






from datetime import datetime as dt
import os

from github import Github


UpperCAmelCase__				   =		[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]
def 						_a    (      )							->			Tuple:
 a		  =      Github(os.environ['''GITHUB_TOKEN'''] )
 a		  =      g.get_repo('''huggingface/transformers''' )
 a		  =      repo.get_issues(state='''open''' )

 for issue in open_issues:
  a		  =      sorted([comment for comment in issue.get_comments()]	,							key=lambda a : i.created_at	,							reverse=a )
  a		  =      comments[0] if len(a ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state='''closed''' )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       '''This issue has been automatically marked as stale because it has not had '''
       '''recent activity. If you think this still needs to be addressed '''
       '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
       '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
       '''are likely to be ignored.''' )


if __name__ == "__main__":
     main()






'''simple docstring'''







import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
   from transformers.models.esm.modeling_esm import (
       ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
       EsmEmbeddings,
       create_position_ids_from_input_ids,
   )







class A__       :






      def __init__( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1_3     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7     ,						SCREAMING_SNAKE_CASE			:Tuple=False     ,						SCREAMING_SNAKE_CASE			:Dict=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=False     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=True     ,						SCREAMING_SNAKE_CASE			:List[str]=3_3     ,						SCREAMING_SNAKE_CASE			:Tuple=3_2     ,						SCREAMING_SNAKE_CASE			:Tuple=5     ,						SCREAMING_SNAKE_CASE			:int=4     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=3_7     ,						SCREAMING_SNAKE_CASE			:List[str]="gelu"     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:Tuple=0.1     ,						SCREAMING_SNAKE_CASE			:str=5_1_2     ,						SCREAMING_SNAKE_CASE			:Dict=1_6     ,						SCREAMING_SNAKE_CASE			:Dict=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:str=3     ,						SCREAMING_SNAKE_CASE			:List[str]=4     ,						SCREAMING_SNAKE_CASE			:List[str]=None     ,						)       ->					Union[str, Any]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=parent
             _a    : List[Any]     					=batch_size
             _a    : Optional[int]     					=seq_length
             _a    : Union[str, Any]     					=is_training
             _a    : List[Any]     					=use_input_mask
             _a    : Optional[int]     					=use_token_type_ids
             _a    : int     					=use_labels
             _a    : List[str]     					=vocab_size
             _a    : List[Any]     					=hidden_size
             _a    : int     					=num_hidden_layers
             _a    : Tuple     					=num_attention_heads
             _a    : Any     					=intermediate_size
             _a    : str     					=hidden_act
             _a    : Union[str, Any]     					=hidden_dropout_prob
             _a    : Union[str, Any]     					=attention_probs_dropout_prob
             _a    : str     					=max_position_embeddings
             _a    : Dict     					=type_vocab_size
             _a    : Tuple     					=type_sequence_label_size
             _a    : Dict     					=initializer_range
             _a    : List[str]     					=num_labels
             _a    : Tuple     					=num_choices
             _a    : int     					=scope






      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					str:




             '''simple docstring'''
             _a    : Optional[int]     					=ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size	)

             _a    : List[Any]     					=None
             if self.use_input_mask:
                    _a    : Any     					=random_attention_mask([self.batch_size, self.seq_length]	)

             _a    : Optional[int]     					=None
             _a    : str     					=None
             _a    : Dict     					=None
             if self.use_labels:
                    _a    : Dict     					=ids_tensor([self.batch_size]     ,						self.type_sequence_label_size	)
                    _a    : str     					=ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels	)
                    _a    : List[str]     					=ids_tensor([self.batch_size]     ,						self.num_choices	)

             _a    : List[Any]     					=self.get_config()

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def __UpperCAmelCase       ( self			:str	)       ->					Optional[int]:




             '''simple docstring'''
             return EsmConfig(
                 vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						pad_token_id=1     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						initializer_range=self.initializer_range     ,						)






      def __UpperCAmelCase       ( self			:List[str]     ,						SCREAMING_SNAKE_CASE			:Tuple     ,						SCREAMING_SNAKE_CASE			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:Any     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:int	)       ->					Tuple:




             '''simple docstring'''
             _a    : Any     					=EsmModel(config=SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.eval()
             _a    : Optional[Any]     					=model(SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=model(SCREAMING_SNAKE_CASE	)
             _a    : str     					=model(SCREAMING_SNAKE_CASE	)

             self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)	)
             self.parent.assertEqual(result.pooler_output.shape     ,						(self.batch_size, self.hidden_size)	)






      def __UpperCAmelCase       ( self			:str     ,						SCREAMING_SNAKE_CASE			:Any     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Optional[Any]	)       ->					Dict:




             '''simple docstring'''
             _a    : str     					=EsmForMaskedLM(config=SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.eval()
             _a    : Union[str, Any]     					=model(SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						labels=SCREAMING_SNAKE_CASE	)
             self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)	)






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:Tuple     ,						SCREAMING_SNAKE_CASE			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:List[Any]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]	)       ->					Optional[Any]:




             '''simple docstring'''
             _a    : int     					=self.num_labels
             _a    : Tuple     					=EsmForTokenClassification(config=SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.eval()
             _a    : Tuple     					=model(SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						labels=SCREAMING_SNAKE_CASE	)
             self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)	)






      def __UpperCAmelCase       ( self			:Dict	)       ->					List[str]:




             '''simple docstring'''
             _a    : Optional[Any]     					=self.prepare_config_and_inputs()
             (
                 (
                 _a
             )					,    (
                 _a
             )					,    (
                 _a
             )					,    (
                 _a
             )					,    (
                 _a
             )					,    (
                 _a
             )					,    
             )    : Any     					=config_and_inputs
             _a    : List[Any]     					={"""input_ids""": input_ids, """attention_mask""": input_mask}
             return config, inputs_dict







@require_torch
class A__       (						UpperCAmelCase__ ,     UpperCAmelCase__ ,     unittest.TestCase      ):
      __UpperCamelCase       :  Any	       =							False

      __UpperCamelCase       :  Any	       =							(
          (
              EsmForMaskedLM,
              EsmModel,
              EsmForSequenceClassification,
              EsmForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase       :  str	       =							()
      __UpperCamelCase       :  List[str]	       =							(
          {
              "feature-extraction": EsmModel,
              "fill-mask": EsmForMaskedLM,
              "text-classification": EsmForSequenceClassification,
              "token-classification": EsmForTokenClassification,
              "zero-shot": EsmForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase       :  Union[str, Any]	       =							True






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					int:




             '''simple docstring'''
             _a    : Dict     					=EsmModelTester(self	)
             _a    : Optional[Any]     					=ConfigTester(self     ,						config_class=SCREAMING_SNAKE_CASE     ,						hidden_size=3_7	)






      def __UpperCAmelCase       ( self			:Tuple	)       ->					List[Any]:




             '''simple docstring'''
             self.config_tester.run_common_tests()






      def __UpperCAmelCase       ( self			:Optional[int]	)       ->					str:




             '''simple docstring'''
             _a    : List[Any]     					=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:List[Any]	)       ->					Dict:




             '''simple docstring'''
             _a    : List[str]     					=self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _a    : Dict     					=type
                    self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Dict	)       ->					List[str]:




             '''simple docstring'''
             _a    : Tuple     					=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:List[Any]	)       ->					List[str]:




             '''simple docstring'''
             _a    : str     					=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE	)






      @slow
      def __UpperCAmelCase       ( self			:str	)       ->					Dict:




             '''simple docstring'''
             for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _a    : Union[str, Any]     					=EsmModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Tuple	)       ->					int:




             '''simple docstring'''
             _a    : Optional[Any]     					=self.model_tester.prepare_config_and_inputs()[0]
             _a    : Dict     					=EsmEmbeddings(config=SCREAMING_SNAKE_CASE	)

             _a    : Tuple     					=torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]]	)
             _a    : Optional[Any]     					=torch.as_tensor(
                 [
                     [
                         0 + model.padding_idx + 1,
                         1 + model.padding_idx + 1,
                         2 + model.padding_idx + 1,
                         model.padding_idx,
                     ]
                 ]	)
             _a    : Any     					=create_position_ids_from_input_ids(SCREAMING_SNAKE_CASE     ,						model.padding_idx	)
             self.assertEqual(position_ids.shape     ,						expected_positions.shape	)
             self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)	)	)






      def __UpperCAmelCase       ( self			:Optional[Any]	)       ->					Tuple:




             '''simple docstring'''
             _a    : List[Any]     					=self.model_tester.prepare_config_and_inputs()[0]
             _a    : Optional[int]     					=EsmEmbeddings(config=SCREAMING_SNAKE_CASE	)

             _a    : Tuple     					=torch.empty(2     ,						4     ,						3_0	)
             _a    : str     					=[
                 0 + embeddings.padding_idx + 1,
                 1 + embeddings.padding_idx + 1,
                 2 + embeddings.padding_idx + 1,
                 3 + embeddings.padding_idx + 1,
             ]
             _a    : int     					=torch.as_tensor([expected_single_positions, expected_single_positions]	)
             _a    : Any     					=embeddings.create_position_ids_from_inputs_embeds(SCREAMING_SNAKE_CASE	)
             self.assertEqual(position_ids.shape     ,						expected_positions.shape	)
             self.assertTrue(torch.all(torch.eq(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)	)	)






      @unittest.skip("""Esm does not support embedding resizing"""	)
      def __UpperCAmelCase       ( self			:Tuple	)       ->					List[str]:




             '''simple docstring'''
             pass






      @unittest.skip("""Esm does not support embedding resizing"""	)
      def __UpperCAmelCase       ( self			:str	)       ->					Any:




             '''simple docstring'''
             pass






      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
      def __UpperCAmelCase       ( self			:Dict	)       ->					Any:




             '''simple docstring'''
             pass







@require_torch
class A__       (						UpperCAmelCase__      ):






      @slow
      def __UpperCAmelCase       ( self			:List[Any]	)       ->					str:




             '''simple docstring'''
             with torch.no_grad():
                    _a    : Optional[int]     					=EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""	)
                    model.eval()
                    _a    : Any     					=torch.tensor([[0, 1, 2, 3, 4, 5]]	)
                    _a    : Tuple     					=model(SCREAMING_SNAKE_CASE	)[0]

                    _a    : int     					=3_3

                    _a    : Tuple     					=torch.Size((1, 6, vocab_size)	)
                    self.assertEqual(output.shape     ,						SCREAMING_SNAKE_CASE	)

                    _a    : Union[str, Any]     					=torch.tensor(
                        [[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]]	)
                    self.assertTrue(torch.allclose(output[:, :3, :3]     ,						SCREAMING_SNAKE_CASE     ,						atol=1e-4	)	)






      @slow
      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					Tuple:




             '''simple docstring'''
             with torch.no_grad():
                    _a    : Any     					=EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""	)
                    model.eval()

                    _a    : Any     					=torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]	)
                    _a    : int     					=model(SCREAMING_SNAKE_CASE	)[0]
                    # compare the actual values for a slice.
                    _a    : str     					=torch.tensor(
                        [[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]]	)
                    self.assertTrue(torch.allclose(output[:, :3, :3]     ,						SCREAMING_SNAKE_CASE     ,						atol=1e-4	)	)


