



'''simple docstring'''





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)





class  __magic_name__   :






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[str]=13				,		UpperCamelCase__      :     Tuple=7				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     str=True				,		UpperCamelCase__      :     Optional[Any]=99				,		UpperCamelCase__      :     str=64				,		UpperCamelCase__      :     List[str]=32				,		UpperCamelCase__      :     List[str]=5				,		UpperCamelCase__      :     Tuple=4				,		UpperCamelCase__      :     str=37				,		UpperCamelCase__      :     Optional[Any]="gelu"				,		UpperCamelCase__      :     List[Any]=0.1				,		UpperCamelCase__      :     Optional[Any]=0.1				,		UpperCamelCase__      :     List[Any]=5_12				,		UpperCamelCase__      :     Tuple=16				,		UpperCamelCase__      :     Optional[int]=2				,		UpperCamelCase__      :     List[Any]=0.02				,		UpperCamelCase__      :     Union[str, Any]=3				,		UpperCamelCase__      :     Dict=4				,		UpperCamelCase__      :     int=None				,		)				->					Tuple:
								'''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         =     embedding_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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''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         =     self.get_config()

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								return MobileBertConfig(
								    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				,		embedding_size=self.embedding_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=UpperCamelCase__				,		initializer_range=self.initializer_range				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModel(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__      )
								UpperCAmelCase         =     model(UpperCamelCase__				,		token_type_ids=UpperCamelCase__      )
								UpperCAmelCase         =     model(UpperCamelCase__      )

								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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForMaskedLM(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForNextSentencePrediction(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		)
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, 2)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForPreTraining(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		next_sentence_label=UpperCamelCase__				,		)
								self.parent.assertEqual(result.prediction_logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertEqual(result.seq_relationship_logits.shape				,		(self.batch_size, 2)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertForQuestionAnswering(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		start_positions=UpperCamelCase__				,		end_positions=UpperCamelCase__				,		)
								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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MobileBertForSequenceClassification(UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_labels
								UpperCAmelCase         =     MobileBertForTokenClassification(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								model.eval()
								UpperCAmelCase         =     model(UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__      )
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.num_labels)      )






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.num_choices
								UpperCAmelCase         =     MobileBertForMultipleChoice(config=UpperCamelCase__      )
								model.to(UpperCamelCase__      )
								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(
								    UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		token_type_ids=UpperCamelCase__				,		labels=UpperCamelCase__				,		)
								self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_choices)      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								(
								    (
								    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  __magic_name__   (							A__,  A__,  unittest.TestCase						):
		lowercase      :       int          =(
		    (
		        MobileBertModel,
		        MobileBertForMaskedLM,
		        MobileBertForMultipleChoice,
		        MobileBertForNextSentencePrediction,
		        MobileBertForPreTraining,
		        MobileBertForQuestionAnswering,
		        MobileBertForSequenceClassification,
		        MobileBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase      :       str          =(
		    {
		        '''feature-extraction''': MobileBertModel,
		        '''fill-mask''': MobileBertForMaskedLM,
		        '''question-answering''': MobileBertForQuestionAnswering,
		        '''text-classification''': MobileBertForSequenceClassification,
		        '''token-classification''': MobileBertForTokenClassification,
		        '''zero-shot''': MobileBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase      :       List[str]          =True






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[int]=False      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     super()._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__				,		return_labels=UpperCamelCase__      )

								if return_labels:
														if model_class in get_values(UpperCamelCase__      ):
																				UpperCAmelCase         =     torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.seq_length)				,		dtype=torch.long				,		device=UpperCamelCase__      )
																				UpperCAmelCase         =     torch.zeros(
																				    self.model_tester.batch_size				,		dtype=torch.long				,		device=UpperCamelCase__      )
								return inputs_dict






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		hidden_size=37      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								self.config_tester.run_common_tests()






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__      )

def    lowerCamelCase_(lowerCamelCase_    ) ->       List[str]:
						return torch.tensor(
						    lowerCamelCase_    ,   dtype=torch.long    ,   device=lowerCamelCase_    ,   )


__lowerCamelCase     :							str  							=				1E-3





@require_torch
@require_sentencepiece
@require_tokenizers
class  __magic_name__   (							unittest.TestCase						):






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MobileBertModel.from_pretrained("google/mobilebert-uncased"      ).to(UpperCamelCase__      )
								UpperCAmelCase         =     _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      )
								with torch.no_grad():
														UpperCAmelCase         =     model(UpperCamelCase__      )[0]
								UpperCAmelCase         =     torch.Size((1, 9, 5_12)      )
								self.assertEqual(output.shape				,		UpperCamelCase__      )
								UpperCAmelCase         =     torch.tensor(
								    [
								        [
								            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
								            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
								            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
								        ]
								    ]				,		device=UpperCamelCase__				,		)

								# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
								# ~1 difference, it's therefore not a good idea to measure using addition.
								# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
								# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
								UpperCAmelCase         =     torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE      )
								UpperCAmelCase         =     torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE      )

								self.assertTrue(lower_bound and upper_bound      )








import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCamelCase     :							str  							=				os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCamelCase     :							Optional[Any]  							=				"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,		"models/bert/"      )      )
								UpperCAmelCase         =     self.transformer_dir
								shutil.copy(
								    os.path.join(UpperCamelCase__				,		"src/transformers/models/bert/modeling_bert.py"      )				,		os.path.join(self.transformer_dir				,		"models/bert/modeling_bert.py"      )				,		)






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     "src/transformers"
								shutil.rmtree(self.transformer_dir      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Union[str, Any]=None      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														UpperCAmelCase         =     comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								UpperCAmelCase         =     black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=1_19      )
								UpperCAmelCase         =     black.format_str(UpperCamelCase__				,		mode=UpperCamelCase__      )
								UpperCAmelCase         =     os.path.join(self.transformer_dir				,		"new_code.py"      )
								with open(UpperCamelCase__				,		"w"				,		newline="\n"      ) as f:
														f.write(UpperCamelCase__      )
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__      )      ) == 0      )
								else:
														check_copies.is_copy_consistent(f.name				,		overwrite=UpperCamelCase__      )
														with open(UpperCamelCase__				,		"r"      ) as f:
																				self.assertTrue(f.read()				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		REFERENCE_CODE + "\n"				,		)

								# With no empty line at the end
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"				,		"BertLMPredictionHead"				,		UpperCamelCase__				,		)

								# Copy consistency with rename
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)

								# Copy consistency with a really long name
								UpperCAmelCase         =     "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
								self.check_copy_consistency(
								    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'				,		F'{long_class_name}LMPredictionHead'				,		re.sub("Bert"				,		UpperCamelCase__				,		UpperCamelCase__      )				,		)

								# Copy consistency with overwrite
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"				,		"TestModelLMPredictionHead"				,		UpperCamelCase__				,		overwrite_result=re.sub("Bert"				,		"TestModel"				,		UpperCamelCase__      )				,		)







		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
								    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
								    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
								    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
								    " Luong, Quoc V. Le, Christopher D. Manning."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
								    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
								    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
								    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
								    " Christopher D. Manning 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								self.assertFalse(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check whether the number of models is equal to README.md after conversion.
								self.assertTrue(UpperCamelCase__      )

								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
								    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								UpperCAmelCase         =     (
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)

								UpperCAmelCase       ,		UpperCAmelCase         =     check_copies.convert_to_localized_md(
								    UpperCamelCase__				,		UpperCamelCase__				,		localized_readme["format_model_list"]      )

								# Check if the model link is synchronized.
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



