+ CTGGGT
2565
+ CTGGGC
2566
+ CTGGGG
2567
+ CCAAAA
2568
+ CCAAAT
2569
+ CCAAAC
2570
+ CCAAAG
2571
+ CCAATA
2572
+ CCAATT
2573
+ CCAATC
2574
+ CCAATG
2575
+ CCAACA
2576
+ CCAACT
2577
+ CCAACC
2578
+ CCAACG
2579
+ CCAAGA
2580
+ CCAAGT
2581
+ CCAAGC
2582
+ CCAAGG
2583
+ CCATAA
2584
+ CCATAT
2585
+ CCATAC
2586
+ CCATAG
2587
+ CCATTA
2588
+ CCATTT
2589
+ CCATTC
2590
+ CCATTG
2591
+ CCATCA
2592
+ CCATCT
2593
+ CCATCC
2594
+ CCATCG
2595
+ CCATGA
2596
+ CCATGT
2597
+ CCATGC
2598
+ CCATGG
2599
+ CCACAA
2600
+ CCACAT
2601
+ CCACAC
2602
+ CCACAG
2603
+ CCACTA
2604
+ CCACTT
2605
+ CCACTC
2606
+ CCACTG
2607
+ CCACCA
2608
+ CCACCT
2609
+ CCACCC
2610
+ CCACCG
2611
+ CCACGA
2612
+ CCACGT
2613
+ CCACGC
2614
+ CCACGG
2615
+ CCAGAA
2616
+ CCAGAT
2617
+ CCAGAC
2618
+ CCAGAG
2619
+ CCAGTA
2620
+ CCAGTT
2621
+ CCAGTC
2622
+ CCAGTG
2623
+ CCAGCA
2624
+ CCAGCT
2625
+ CCAGCC
2626
+ CCAGCG
2627
+ CCAGGA
2628
+ CCAGGT
2629
+ CCAGGC
2630
+ CCAGGG
2631
+ CCTAAA
2632
+ CCTAAT
2633
+ CCTAAC
2634
+ CCTAAG
2635
+ CCTATA
2636
+ CCTATT
2637
+ CCTATC
2638
+ CCTATG
2639
+ CCTACA
2640
+ CCTACT
2641
+ CCTACC
2642
+ CCTACG
2643
+ CCTAGA
2644
+ CCTAGT
2645
+ CCTAGC
2646
+ CCTAGG
2647
+ CCTTAA
2648
+ CCTTAT
2649
+ CCTTAC
2650
+ CCTTAG
2651
+ CCTTTA
2652
+ CCTTTT
2653
+ CCTTTC
2654
+ CCTTTG
2655
+ CCTTCA
2656
+ CCTTCT
2657
+ CCTTCC
2658
+ CCTTCG
2659
+ CCTTGA
2660
+ CCTTGT
2661
+ CCTTGC
2662
+ CCTTGG
2663
+ CCTCAA
2664
+ CCTCAT
2665
+ CCTCAC
2666
+ CCTCAG
2667
+ CCTCTA
2668
+ CCTCTT
2669
+ CCTCTC
2670
+ CCTCTG
2671
+ CCTCCA
2672
+ CCTCCT
2673
+ CCTCCC
2674
+ CCTCCG
2675
+ CCTCGA
2676
+ CCTCGT
2677
+ CCTCGC
2678
+ CCTCGG
2679
+ CCTGAA
2680
+ CCTGAT
2681
+ CCTGAC
2682
+ CCTGAG
2683
+ CCTGTA
2684
+ CCTGTT
2685
+ CCTGTC
2686
+ CCTGTG
2687
+ CCTGCA
2688
+ CCTGCT
2689
+ CCTGCC
2690
+ CCTGCG
2691
+ CCTGGA
2692
+ CCTGGT
2693
+ CCTGGC
2694
+ CCTGGG
2695
+ CCCAAA
2696
+ CCCAAT
2697
+ CCCAAC
2698
+ CCCAAG
2699
+ CCCATA
2700
+ CCCATT
2701
+ CCCATC
2702
+ CCCATG
2703
+ CCCACA
2704
+ CCCACT
2705
+ CCCACC
2706
+ CCCACG
2707
+ CCCAGA
2708
+ CCCAGT
2709
+ CCCAGC
2710
+ CCCAGG
2711
+ CCCTAA
2712
+ CCCTAT
2713
+ CCCTAC
2714
+ CCCTAG
2715
+ CCCTTA
2716
+ CCCTTT
2717
+ CCCTTC
2718
+ CCCTTG
2719
+ CCCTCA
2720
+ CCCTCT
2721
+ CCCTCC
2722
+ CCCTCG
2723
+ CCCTGA
2724
+ CCCTGT
2725
+ CCCTGC
2726
+ CCCTGG
2727
+ CCCCAA
2728
+ CCCCAT
2729
+ CCCCAC
2730
+ CCCCAG
2731
+ CCCCTA
2732
+ CCCCTT
2733
+ CCCCTC
2734
+ CCCCTG
2735
+ CCCCCA
2736
+ CCCCCT
2737
+ CCCCCC
2738
+ CCCCCG
2739
+ CCCCGA
2740
+ CCCCGT
2741
+ CCCCGC
2742
+ CCCCGG
2743
+ CCCGAA
2744
+ CCCGAT
2745
+ CCCGAC
2746
+ CCCGAG
2747
+ CCCGTA
2748
+ CCCGTT
2749
+ CCCGTC
2750
+ CCCGTG
2751
+ CCCGCA
2752
+ CCCGCT
2753
+ CCCGCC
2754
+ CCCGCG
2755
+ CCCGGA
2756
+ CCCGGT
2757
+ CCCGGC
2758
+ CCCGGG
2759
+ CCGAAA
2760
+ CCGAAT
2761
+ CCGAAC
2762
+ CCGAAG
2763
+ CCGATA
2764
+ CCGATT
2765
+ CCGATC
2766
+ CCGATG
2767
+ CCGACA
2768
+ CCGACT
2769
+ CCGACC
2770
+ CCGACG
2771
+ CCGAGA
2772
+ CCGAGT
2773
+ CCGAGC
2774
+ CCGAGG
2775
+ CCGTAA
2776
+ CCGTAT
2777
+ CCGTAC
2778
+ CCGTAG
2779
+ CCGTTA
2780
+ CCGTTT
2781
+ CCGTTC
2782
+ CCGTTG
2783
+ CCGTCA
2784
+ CCGTCT
2785
+ CCGTCC
2786
+ CCGTCG
2787
+ CCGTGA
2788
+ CCGTGT
2789
+ CCGTGC
2790
+ CCGTGG
2791
+ CCGCAA
2792
+ CCGCAT
2793
+ CCGCAC
2794
+ CCGCAG
2795
+ CCGCTA
2796
+ CCGCTT
2797
+ CCGCTC
2798
+ CCGCTG
2799
+ CCGCCA
2800
+ CCGCCT
2801
+ CCGCCC
2802
+ CCGCCG
2803
+ CCGCGA
2804
+ CCGCGT
2805
+ CCGCGC
2806
+ CCGCGG
2807
+ CCGGAA
2808
+ CCGGAT
2809
+ CCGGAC
2810
+ CCGGAG
2811
+ CCGGTA
2812
+ CCGGTT
2813
+ CCGGTC
2814
+ CCGGTG
2815
+ CCGGCA
2816
+ CCGGCT
2817
+ CCGGCC
2818
+ CCGGCG
2819
+ CCGGGA
2820
+ CCGGGT
2821
+ CCGGGC
2822
+ CCGGGG
2823
+ CGAAAA
2824
+ CGAAAT
2825
+ CGAAAC
2826
+ CGAAAG
2827
+ CGAATA
2828
+ CGAATT
2829
+ CGAATC
2830
+ CGAATG
2831
+ CGAACA
2832
+ CGAACT
2833
+ CGAACC
2834
+ CGAACG
2835
+ CGAAGA
2836
+ CGAAGT
2837
+ CGAAGC
2838
+ CGAAGG
2839
+ CGATAA
2840
+ CGATAT
2841
+ CGATAC
2842
+ CGATAG
2843
+ CGATTA
2844
+ CGATTT
2845
+ CGATTC
2846
+ CGATTG
2847
+ CGATCA
2848
+ CGATCT
2849
+ CGATCC
2850
+ CGATCG
2851
+ CGATGA
2852
+ CGATGT
2853
+ CGATGC
2854
+ CGATGG
2855
+ CGACAA
2856
+ CGACAT
2857
+ CGACAC
2858
+ CGACAG
2859
+ CGACTA
2860
+ CGACTT
2861
+ CGACTC
2862
+ CGACTG
2863
+ CGACCA
2864
+ CGACCT
2865
+ CGACCC
2866
+ CGACCG
2867
+ CGACGA
2868
+ CGACGT
2869
+ CGACGC
2870
+ CGACGG
2871
+ CGAGAA
2872
+ CGAGAT
2873
+ CGAGAC
2874
+ CGAGAG
2875
+ CGAGTA
2876
+ CGAGTT
2877
+ CGAGTC
2878
+ CGAGTG
2879
+ CGAGCA
2880
+ CGAGCT
2881
+ CGAGCC
2882
+ CGAGCG
2883
+ CGAGGA
2884
+ CGAGGT
2885
+ CGAGGC
2886
+ CGAGGG
2887
+ CGTAAA
2888
+ CGTAAT
2889
+ CGTAAC
2890
+ CGTAAG
2891
+ CGTATA
2892
+ CGTATT
2893
+ CGTATC
2894
+ CGTATG
2895
+ CGTACA
2896
+ CGTACT
2897
+ CGTACC
2898
+ CGTACG
2899
+ CGTAGA
2900
+ CGTAGT
2901
+ CGTAGC
2902
+ CGTAGG
2903
+ CGTTAA
2904
+ CGTTAT
2905
+ CGTTAC
2906
+ CGTTAG
2907
+ CGTTTA
2908
+ CGTTTT
2909
+ CGTTTC
2910
+ CGTTTG
2911
+ CGTTCA
2912
+ CGTTCT
2913
+ CGTTCC
2914
+ CGTTCG
2915
+ CGTTGA
2916
+ CGTTGT
2917
+ CGTTGC
2918
+ CGTTGG
2919
+ CGTCAA
2920
+ CGTCAT
2921
+ CGTCAC
2922
+ CGTCAG
2923
+ CGTCTA
2924
+ CGTCTT
2925
+ CGTCTC
2926
+ CGTCTG
2927
+ CGTCCA
2928
+ CGTCCT
2929
+ CGTCCC
2930
+ CGTCCG
2931
+ CGTCGA
2932
+ CGTCGT
2933
+ CGTCGC
2934
+ CGTCGG
2935
+ CGTGAA
2936
+ CGTGAT
2937
+ CGTGAC
2938
+ CGTGAG
2939
+ CGTGTA
2940
+ CGTGTT
2941
+ CGTGTC
2942
+ CGTGTG
2943
+ CGTGCA
2944
+ CGTGCT
2945
+ CGTGCC
2946
+ CGTGCG
2947
+ CGTGGA
2948
+ CGTGGT
2949
+ CGTGGC
2950
+ CGTGGG
2951
+ CGCAAA
2952
+ CGCAAT
2953
+ CGCAAC
2954
+ CGCAAG
2955
+ CGCATA
2956
+ CGCATT
2957
+ CGCATC
2958
+ CGCATG
2959
+ CGCACA
2960
+ CGCACT
2961
+ CGCACC
2962
+ CGCACG
2963
+ CGCAGA
2964
+ CGCAGT
2965
+ CGCAGC
2966
+ CGCAGG
2967
+ CGCTAA
2968
+ CGCTAT
2969
+ CGCTAC
2970
+ CGCTAG
2971
+ CGCTTA
2972
+ CGCTTT
2973
+ CGCTTC
2974
+ CGCTTG
2975
+ CGCTCA
2976
+ CGCTCT
2977
+ CGCTCC
2978
+ CGCTCG
2979
+ CGCTGA
2980
+ CGCTGT
2981
+ CGCTGC
2982
+ CGCTGG
2983
+ CGCCAA
2984
+ CGCCAT
2985
+ CGCCAC
2986
+ CGCCAG
2987
+ CGCCTA
2988
+ CGCCTT
2989
+ CGCCTC
2990
+ CGCCTG
2991
+ CGCCCA
2992
+ CGCCCT
2993
+ CGCCCC
2994
+ CGCCCG
2995
+ CGCCGA
2996
+ CGCCGT
2997
+ CGCCGC
2998
+ CGCCGG
2999
+ CGCGAA
3000
+ CGCGAT
3001
+ CGCGAC
3002
+ CGCGAG
3003
+ CGCGTA
3004
+ CGCGTT
3005
+ CGCGTC
3006
+ CGCGTG
3007
+ CGCGCA
3008
+ CGCGCT
3009
+ CGCGCC
3010
+ CGCGCG
3011
+ CGCGGA
3012
+ CGCGGT
3013
+ CGCGGC
3014
+ CGCGGG
3015
+ CGGAAA
3016
+ CGGAAT
3017
+ CGGAAC
3018
+ CGGAAG
3019
+ CGGATA
3020
+ CGGATT
3021
+ CGGATC
3022
+ CGGATG
3023
+ CGGACA
3024
+ CGGACT
3025
+ CGGACC
3026
+ CGGACG
3027
+ CGGAGA
3028
+ CGGAGT
3029
+ CGGAGC
3030
+ CGGAGG
3031
+ CGGTAA
3032
+ CGGTAT
3033
+ CGGTAC
3034
+ CGGTAG
3035
+ CGGTTA
3036
+ CGGTTT
3037
+ CGGTTC
3038
+ CGGTTG
3039
+ CGGTCA
3040
+ CGGTCT
3041
+ CGGTCC
3042
+ CGGTCG
3043
+ CGGTGA
3044
+ CGGTGT
3045
+ CGGTGC
3046
+ CGGTGG
3047
+ CGGCAA
3048
+ CGGCAT
3049
+ CGGCAC
3050
+ CGGCAG
3051
+ CGGCTA
3052
+ CGGCTT
3053
+ CGGCTC
3054
+ CGGCTG
3055
+ CGGCCA
3056
+ CGGCCT
3057
+ CGGCCC
3058
+ CGGCCG
3059
+ CGGCGA
3060
+ CGGCGT
3061
+ CGGCGC
3062
+ CGGCGG
3063
+ CGGGAA
3064
+ CGGGAT
3065
+ CGGGAC
3066
+ CGGGAG
3067
+ CGGGTA
3068
+ CGGGTT
3069
+ CGGGTC
3070
+ CGGGTG
3071
+ CGGGCA
3072
+ CGGGCT
3073
+ CGGGCC
3074
+ CGGGCG
3075
+ CGGGGA
3076
+ CGGGGT
3077
+ CGGGGC
3078
+ CGGGGG
3079
+ GAAAAA
3080
+ GAAAAT
3081
+ GAAAAC
3082
+ GAAAAG
3083
+ GAAATA
3084
+ GAAATT
3085
+ GAAATC
3086
+ GAAATG
3087
+ GAAACA
3088
+ GAAACT
3089
+ GAAACC
3090
+ GAAACG
3091
+ GAAAGA
3092
+ GAAAGT
3093
+ GAAAGC
3094
+ GAAAGG
3095
+ GAATAA
3096
+ GAATAT
3097
+ GAATAC
3098
+ GAATAG
3099
+ GAATTA
3100
+ GAATTT
3101
+ GAATTC
3102
+ GAATTG
3103
+ GAATCA
3104
+ GAATCT
3105
+ GAATCC
3106
+ GAATCG
3107
+ GAATGA
3108
+ GAATGT
3109
+ GAATGC
3110
+ GAATGG
3111
+ GAACAA
3112
+ GAACAT
3113
+ GAACAC
3114
+ GAACAG
3115
+ GAACTA
3116
+ GAACTT
3117
+ GAACTC
3118
+ GAACTG
3119
+ GAACCA
3120
+ GAACCT
3121
+ GAACCC
3122
+ GAACCG
3123
+ GAACGA
3124
+ GAACGT
3125
+ GAACGC
3126
+ GAACGG
3127
+ GAAGAA
3128
+ GAAGAT
3129
+ GAAGAC
3130
+ GAAGAG
3131
+ GAAGTA
3132
+ GAAGTT
3133
+ GAAGTC
3134
+ GAAGTG
3135
+ GAAGCA
3136
+ GAAGCT
3137
+ GAAGCC
3138
+ GAAGCG
3139
+ GAAGGA
3140
+ GAAGGT
3141
+ GAAGGC
3142
+ GAAGGG
3143
+ GATAAA
3144
+ GATAAT
3145
+ GATAAC
3146
+ GATAAG
3147
+ GATATA
3148
+ GATATT
3149
+ GATATC
3150
+ GATATG
3151
+ GATACA
3152
+ GATACT
3153
+ GATACC
3154
+ GATACG
3155
+ GATAGA
3156
+ GATAGT
3157
+ GATAGC
3158
+ GATAGG
3159
+ GATTAA
3160
+ GATTAT
3161
+ GATTAC
3162
+ GATTAG
3163
+ GATTTA
3164
+ GATTTT
3165
+ GATTTC
3166
+ GATTTG
3167
+ GATTCA
3168
+ GATTCT
3169
+ GATTCC
3170
+ GATTCG
3171
+ GATTGA
3172
+ GATTGT
3173
+ GATTGC
3174
+ GATTGG
3175
+ GATCAA
3176
+ GATCAT
3177
+ GATCAC
3178
+ GATCAG
3179
+ GATCTA
3180
+ GATCTT
3181
+ GATCTC
3182
+ GATCTG
3183
+ GATCCA
3184
+ GATCCT
3185
+ GATCCC
3186
+ GATCCG
3187
+ GATCGA
3188
+ GATCGT
3189
+ GATCGC
3190
+ GATCGG
3191
+ GATGAA
3192
+ GATGAT
3193
+ GATGAC
3194
+ GATGAG
3195
+ GATGTA
3196
+ GATGTT
3197
+ GATGTC
3198
+ GATGTG
3199
+ GATGCA
3200
+ GATGCT
3201
+ GATGCC
3202
+ GATGCG
3203
+ GATGGA
3204
+ GATGGT
3205
+ GATGGC
3206
+ GATGGG
3207
+ GACAAA
3208
+ GACAAT
3209
+ GACAAC
3210
+ GACAAG
3211
+ GACATA
3212
+ GACATT
3213
+ GACATC
3214
+ GACATG
3215
+ GACACA
3216
+ GACACT
3217
+ GACACC
3218
+ GACACG
3219
+ GACAGA
3220
+ GACAGT
3221
+ GACAGC
3222
+ GACAGG
3223
+ GACTAA
3224
+ GACTAT
3225
+ GACTAC
3226
+ GACTAG
3227
+ GACTTA
3228
+ GACTTT
3229
+ GACTTC
3230
+ GACTTG
3231
+ GACTCA
3232
+ GACTCT
3233
+ GACTCC
3234
+ GACTCG
3235
+ GACTGA
3236
+ GACTGT
3237
+ GACTGC
3238
+ GACTGG
3239
+ GACCAA
3240
+ GACCAT
3241
+ GACCAC
3242
+ GACCAG
3243
+ GACCTA
3244
+ GACCTT
3245
+ GACCTC
3246
+ GACCTG
3247
+ GACCCA
3248
+ GACCCT
3249
+ GACCCC
3250
+ GACCCG
3251
+ GACCGA
3252
+ GACCGT
3253
+ GACCGC
3254
+ GACCGG
3255
+ GACGAA
3256
+ GACGAT
3257
+ GACGAC
3258
+ GACGAG
3259
+ GACGTA
3260
+ GACGTT
3261
+ GACGTC
3262
+ GACGTG
3263
+ GACGCA
3264
+ GACGCT
3265
+ GACGCC
3266
+ GACGCG
3267
+ GACGGA
3268
+ GACGGT
3269
+ GACGGC
3270
+ GACGGG
3271
+ GAGAAA
3272
+ GAGAAT
3273
+ GAGAAC
3274
+ GAGAAG
3275
+ GAGATA
3276
+ GAGATT
3277
+ GAGATC
3278
+ GAGATG
3279
+ GAGACA
3280
+ GAGACT
3281
+ GAGACC
3282
+ GAGACG
3283
+ GAGAGA
3284
+ GAGAGT
3285
+ GAGAGC
3286
+ GAGAGG
3287
+ GAGTAA
3288
+ GAGTAT
3289
+ GAGTAC
3290
+ GAGTAG
3291
+ GAGTTA
3292
+ GAGTTT
3293
+ GAGTTC
3294
+ GAGTTG
3295
+ GAGTCA
3296
+ GAGTCT
3297
+ GAGTCC
3298
+ GAGTCG
3299
+ GAGTGA
3300
+ GAGTGT
3301
+ GAGTGC
3302
+ GAGTGG
3303
+ GAGCAA
3304
+ GAGCAT
3305
+ GAGCAC
3306
+ GAGCAG
3307
+ GAGCTA
3308
+ GAGCTT
3309
+ GAGCTC
3310
+ GAGCTG
3311
+ GAGCCA
3312
+ GAGCCT
3313
+ GAGCCC
3314
+ GAGCCG
3315
+ GAGCGA
3316
+ GAGCGT
3317
+ GAGCGC
3318
+ GAGCGG
3319
+ GAGGAA
3320
+ GAGGAT
3321
+ GAGGAC
3322
+ GAGGAG
3323
+ GAGGTA
3324
+ GAGGTT
3325
+ GAGGTC
3326
+ GAGGTG
3327
+ GAGGCA
3328
+ GAGGCT
3329
+ GAGGCC
3330
+ GAGGCG
3331
+ GAGGGA
3332
+ GAGGGT
3333
+ GAGGGC
3334
+ GAGGGG
3335
+ GTAAAA
3336
+ GTAAAT
3337
+ GTAAAC
3338
+ GTAAAG
3339
+ GTAATA
3340
+ GTAATT
3341
+ GTAATC
3342
+ GTAATG
3343
+ GTAACA
3344
+ GTAACT
3345
+ GTAACC
3346
+ GTAACG
3347
+ GTAAGA
3348
+ GTAAGT
3349
+ GTAAGC
3350
+ GTAAGG
3351
+ GTATAA
3352
+ GTATAT
3353
+ GTATAC
3354
+ GTATAG
3355
+ GTATTA
3356
+ GTATTT
3357
+ GTATTC
3358
+ GTATTG
3359
+ GTATCA
3360
+ GTATCT
3361
+ GTATCC
3362
+ GTATCG
3363
+ GTATGA
3364
+ GTATGT
3365
+ GTATGC
3366
+ GTATGG
3367
+ GTACAA
3368
+ GTACAT
3369
+ GTACAC
3370
+ GTACAG
3371
+ GTACTA
3372
+ GTACTT
3373
+ GTACTC
3374
+ GTACTG
3375
+ GTACCA
3376
+ GTACCT
3377
+ GTACCC
3378
+ GTACCG
3379
+ GTACGA
3380
+ GTACGT
3381
+ GTACGC
3382
+ GTACGG
3383
+ GTAGAA
3384
+ GTAGAT
3385
+ GTAGAC
3386
+ GTAGAG
3387
+ GTAGTA
3388
+ GTAGTT
3389
+ GTAGTC
3390
+ GTAGTG
3391
+ GTAGCA
3392
+ GTAGCT
3393
+ GTAGCC
3394
+ GTAGCG
3395
+ GTAGGA
3396
+ GTAGGT
3397
+ GTAGGC
3398
+ GTAGGG
3399
+ GTTAAA
3400
+ GTTAAT
3401
+ GTTAAC
3402
+ GTTAAG
3403
+ GTTATA
3404
+ GTTATT
3405
+ GTTATC
3406
+ GTTATG
3407
+ GTTACA
3408
+ GTTACT
3409
+ GTTACC
3410
+ GTTACG
3411
+ GTTAGA
3412
+ GTTAGT
3413
+ GTTAGC
3414
+ GTTAGG
3415
+ GTTTAA
3416
+ GTTTAT
3417
+ GTTTAC
3418
+ GTTTAG
3419
+ GTTTTA
3420
+ GTTTTT
3421
+ GTTTTC
3422
+ GTTTTG
3423
+ GTTTCA
3424
+ GTTTCT
3425
+ GTTTCC
3426
+ GTTTCG
3427
+ GTTTGA
3428
+ GTTTGT
3429
+ GTTTGC
3430
+ GTTTGG
3431
+ GTTCAA
3432
+ GTTCAT
3433
+ GTTCAC
3434
+ GTTCAG
3435
+ GTTCTA
3436
+ GTTCTT
3437
+ GTTCTC
3438
+ GTTCTG
3439
+ GTTCCA
3440
+ GTTCCT
3441
+ GTTCCC
3442
+ GTTCCG
3443
+ GTTCGA
3444
+ GTTCGT
3445
+ GTTCGC
3446
+ GTTCGG
3447
+ GTTGAA
3448
+ GTTGAT
3449
+ GTTGAC
3450
+ GTTGAG
3451
+ GTTGTA
3452
+ GTTGTT
3453
+ GTTGTC
3454
+ GTTGTG
3455
+ GTTGCA
3456
+ GTTGCT
3457
+ GTTGCC
3458
+ GTTGCG
3459
+ GTTGGA
3460
+ GTTGGT
3461
+ GTTGGC
3462
+ GTTGGG
3463
+ GTCAAA
3464
+ GTCAAT
3465
+ GTCAAC
3466
+ GTCAAG
3467
+ GTCATA
3468
+ GTCATT
3469
+ GTCATC
3470
+ GTCATG
3471
+ GTCACA
3472
+ GTCACT
3473
+ GTCACC
3474
+ GTCACG
3475
+ GTCAGA
3476
+ GTCAGT
3477
+ GTCAGC
3478
+ GTCAGG
3479
+ GTCTAA
3480
+ GTCTAT
3481
+ GTCTAC
3482
+ GTCTAG
3483
+ GTCTTA
3484
+ GTCTTT
3485
+ GTCTTC
3486
+ GTCTTG
3487
+ GTCTCA
3488
+ GTCTCT
3489
+ GTCTCC
3490
+ GTCTCG
3491
+ GTCTGA
3492
+ GTCTGT
3493
+ GTCTGC
3494
+ GTCTGG
3495
+ GTCCAA
3496
+ GTCCAT
3497
+ GTCCAC
3498
+ GTCCAG
3499
+ GTCCTA
3500
+ GTCCTT
3501
+ GTCCTC
3502
+ GTCCTG
3503
+ GTCCCA
3504
+ GTCCCT
3505
+ GTCCCC
3506
+ GTCCCG
3507
+ GTCCGA
3508
+ GTCCGT
3509
+ GTCCGC
3510
+ GTCCGG
3511
+ GTCGAA
3512
+ GTCGAT
3513
+ GTCGAC
3514
+ GTCGAG
3515
+ GTCGTA
3516
+ GTCGTT
3517
+ GTCGTC
3518
+ GTCGTG
3519
+ GTCGCA
3520
+ GTCGCT
3521
+ GTCGCC
3522
+ GTCGCG
3523
+ GTCGGA
3524
+ GTCGGT
3525
+ GTCGGC
3526
+ GTCGGG
3527
+ GTGAAA
3528
+ GTGAAT
3529
+ GTGAAC
3530
+ GTGAAG
3531
+ GTGATA
3532
+ GTGATT
3533
+ GTGATC
3534
+ GTGATG
3535
+ GTGACA
3536
+ GTGACT
3537
+ GTGACC
3538
+ GTGACG
3539
+ GTGAGA
3540
+ GTGAGT
3541
+ GTGAGC
3542
+ GTGAGG
3543
+ GTGTAA
3544
+ GTGTAT
3545
+ GTGTAC
3546
+ GTGTAG
3547
+ GTGTTA
3548
+ GTGTTT
3549
+ GTGTTC
3550
+ GTGTTG
3551
+ GTGTCA
3552
+ GTGTCT
3553
+ GTGTCC
3554
+ GTGTCG
3555
+ GTGTGA
3556
+ GTGTGT
3557
+ GTGTGC
3558
+ GTGTGG
3559
+ GTGCAA
3560
+ GTGCAT
3561
+ GTGCAC
3562
+ GTGCAG
3563
+ GTGCTA
3564
+ GTGCTT
3565
+ GTGCTC
3566
+ GTGCTG
3567
+ GTGCCA
3568
+ GTGCCT
3569
+ GTGCCC
3570
+ GTGCCG
3571
+ GTGCGA
3572
+ GTGCGT
3573
+ GTGCGC
3574
+ GTGCGG
3575
+ GTGGAA
3576
+ GTGGAT
3577
+ GTGGAC
3578
+ GTGGAG
3579
+ GTGGTA
3580
+ GTGGTT
3581
+ GTGGTC
3582
+ GTGGTG
3583
+ GTGGCA
3584
+ GTGGCT
3585
+ GTGGCC
3586
+ GTGGCG
3587
+ GTGGGA
3588
+ GTGGGT
3589
+ GTGGGC
3590
+ GTGGGG
3591
+ GCAAAA
3592
+ GCAAAT
3593
+ GCAAAC
3594
+ GCAAAG
3595
+ GCAATA
3596
+ GCAATT
3597
+ GCAATC
3598
+ GCAATG
3599
+ GCAACA
3600
+ GCAACT
3601
+ GCAACC
3602
+ GCAACG
3603
+ GCAAGA
3604
+ GCAAGT
3605
+ GCAAGC
3606
+ GCAAGG
3607
+ GCATAA
3608
+ GCATAT
3609
+ GCATAC
3610
+ GCATAG
3611
+ GCATTA
3612
+ GCATTT
3613
+ GCATTC
3614
+ GCATTG
3615
+ GCATCA
3616
+ GCATCT
3617
+ GCATCC
3618
+ GCATCG
3619
+ GCATGA
3620
+ GCATGT
3621
+ GCATGC
3622
+ GCATGG
3623
+ GCACAA
3624
+ GCACAT
3625
+ GCACAC
3626
+ GCACAG
3627
+ GCACTA
3628
+ GCACTT
3629
+ GCACTC
3630
+ GCACTG
3631
+ GCACCA
3632
+ GCACCT
3633
+ GCACCC
3634
+ GCACCG
3635
+ GCACGA
3636
+ GCACGT
3637
+ GCACGC
3638
+ GCACGG
3639
+ GCAGAA
3640
+ GCAGAT
3641
+ GCAGAC
3642
+ GCAGAG
3643
+ GCAGTA
3644
+ GCAGTT
3645
+ GCAGTC
3646
+ GCAGTG
3647
+ GCAGCA
3648
+ GCAGCT
3649
+ GCAGCC
3650
+ GCAGCG
3651
+ GCAGGA
3652
+ GCAGGT
3653
+ GCAGGC
3654
+ GCAGGG
3655
+ GCTAAA
3656
+ GCTAAT
3657
+ GCTAAC
3658
+ GCTAAG
3659
+ GCTATA
3660
+ GCTATT
3661
+ GCTATC
3662
+ GCTATG
3663
+ GCTACA
3664
+ GCTACT
3665
+ GCTACC
3666
+ GCTACG
3667
+ GCTAGA
3668
+ GCTAGT
3669
+ GCTAGC
3670
+ GCTAGG
3671
+ GCTTAA
3672
+ GCTTAT
3673
+ GCTTAC
3674
+ GCTTAG
3675
+ GCTTTA
3676
+ GCTTTT
3677
+ GCTTTC
3678
+ GCTTTG
3679
+ GCTTCA
3680
+ GCTTCT
3681
+ GCTTCC
3682
+ GCTTCG
3683
+ GCTTGA
3684
+ GCTTGT
3685
+ GCTTGC
3686
+ GCTTGG
3687
+ GCTCAA
3688
+ GCTCAT
3689
+ GCTCAC
3690
+ GCTCAG
3691
+ GCTCTA
3692
+ GCTCTT
3693
+ GCTCTC
3694
+ GCTCTG
3695
+ GCTCCA
3696
+ GCTCCT
3697
+ GCTCCC
3698
+ GCTCCG
3699
+ GCTCGA
3700
+ GCTCGT
3701
+ GCTCGC
3702
+ GCTCGG
3703
+ GCTGAA
3704
+ GCTGAT
3705
+ GCTGAC
3706
+ GCTGAG
3707
+ GCTGTA
3708
+ GCTGTT
3709
+ GCTGTC
3710
+ GCTGTG
3711
+ GCTGCA
3712
+ GCTGCT
3713
+ GCTGCC
3714
+ GCTGCG
3715
+ GCTGGA
3716
+ GCTGGT
3717
+ GCTGGC
3718
+ GCTGGG
3719
+ GCCAAA
3720
+ GCCAAT
3721
+ GCCAAC
3722
+ GCCAAG
3723
+ GCCATA
3724
+ GCCATT
3725
+ GCCATC
3726
+ GCCATG
3727
+ GCCACA
3728
+ GCCACT
3729
+ GCCACC
3730
+ GCCACG
3731
+ GCCAGA
3732
+ GCCAGT
3733
+ GCCAGC
3734
+ GCCAGG
3735
+ GCCTAA
3736
+ GCCTAT
3737
+ GCCTAC
3738
+ GCCTAG
3739
+ GCCTTA
3740
+ GCCTTT
3741
+ GCCTTC
3742
+ GCCTTG
3743
+ GCCTCA
3744
+ GCCTCT
3745
+ GCCTCC
3746
+ GCCTCG
3747
+ GCCTGA
3748
+ GCCTGT
3749
+ GCCTGC
3750
+ GCCTGG
3751
+ GCCCAA
3752
+ GCCCAT
3753
+ GCCCAC
3754
+ GCCCAG
3755
+ GCCCTA
3756
+ GCCCTT
3757
+ GCCCTC
3758
+ GCCCTG
3759
+ GCCCCA
3760
+ GCCCCT
3761
+ GCCCCC
3762
+ GCCCCG
3763
+ GCCCGA
3764
+ GCCCGT
3765
+ GCCCGC
3766
+ GCCCGG
3767
+ GCCGAA
3768
+ GCCGAT
3769
+ GCCGAC
3770
+ GCCGAG
3771
+ GCCGTA
3772
+ GCCGTT
3773
+ GCCGTC
3774
+ GCCGTG
3775
+ GCCGCA
3776
+ GCCGCT
3777
+ GCCGCC
3778
+ GCCGCG
3779
+ GCCGGA
3780
+ GCCGGT
3781
+ GCCGGC
3782
+ GCCGGG
3783
+ GCGAAA
3784
+ GCGAAT
3785
+ GCGAAC
3786
+ GCGAAG
3787
+ GCGATA
3788
+ GCGATT
3789
+ GCGATC
3790
+ GCGATG
3791
+ GCGACA
3792
+ GCGACT
3793
+ GCGACC
3794
+ GCGACG
3795
+ GCGAGA
3796
+ GCGAGT
3797
+ GCGAGC
3798
+ GCGAGG
3799
+ GCGTAA
3800
+ GCGTAT
3801
+ GCGTAC
3802
+ GCGTAG
3803
+ GCGTTA
3804
+ GCGTTT
3805
+ GCGTTC
3806
+ GCGTTG
3807
+ GCGTCA
3808
+ GCGTCT
3809
+ GCGTCC
3810
+ GCGTCG
3811
+ GCGTGA
3812
+ GCGTGT
3813
+ GCGTGC
3814
+ GCGTGG
3815
+ GCGCAA
3816
+ GCGCAT
3817
+ GCGCAC
3818
+ GCGCAG
3819
+ GCGCTA
3820
+ GCGCTT
3821
+ GCGCTC
3822
+ GCGCTG
3823
+ GCGCCA
3824
+ GCGCCT
3825
+ GCGCCC
3826
+ GCGCCG
3827
+ GCGCGA
3828
+ GCGCGT
3829
+ GCGCGC
3830
+ GCGCGG
3831
+ GCGGAA
3832
+ GCGGAT
3833
+ GCGGAC
3834
+ GCGGAG
3835
+ GCGGTA
3836
+ GCGGTT
3837
+ GCGGTC
3838
+ GCGGTG
3839
+ GCGGCA
3840
+ GCGGCT
3841
+ GCGGCC
3842
+ GCGGCG
3843
+ GCGGGA
3844
+ GCGGGT
3845
+ GCGGGC
3846
+ GCGGGG
3847
+ GGAAAA
3848
+ GGAAAT
3849
+ GGAAAC
3850
+ GGAAAG
3851
+ GGAATA
3852
+ GGAATT
3853
+ GGAATC
3854
+ GGAATG
3855
+ GGAACA
3856
+ GGAACT
3857
+ GGAACC
3858
+ GGAACG
3859
+ GGAAGA
3860
+ GGAAGT
3861
+ GGAAGC
3862
+ GGAAGG
3863
+ GGATAA
3864
+ GGATAT
3865
+ GGATAC
3866
+ GGATAG
3867
+ GGATTA
3868
+ GGATTT
3869
+ GGATTC
3870
+ GGATTG
3871
+ GGATCA
3872
+ GGATCT
3873
+ GGATCC
3874
+ GGATCG
3875
+ GGATGA
3876
+ GGATGT
3877
+ GGATGC
3878
+ GGATGG
3879
+ GGACAA
3880
+ GGACAT
3881
+ GGACAC
3882
+ GGACAG
3883
+ GGACTA
3884
+ GGACTT
3885
+ GGACTC
3886
+ GGACTG
3887
+ GGACCA
3888
+ GGACCT
3889
+ GGACCC
3890
+ GGACCG
3891
+ GGACGA
3892
+ GGACGT
3893
+ GGACGC
3894
+ GGACGG
3895
+ GGAGAA
3896
+ GGAGAT
3897
+ GGAGAC
3898
+ GGAGAG
3899
+ GGAGTA
3900
+ GGAGTT
3901
+ GGAGTC
3902
+ GGAGTG
3903
+ GGAGCA
3904
+ GGAGCT
3905
+ GGAGCC
3906
+ GGAGCG
3907
+ GGAGGA
3908
+ GGAGGT
3909
+ GGAGGC
3910
+ GGAGGG
3911
+ GGTAAA
3912
+ GGTAAT
3913
+ GGTAAC
3914
+ GGTAAG
3915
+ GGTATA
3916
+ GGTATT
3917
+ GGTATC
3918
+ GGTATG
3919
+ GGTACA
3920
+ GGTACT
3921
+ GGTACC
3922
+ GGTACG
3923
+ GGTAGA
3924
+ GGTAGT
3925
+ GGTAGC
3926
+ GGTAGG
3927
+ GGTTAA
3928
+ GGTTAT
3929
+ GGTTAC
3930
+ GGTTAG
3931
+ GGTTTA
3932
+ GGTTTT
3933
+ GGTTTC
3934
+ GGTTTG
3935
+ GGTTCA
3936
+ GGTTCT
3937
+ GGTTCC
3938
+ GGTTCG
3939
+ GGTTGA
3940
+ GGTTGT
3941
+ GGTTGC
3942
+ GGTTGG
3943
+ GGTCAA
3944
+ GGTCAT
3945
+ GGTCAC
3946
+ GGTCAG
3947
+ GGTCTA
3948
+ GGTCTT
3949
+ GGTCTC
3950
+ GGTCTG
3951
+ GGTCCA
3952
+ GGTCCT
3953
+ GGTCCC
3954
+ GGTCCG
3955
+ GGTCGA
3956
+ GGTCGT
3957
+ GGTCGC
3958
+ GGTCGG
3959
+ GGTGAA
3960
+ GGTGAT
3961
+ GGTGAC
3962
+ GGTGAG
3963
+ GGTGTA
3964
+ GGTGTT
3965
+ GGTGTC
3966
+ GGTGTG
3967
+ GGTGCA
3968
+ GGTGCT
3969
+ GGTGCC
3970
+ GGTGCG
3971
+ GGTGGA
3972
+ GGTGGT
3973
+ GGTGGC
3974
+ GGTGGG
3975
+ GGCAAA
3976
+ GGCAAT
3977
+ GGCAAC
3978
+ GGCAAG
3979
+ GGCATA
3980
+ GGCATT
3981
+ GGCATC
3982
+ GGCATG
3983
+ GGCACA
3984
+ GGCACT
3985
+ GGCACC
3986
+ GGCACG
3987
+ GGCAGA
3988
+ GGCAGT
3989
+ GGCAGC
3990
+ GGCAGG
3991
+ GGCTAA
3992
+ GGCTAT
3993
+ GGCTAC
3994
+ GGCTAG
3995
+ GGCTTA
3996
+ GGCTTT
3997
+ GGCTTC
3998
+ GGCTTG
3999
+ GGCTCA
4000
+ GGCTCT
4001
+ GGCTCC
4002
+ GGCTCG
4003
+ GGCTGA
4004
+ GGCTGT
4005
+ GGCTGC
4006
+ GGCTGG
4007
+ GGCCAA
4008
+ GGCCAT
4009
+ GGCCAC
4010
+ GGCCAG
4011
+ GGCCTA
4012
+ GGCCTT
4013
+ GGCCTC
4014
+ GGCCTG
4015
+ GGCCCA
4016
+ GGCCCT
4017
+ GGCCCC
4018
+ GGCCCG
4019
+ GGCCGA
4020
+ GGCCGT
4021
+ GGCCGC
4022
+ GGCCGG
4023
+ GGCGAA
4024
+ GGCGAT
4025
+ GGCGAC
4026
+ GGCGAG
4027
+ GGCGTA
4028
+ GGCGTT
4029
+ GGCGTC
4030
+ GGCGTG
4031
+ GGCGCA
4032
+ GGCGCT
4033
+ GGCGCC
4034
+ GGCGCG
4035
+ GGCGGA
4036
+ GGCGGT
4037
+ GGCGGC
4038
+ GGCGGG
4039
+ GGGAAA
4040
+ GGGAAT
4041
+ GGGAAC
4042
+ GGGAAG
4043
+ GGGATA
4044
+ GGGATT
4045
+ GGGATC
4046
+ GGGATG
4047
+ GGGACA
4048
+ GGGACT
4049
+ GGGACC
4050
+ GGGACG
4051
+ GGGAGA
4052
+ GGGAGT
4053
+ GGGAGC
4054
+ GGGAGG
4055
+ GGGTAA
4056
+ GGGTAT
4057
+ GGGTAC
4058
+ GGGTAG
4059
+ GGGTTA
4060
+ GGGTTT
4061
+ GGGTTC
4062
+ GGGTTG
4063
+ GGGTCA
4064
+ GGGTCT
4065
+ GGGTCC
4066
+ GGGTCG
4067
+ GGGTGA
4068
+ GGGTGT
4069
+ GGGTGC
4070
+ GGGTGG
4071
+ GGGCAA
4072
+ GGGCAT
4073
+ GGGCAC
4074
+ GGGCAG
4075
+ GGGCTA
4076
+ GGGCTT
4077
+ GGGCTC
4078
+ GGGCTG
4079
+ GGGCCA
4080
+ GGGCCT
4081
+ GGGCCC
4082
+ GGGCCG
4083
+ GGGCGA
4084
+ GGGCGT
4085
+ GGGCGC
4086
+ GGGCGG
4087
+ GGGGAA
4088
+ GGGGAT
4089
+ GGGGAC
4090
+ GGGGAG
4091
+ GGGGTA
4092
+ GGGGTT
4093
+ GGGGTC
4094
+ GGGGTG
4095
+ GGGGCA
4096
+ GGGGCT
4097
+ GGGGCC
4098
+ GGGGCG
4099
+ GGGGGA
4100
+ GGGGGT
4101
+ GGGGGC
4102
+ GGGGGG
4103
+ A
4104
+ T
4105
+ C
4106
+ G
4107
+ N