import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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,
	    NezhaForMaskedLM,
	    NezhaForMultipleChoice,
	    NezhaForNextSentencePrediction,
	    NezhaForPreTraining,
	    NezhaForQuestionAnswering,
	    NezhaForSequenceClassification,
	    NezhaForTokenClassification,
	    NezhaModel,
	)
	from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST




class     _snake_case  :
					def __init__(     self: Tuple		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: str=13		,     __lowerCamelCase: List[str]=7		,     __lowerCamelCase: Dict=True		,     __lowerCamelCase: Any=True		,     __lowerCamelCase: List[str]=True		,     __lowerCamelCase: List[str]=True		,     __lowerCamelCase: Optional[int]=99		,     __lowerCamelCase: Union[str, Any]=32		,     __lowerCamelCase: Optional[Any]=5		,     __lowerCamelCase: Union[str, Any]=4		,     __lowerCamelCase: Dict=37		,     __lowerCamelCase: List[str]="gelu"		,     __lowerCamelCase: Optional[Any]=0.1		,     __lowerCamelCase: List[Any]=0.1		,     __lowerCamelCase: Tuple=1_28		,     __lowerCamelCase: int=32		,     __lowerCamelCase: int=16		,     __lowerCamelCase: Dict=2		,     __lowerCamelCase: Dict=0.02		,     __lowerCamelCase: Any=3		,     __lowerCamelCase: Dict=4		,     __lowerCamelCase: Any=None		,     )			-> Optional[Any]:
												__UpperCAmelCase  :	List[Any]   			=      parent
												__UpperCAmelCase  :	Optional[Any]   			=      batch_size
												__UpperCAmelCase  :	Tuple   			=      seq_length
												__UpperCAmelCase  :	str   			=      is_training
												__UpperCAmelCase  :	Dict   			=      use_input_mask
												__UpperCAmelCase  :	Tuple   			=      use_token_type_ids
												__UpperCAmelCase  :	Dict   			=      use_labels
												__UpperCAmelCase  :	Tuple   			=      vocab_size
												__UpperCAmelCase  :	List[Any]   			=      hidden_size
												__UpperCAmelCase  :	str   			=      num_hidden_layers
												__UpperCAmelCase  :	List[Any]   			=      num_attention_heads
												__UpperCAmelCase  :	Tuple   			=      intermediate_size
												__UpperCAmelCase  :	Dict   			=      hidden_act
												__UpperCAmelCase  :	Union[str, Any]   			=      hidden_dropout_prob
												__UpperCAmelCase  :	Union[str, Any]   			=      attention_probs_dropout_prob
												__UpperCAmelCase  :	int   			=      max_position_embeddings
												__UpperCAmelCase  :	Any   			=      type_vocab_size
												__UpperCAmelCase  :	Union[str, Any]   			=      type_sequence_label_size
												__UpperCAmelCase  :	Union[str, Any]   			=      initializer_range
												__UpperCAmelCase  :	Any   			=      num_labels
												__UpperCAmelCase  :	str   			=      num_choices
												__UpperCAmelCase  :	List[str]   			=      scope
					def       _lowerCamelCase       (     self: List[Any]   )			-> Any:
												__UpperCAmelCase  :	Optional[Any]   			=      ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size   )

												__UpperCAmelCase  :	List[str]   			=      None
												if self.use_input_mask:
																			__UpperCAmelCase  :	Optional[int]   			=      random_attention_mask([self.batch_size, self.seq_length]   )

												__UpperCAmelCase  :	Dict   			=      None
												if self.use_token_type_ids:
																			__UpperCAmelCase  :	str   			=      ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size   )

												__UpperCAmelCase  :	Tuple   			=      None
												__UpperCAmelCase  :	Any   			=      None
												__UpperCAmelCase  :	Optional[int]   			=      None
												if self.use_labels:
																			__UpperCAmelCase  :	Optional[int]   			=      ids_tensor([self.batch_size]		,     self.type_sequence_label_size   )
																			__UpperCAmelCase  :	str   			=      ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels   )
																			__UpperCAmelCase  :	Optional[Any]   			=      ids_tensor([self.batch_size]		,     self.num_choices   )

												__UpperCAmelCase  :	Tuple   			=      self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def       _lowerCamelCase       (     self: List[str]   )			-> Any:
												return NezhaConfig(
												    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=__a		,     initializer_range=self.initializer_range		,     )
					def       _lowerCamelCase       (     self: List[str]   )			-> Tuple:
												(
												    (
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,							
												)  :	Optional[Any]   			=      self.prepare_config_and_inputs()

												__UpperCAmelCase  :	Union[str, Any]   			=      True
												__UpperCAmelCase  :	List[Any]   			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
												__UpperCAmelCase  :	Union[str, Any]   			=      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       _lowerCamelCase       (     self: Tuple		,     __lowerCamelCase: Optional[Any]		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: str   )			-> Optional[Any]:
												__UpperCAmelCase  :	List[str]   			=      NezhaModel(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Dict   			=      model(__a		,     attention_mask=__a		,     token_type_ids=__a   )
												__UpperCAmelCase  :	Optional[Any]   			=      model(__a		,     token_type_ids=__a   )
												__UpperCAmelCase  :	str   			=      model(__a   )
												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       _lowerCamelCase       (     self: Optional[Any]		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: Any		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: int		,     __lowerCamelCase: Dict		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: Dict		,     __lowerCamelCase: int		,     )			-> Optional[int]:
												__UpperCAmelCase  :	str   			=      True
												__UpperCAmelCase  :	Dict   			=      NezhaModel(__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Union[str, Any]   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     encoder_hidden_states=__a		,     encoder_attention_mask=__a		,     )
												__UpperCAmelCase  :	Any   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     encoder_hidden_states=__a		,     )
												__UpperCAmelCase  :	Dict   			=      model(__a		,     attention_mask=__a		,     token_type_ids=__a   )
												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       _lowerCamelCase       (     self: Optional[Any]		,     __lowerCamelCase: Optional[Any]		,     __lowerCamelCase: Dict		,     __lowerCamelCase: str		,     __lowerCamelCase: Any		,     __lowerCamelCase: Dict		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: List[str]   )			-> Optional[int]:
												__UpperCAmelCase  :	List[Any]   			=      NezhaForMaskedLM(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Any   			=      model(__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)   )
					def       _lowerCamelCase       (     self: List[Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: str		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: Any		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: Union[str, Any]   )			-> int:
												__UpperCAmelCase  :	str   			=      NezhaForNextSentencePrediction(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Dict   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, 2)   )
					def       _lowerCamelCase       (     self: List[str]		,     __lowerCamelCase: Optional[Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Any		,     __lowerCamelCase: Any		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: Any		,     __lowerCamelCase: Union[str, Any]   )			-> Optional[Any]:
												__UpperCAmelCase  :	Optional[int]   			=      NezhaForPreTraining(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Optional[Any]   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     next_sentence_label=__a		,     )
												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       _lowerCamelCase       (     self: Any		,     __lowerCamelCase: int		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: List[Any]   )			-> Union[str, Any]:
												__UpperCAmelCase  :	Union[str, Any]   			=      NezhaForQuestionAnswering(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	int   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     start_positions=__a		,     end_positions=__a		,     )
												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       _lowerCamelCase       (     self: Union[str, Any]		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: Any   )			-> Any:
												__UpperCAmelCase  :	List[Any]   			=      self.num_labels
												__UpperCAmelCase  :	Optional[int]   			=      NezhaForSequenceClassification(__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Optional[int]   			=      model(__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)   )
					def       _lowerCamelCase       (     self: str		,     __lowerCamelCase: str		,     __lowerCamelCase: int		,     __lowerCamelCase: Optional[Any]		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: Tuple		,     __lowerCamelCase: str		,     __lowerCamelCase: List[str]   )			-> Optional[int]:
												__UpperCAmelCase  :	str   			=      self.num_labels
												__UpperCAmelCase  :	Dict   			=      NezhaForTokenClassification(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Any   			=      model(__a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)   )
					def       _lowerCamelCase       (     self: Union[str, Any]		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: str		,     __lowerCamelCase: Optional[int]		,     __lowerCamelCase: int		,     __lowerCamelCase: Any		,     __lowerCamelCase: Dict		,     __lowerCamelCase: Optional[int]   )			-> List[Any]:
												__UpperCAmelCase  :	int   			=      self.num_choices
												__UpperCAmelCase  :	List[str]   			=      NezhaForMultipleChoice(config=__a   )
												model.to(__a   )
												model.eval()
												__UpperCAmelCase  :	Dict   			=      input_ids.unsqueeze(1   ).expand(-1		,     self.num_choices		,     -1   ).contiguous()
												__UpperCAmelCase  :	Union[str, Any]   			=      token_type_ids.unsqueeze(1   ).expand(-1		,     self.num_choices		,     -1   ).contiguous()
												__UpperCAmelCase  :	str   			=      input_mask.unsqueeze(1   ).expand(-1		,     self.num_choices		,     -1   ).contiguous()
												__UpperCAmelCase  :	Optional[int]   			=      model(
												    __a		,     attention_mask=__a		,     token_type_ids=__a		,     labels=__a		,     )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)   )





					def       _lowerCamelCase       (     self: Any   )			-> Dict:
												__UpperCAmelCase  :	str   			=      self.prepare_config_and_inputs()
												(
												    (
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,							
												)  :	str   			=      config_and_inputs
												__UpperCAmelCase  :	str   			=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict




@require_torch
class     _snake_case  (  lowerCamelCase_    ,					lowerCamelCase_    ,					lowerCamelCase_    ,					unittest.TestCase ):
					lowerCamelCase__:						List[str]     =   (
					    (
					        NezhaModel,
					        NezhaForMaskedLM,
					        NezhaForMultipleChoice,
					        NezhaForNextSentencePrediction,
					        NezhaForPreTraining,
					        NezhaForQuestionAnswering,
					        NezhaForSequenceClassification,
					        NezhaForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCamelCase__:						Tuple     =   (
					    {
					        "feature-extraction": NezhaModel,
					        "fill-mask": NezhaForMaskedLM,
					        "question-answering": NezhaForQuestionAnswering,
					        "text-classification": NezhaForSequenceClassification,
					        "token-classification": NezhaForTokenClassification,
					        "zero-shot": NezhaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCamelCase__:						Tuple     =   True
					def       _lowerCamelCase       (     self: Dict		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: Any		,     __lowerCamelCase: Optional[int]=False   )			-> Dict:
												__UpperCAmelCase  :	Tuple   			=      super()._prepare_for_class(__a		,     __a		,     return_labels=__a   )

												if return_labels:
																			if model_class in get_values(__a   ):
																										__UpperCAmelCase  :	Dict   			=      torch.zeros(
																										    (self.model_tester.batch_size, self.model_tester.seq_length)		,     dtype=torch.long		,     device=__a   )
																										__UpperCAmelCase  :	Dict   			=      torch.zeros(
																										    self.model_tester.batch_size		,     dtype=torch.long		,     device=__a   )
												return inputs_dict
					def       _lowerCamelCase       (     self: int   )			-> Dict:
												__UpperCAmelCase  :	List[str]   			=      NezhaModelTester(self   )
												__UpperCAmelCase  :	List[Any]   			=      ConfigTester(self		,     config_class=__a		,     hidden_size=37   )
					def       _lowerCamelCase       (     self: int   )			-> Optional[Any]:
												self.config_tester.run_common_tests()
					def       _lowerCamelCase       (     self: Union[str, Any]   )			-> List[str]:
												__UpperCAmelCase  :	Any   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a   )
					def       _lowerCamelCase       (     self: Union[str, Any]   )			-> Any:
												__UpperCAmelCase  :	Tuple   			=      self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__a   )
					def       _lowerCamelCase       (     self: str   )			-> Union[str, Any]:
												# This regression test was failing with PyTorch < 1.3
												(
												    (
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,		(
												    __UpperCAmelCase					
												)			,							
												)  :	Union[str, Any]   			=      self.model_tester.prepare_config_and_inputs_for_decoder()

												__UpperCAmelCase  :	Any   			=      None

												self.model_tester.create_and_check_model_as_decoder(
												    __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     )
					def       _lowerCamelCase       (     self: Optional[int]   )			-> Any:
												__UpperCAmelCase  :	List[str]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__a   )
					def       _lowerCamelCase       (     self: Any   )			-> Tuple:
												__UpperCAmelCase  :	Union[str, Any]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*__a   )
					def       _lowerCamelCase       (     self: Tuple   )			-> List[str]:
												__UpperCAmelCase  :	List[str]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_next_sequence_prediction(*__a   )
					def       _lowerCamelCase       (     self: Tuple   )			-> Optional[int]:
												__UpperCAmelCase  :	List[Any]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__a   )
					def       _lowerCamelCase       (     self: Tuple   )			-> Union[str, Any]:
												__UpperCAmelCase  :	Dict   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__a   )
					def       _lowerCamelCase       (     self: Optional[Any]   )			-> Dict:
												__UpperCAmelCase  :	List[Any]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__a   )
					def       _lowerCamelCase       (     self: Dict   )			-> Tuple:
												__UpperCAmelCase  :	Union[str, Any]   			=      self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__a   )
					@slow
					def       _lowerCamelCase       (     self: int   )			-> Tuple:
												for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase  :	Optional[Any]   			=      NezhaModel.from_pretrained(__a   )
																			self.assertIsNotNone(__a   )





					@slow
					@require_torch_gpu
					def       _lowerCamelCase       (     self: List[str]   )			-> str:
												__UpperCAmelCase			,		__UpperCAmelCase  :	Optional[int]   			=      self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			# NezhaForMultipleChoice behaves incorrectly in JIT environments.
																			if model_class == NezhaForMultipleChoice:
																										return

																			__UpperCAmelCase  :	int   			=      True
																			__UpperCAmelCase  :	Dict   			=      model_class(config=__a   )

																			__UpperCAmelCase  :	List[str]   			=      self._prepare_for_class(__a		,     __a   )
																			__UpperCAmelCase  :	Optional[Any]   			=      torch.jit.trace(
																			    __a		,     (inputs_dict["input_ids"].to("cpu"   ), inputs_dict["attention_mask"].to("cpu"   ))   )

																			with tempfile.TemporaryDirectory() as tmp:
																										torch.jit.save(__a		,     os.path.join(__a		,     "bert.pt"   )   )
																										__UpperCAmelCase  :	List[Any]   			=      torch.jit.load(os.path.join(__a		,     "bert.pt"   )		,     map_location=__a   )
																										loaded(inputs_dict["input_ids"].to(__a   )		,     inputs_dict["attention_mask"].to(__a   )   )




@require_torch
class     _snake_case  (  unittest.TestCase ):
					@slow
					def       _lowerCamelCase       (     self: List[Any]   )			-> Optional[int]:
												__UpperCAmelCase  :	Dict   			=      NezhaModel.from_pretrained("sijunhe/nezha-cn-base"   )
												__UpperCAmelCase  :	List[Any]   			=      torch.tensor([[0, 1, 2, 3, 4, 5]]   )
												__UpperCAmelCase  :	Tuple   			=      torch.tensor([[0, 1, 1, 1, 1, 1]]   )
												with torch.no_grad():
																			__UpperCAmelCase  :	Union[str, Any]   			=      model(__a		,     attention_mask=__a   )[0]
												__UpperCAmelCase  :	Dict   			=      torch.Size((1, 6, 7_68)   )
												self.assertEqual(output.shape		,     __a   )
												__UpperCAmelCase  :	str   			=      torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]   )

												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,     __a		,     atol=1e-4   )   )





					@slow
					def       _lowerCamelCase       (     self: List[str]   )			-> Dict:
												__UpperCAmelCase  :	Optional[int]   			=      NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"   )
												__UpperCAmelCase  :	int   			=      torch.tensor([[0, 1, 2, 3, 4, 5]]   )
												__UpperCAmelCase  :	List[str]   			=      torch.tensor([[1, 1, 1, 1, 1, 1]]   )
												with torch.no_grad():
																			__UpperCAmelCase  :	Optional[Any]   			=      model(__a		,     attention_mask=__a   )[0]
												__UpperCAmelCase  :	Union[str, Any]   			=      torch.Size((1, 6, 2_11_28)   )
												self.assertEqual(output.shape		,     __a   )
												__UpperCAmelCase  :	List[Any]   			=      torch.tensor(
												    [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]   )

												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,     __a		,     atol=1e-4   )   )











'''simple docstring'''







import argparse
import os
import re


lowerCAmelCase_					:    Any      	=			'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_					:    List[str]      	=			re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowerCAmelCase_					:    Tuple      	=			re.compile(R'\s*\(\s*"(\S[^"]+)"')






def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      bool = False						)	->	Optional[Any]:
				with open(lowercase    ,							"r"    ,							encoding="utf-8"						) as f:
								_a								= f.read()

				_a								= content.split("\n"						)
				_a								= []
				_a								= 0
				while line_idx < len(lowercase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												_a								= len(re.search(r"^(\s*)\S"    ,							lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(" " * indent + "("						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												_a								= []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				_a								= line_idx
																				while not lines[line_idx].startswith(" " * indent + ")"						):
																								line_idx += 1
																				blocks.append("\n".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												_a								= sorted(lowercase    ,							key=lambda lowercase						: _re_identifier.search(lowercase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as f:
												f.write("\n".join(lowercase						)						)
				elif "\n".join(lowercase						) != content:
								return True






def  _lowerCamelCase				( lowercase						:      bool = False						)	->	List[str]:
				_a								= [os.path.join(lowercase    ,							lowercase						) for f in os.listdir(lowercase						) if f.endswith(".py"						)]
				_a								= [sort_auto_mapping(lowercase    ,							overwrite=lowercase						) for fname in fnames]

				if not overwrite and any(lowercase						):
								_a								= [f for f, d in zip(lowercase    ,							lowercase						) if d]
								raise ValueError(
								    F'The following files have auto mappings that need sorting: {", ".join(lowercase						)}. Run `make style` to fix'
								    " this."						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			argparse.ArgumentParser()
					parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
					lowerCAmelCase_					:    Optional[int]      	=			parser.parse_args()

					sort_all_auto_mappings(not args.check_only)
