code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
    import torch
  
    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
    )
    from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=7							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_=99							,lowerCamelCase_=32							,lowerCamelCase_=5							,lowerCamelCase_=4							,lowerCamelCase_=37							,lowerCamelCase_="gelu"							,lowerCamelCase_=0.1							,lowerCamelCase_=0.1							,lowerCamelCase_=512							,lowerCamelCase_=16							,lowerCamelCase_=2							,lowerCamelCase_=0.0_2							,lowerCamelCase_=3							,lowerCamelCase_=4							,lowerCamelCase_=None							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					parent
            _UpperCAmelCase   	=					batch_size
            _UpperCAmelCase   	=					seq_length
            _UpperCAmelCase   	=					is_training
            _UpperCAmelCase   	=					use_input_mask
            _UpperCAmelCase   	=					use_token_type_ids
            _UpperCAmelCase   	=					use_labels
            _UpperCAmelCase   	=					vocab_size
            _UpperCAmelCase   	=					hidden_size
            _UpperCAmelCase   	=					num_hidden_layers
            _UpperCAmelCase   	=					num_attention_heads
            _UpperCAmelCase   	=					intermediate_size
            _UpperCAmelCase   	=					hidden_act
            _UpperCAmelCase   	=					hidden_dropout_prob
            _UpperCAmelCase   	=					attention_probs_dropout_prob
            _UpperCAmelCase   	=					max_position_embeddings
            _UpperCAmelCase   	=					type_vocab_size
            _UpperCAmelCase   	=					type_sequence_label_size
            _UpperCAmelCase   	=					initializer_range
            _UpperCAmelCase   	=					num_labels
            _UpperCAmelCase   	=					num_choices
            _UpperCAmelCase   	=					scope
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size		)
  
            _UpperCAmelCase   	=					None
            if self.use_input_mask:
               _UpperCAmelCase   	=					random_attention_mask([self.batch_size, self.seq_length]		)
  
            _UpperCAmelCase   	=					None
            if self.use_token_type_ids:
               _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,self.type_vocab_size		)
  
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            if self.use_labels:
               _UpperCAmelCase   	=					ids_tensor([self.batch_size]							,self.type_sequence_label_size		)
               _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,self.num_labels		)
               _UpperCAmelCase   	=					ids_tensor([self.batch_size]							,self.num_choices		)
               _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,1		)
  
            _UpperCAmelCase   	=					self.get_config()
  
            return (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                fake_token_labels,
            )
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return ElectraConfig(
                vocab_size=self.vocab_size							,hidden_size=self.hidden_size							,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							,is_decoder=lowerCamelCase_							,initializer_range=self.initializer_range							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            (
                (
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,														
            )   	=					self.prepare_config_and_inputs()
  
            _UpperCAmelCase   	=					True
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.seq_length]							,vocab_size=2		)
  
            return (
                config,
                input_ids,
                token_type_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraModel(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_		)
            _UpperCAmelCase   	=					model(lowerCamelCase_							,token_type_ids=lowerCamelCase_		)
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
            self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					True
            _UpperCAmelCase   	=					ElectraModel(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(
                lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,encoder_hidden_states=lowerCamelCase_							,encoder_attention_mask=lowerCamelCase_							,)
            _UpperCAmelCase   	=					model(
                lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,encoder_hidden_states=lowerCamelCase_							,)
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_		)
            self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraForMaskedLM(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraForCausalLM(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.num_labels
            _UpperCAmelCase   	=					ElectraForTokenClassification(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.num_labels)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.num_labels
            _UpperCAmelCase   	=					ElectraForPreTraining(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.num_labels
            _UpperCAmelCase   	=					ElectraForSequenceClassification(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraForQuestionAnswering(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(
                lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,start_positions=lowerCamelCase_							,end_positions=lowerCamelCase_							,)
            self.parent.assertEqual(result.start_logits.shape							,(self.batch_size, self.seq_length)		)
            self.parent.assertEqual(result.end_logits.shape							,(self.batch_size, self.seq_length)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.num_choices
            _UpperCAmelCase   	=					ElectraForMultipleChoice(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					input_ids.unsqueeze(1		).expand(-1							,self.num_choices							,-1		).contiguous()
            _UpperCAmelCase   	=					token_type_ids.unsqueeze(1		).expand(-1							,self.num_choices							,-1		).contiguous()
            _UpperCAmelCase   	=					input_mask.unsqueeze(1		).expand(-1							,self.num_choices							,-1		).contiguous()
            _UpperCAmelCase   	=					model(
                lowerCamelCase_							,attention_mask=lowerCamelCase_							,token_type_ids=lowerCamelCase_							,labels=lowerCamelCase_							,)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_choices)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.prepare_config_and_inputs()
            (
                (
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,							(
                _UpperCAmelCase							
            )	,														
            )   	=					config_and_inputs
            _UpperCAmelCase   	=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
         _lowerCamelCase							:							Optional[int]       =			(
             (
                 ElectraModel,
                 ElectraForPreTraining,
                 ElectraForMaskedLM,
                 ElectraForCausalLM,
                 ElectraForMultipleChoice,
                 ElectraForTokenClassification,
                 ElectraForSequenceClassification,
                 ElectraForQuestionAnswering,
             )
             if is_torch_available()
             else ()
         )
         _lowerCamelCase							:							Optional[Any]       =			(
             {
                 'feature-extraction': ElectraModel,
                 'fill-mask': ElectraForMaskedLM,
                 'question-answering': ElectraForQuestionAnswering,
                 'text-classification': ElectraForSequenceClassification,
                 'text-generation': ElectraForCausalLM,
                 'token-classification': ElectraForTokenClassification,
                 'zero-shot': ElectraForSequenceClassification,
             }
             if is_torch_available()
             else {}
         )
         _lowerCamelCase							:							Optional[int]       =			True
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					super()._prepare_for_class(lowerCamelCase_							,lowerCamelCase_							,return_labels=lowerCamelCase_		)
  
            if return_labels:
               if model_class in get_values(lowerCamelCase_		):
                  _UpperCAmelCase   	=					torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)							,dtype=torch.long							,device=lowerCamelCase_		)
            return inputs_dict
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraModelTester(self		)
            _UpperCAmelCase   	=					ConfigTester(self							,config_class=lowerCamelCase_							,hidden_size=37		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.config_tester.run_common_tests()
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_model(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_electra_model_as_decoder(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
               _UpperCAmelCase   	=					type
               self.model_tester.create_and_check_electra_model(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_masked_lm(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_token_classification(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_pretraining(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_question_answering(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCamelCase_		)
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase   	=					ElectraModel.from_pretrained(lowerCamelCase_		)
               self.assertIsNotNone(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_electra_for_causal_lm(*lowerCamelCase_		)
  
  
  
  
  
  
  @require_torch
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					ElectraModel.from_pretrained("google/electra-small-discriminator"		)
            _UpperCAmelCase   	=					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
            _UpperCAmelCase   	=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
            _UpperCAmelCase   	=					model(lowerCamelCase_							,attention_mask=lowerCamelCase_		)[0]
            _UpperCAmelCase   	=					torch.Size((1, 11, 256)		)
            self.assertEqual(output.shape							,lowerCamelCase_		)
            _UpperCAmelCase   	=					torch.tensor(
                [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]		)
  
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,lowerCamelCase_							,atol=1e-4		)		)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import collections
  import importlib.util
  import os
  import re
  
  
  # All paths are set with the intent you should run this script from the root of the repo with the command
  # python utils/check_table.py
  SCREAMING_SNAKE_CASE_     				=   'src/diffusers'
  SCREAMING_SNAKE_CASE_     				=   'docs/source/en'
  SCREAMING_SNAKE_CASE_     				=   '.'
  def 			__UpperCAmelCase		(		a_			:		Optional[Any]    ,  a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     with open(a_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
        _UpperCAmelCase   	=					f.readlines()
     # Find the start prompt.
     _UpperCAmelCase   	=					0
     while not lines[start_index].startswith(a_				):
        start_index += 1
     start_index += 1
  
     _UpperCAmelCase   	=					start_index
     while not lines[end_index].startswith(a_				):
        end_index += 1
     end_index -= 1
  
     while len(lines[start_index]				) <= 1:
        start_index += 1
     while len(lines[end_index]				) <= 1:
        end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]				), start_index, end_index, lines
  
  
  # Add here suffixes that are used to identify models, separated by |
  SCREAMING_SNAKE_CASE_     				=   'Model|Encoder|Decoder|ForConditionalGeneration'
  # Regexes that match TF/Flax/PT model names.
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  SCREAMING_SNAKE_CASE_     				=   importlib.util.spec_from_file_location(
      'diffusers',
      os.path.join(TRANSFORMERS_PATH, '__init__.py'),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  SCREAMING_SNAKE_CASE_     				=   spec.loader.load_module()
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     _UpperCAmelCase   	=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  a_				)
     return [m.group(0				) for m in matches]
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     _UpperCAmelCase   	=					2 if text == "✅" or text == "❌" else len(a_				)
     _UpperCAmelCase   	=					(width - text_length) // 2
     _UpperCAmelCase   	=					width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent
  def 			__UpperCAmelCase		(		)     ->       List[Any]:
     _UpperCAmelCase   	=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _UpperCAmelCase   	=					{
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     _UpperCAmelCase   	=					{name: config.replace("ConfigMixin"    ,  ""				) for name, config in model_name_to_config.items()}
  
     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
  
     # Let's lookup through all diffusers object (once).
     for attr_name in dir(a_				):
        _UpperCAmelCase   	=					None
        if attr_name.endswith("Tokenizer"				):
           _UpperCAmelCase   	=					slow_tokenizers
           _UpperCAmelCase   	=					attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"				):
           _UpperCAmelCase   	=					fast_tokenizers
           _UpperCAmelCase   	=					attr_name[:-13]
        elif _re_tf_models.match(a_				) is not None:
           _UpperCAmelCase   	=					tf_models
           _UpperCAmelCase   	=					_re_tf_models.match(a_				).groups()[0]
        elif _re_flax_models.match(a_				) is not None:
           _UpperCAmelCase   	=					flax_models
           _UpperCAmelCase   	=					_re_flax_models.match(a_				).groups()[0]
        elif _re_pt_models.match(a_				) is not None:
           _UpperCAmelCase   	=					pt_models
           _UpperCAmelCase   	=					_re_pt_models.match(a_				).groups()[0]
  
        if lookup_dict is not None:
           while len(a_				) > 0:
              if attr_name in model_name_to_prefix.values():
                 _UpperCAmelCase   	=					True
                 break
              # Try again after removing the last word in the name
              _UpperCAmelCase   	=					"".join(camel_case_split(a_				)[:-1]				)
  
      # Let's build that table!
     _UpperCAmelCase   	=					list(model_name_to_config.keys()				)
     model_names.sort(key=str.lower				)
     _UpperCAmelCase   	=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     _UpperCAmelCase   	=					[len(a_				) + 2 for c in columns]
     _UpperCAmelCase   	=					max([len(a_				) for name in model_names]				) + 2
  
     # Build the table per se
     _UpperCAmelCase   	=					"|" + "|".join([_center_text(a_    ,  a_				) for c, w in zip(a_    ,  a_				)]				) + "|\n"
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]				) + "|\n"
  
     _UpperCAmelCase   	=					{True: "✅", False: "❌"}
     for name in model_names:
        _UpperCAmelCase   	=					model_name_to_prefix[name]
        _UpperCAmelCase   	=					[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(a_    ,  a_				) for l, w in zip(a_    ,  a_				)]				) + "|\n"
     return table
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]=False				)     ->       int:
     _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					_find_text_in_file(
         filename=os.path.join(a_    ,  "index.mdx"				)    ,  start_prompt="<!--This table is updated automatically from the auto modules"    ,  end_prompt="<!-- End table-->"    ,  )
     _UpperCAmelCase   	=					get_model_table_from_auto_modules()
  
     if current_table != new_table:
        if overwrite:
           with open(os.path.join(a_    ,  "index.mdx"				)    ,  "w"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
              f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
        else:
           raise ValueError(
               "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
    check_model_table(args.fix_and_overwrite)
  
  