




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  __magic_name__						:
				'''simple docstring'''







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


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size		)

											SCREAMING_SNAKE_CASE_   	=    None
											if self.use_input_mask:
																		SCREAMING_SNAKE_CASE_   	=    random_attention_mask([self.batch_size, self.seq_length]		)

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

											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size]		,					self.type_sequence_label_size		)
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels		)
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size]		,					self.num_choices		)

											SCREAMING_SNAKE_CASE_   	=    self.get_config()

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


				def     _A							(							self:   Union[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=_lowerCamelCase		,					initializer_range=self.initializer_range		,					)


				def     _A							(							self:   Optional[int]		):
											(
											    (
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,		
											)   	=    self.prepare_config_and_inputs()

											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   List[str]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    NezhaModel(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					token_type_ids=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
											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     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    NezhaModel(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					encoder_hidden_states=_lowerCamelCase		,					encoder_attention_mask=_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					encoder_hidden_states=_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    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)		)
											self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    NezhaForMaskedLM(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    NezhaForNextSentencePrediction(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		,					)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, 2)		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    NezhaForPreTraining(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		,					next_sentence_label=_lowerCamelCase		,					)
											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     _A							(							self:   List[str]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    NezhaForQuestionAnswering(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   int		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.num_labels
											SCREAMING_SNAKE_CASE_   	=    NezhaForSequenceClassification(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.num_labels
											SCREAMING_SNAKE_CASE_   	=    NezhaForTokenClassification(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.num_choices
											SCREAMING_SNAKE_CASE_   	=    NezhaForMultipleChoice(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    input_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    token_type_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    input_mask.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		,					)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)		)





				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,		
											)   	=    config_and_inputs
											SCREAMING_SNAKE_CASE_   	=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
											return config, inputs_dict






@require_torch
class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					(
				    (
				        NezhaModel,
				        NezhaForMaskedLM,
				        NezhaForMultipleChoice,
				        NezhaForNextSentencePrediction,
				        NezhaForPreTraining,
				        NezhaForQuestionAnswering,
				        NezhaForSequenceClassification,
				        NezhaForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					(
				    {
				        "feature-extraction": NezhaModel,
				        "fill-mask": NezhaForMaskedLM,
				        "question-answering": NezhaForQuestionAnswering,
				        "text-classification": NezhaForSequenceClassification,
				        "token-classification": NezhaForTokenClassification,
				        "zero-shot": NezhaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					True


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=False		):
											SCREAMING_SNAKE_CASE_   	=    super()._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)

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


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    NezhaModelTester(self		)
											SCREAMING_SNAKE_CASE_   	=    ConfigTester(self		,					config_class=_lowerCamelCase		,					hidden_size=37		)


				def     _A							(							self:   str		):
											self.config_tester.run_common_tests()


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase		)


				def     _A							(							self:   Any		):
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,	(
											    SCREAMING_SNAKE_CASE_	
											)	,		
											)   	=    self.model_tester.prepare_config_and_inputs_for_decoder()

											SCREAMING_SNAKE_CASE_   	=    None

											self.model_tester.create_and_check_model_as_decoder(
											    _lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase		)


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCamelCase		)


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase		)


				@slow
				def     _A							(							self:   List[str]		):
											for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE_   	=    NezhaModel.from_pretrained(_lowerCamelCase		)
																		self.assertIsNotNone(_lowerCamelCase		)





				@slow
				@require_torch_gpu
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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

																		SCREAMING_SNAKE_CASE_   	=    True
																		SCREAMING_SNAKE_CASE_   	=    model_class(config=_lowerCamelCase		)

																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    torch.jit.trace(
																		    _lowerCamelCase		,					(inputs_dict['''input_ids'''].to('''cpu'''		), inputs_dict['''attention_mask'''].to('''cpu'''		))		)

																		with tempfile.TemporaryDirectory() as tmp:
																									torch.jit.save(_lowerCamelCase		,					os.path.join(_lowerCamelCase		,					'''bert.pt'''		)		)
																									SCREAMING_SNAKE_CASE_   	=    torch.jit.load(os.path.join(_lowerCamelCase		,					'''bert.pt'''		)		,					map_location=_lowerCamelCase		)
																									loaded(inputs_dict['''input_ids'''].to(_lowerCamelCase		)		,					inputs_dict['''attention_mask'''].to(_lowerCamelCase		)		)






@require_torch
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				@slow
				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[0, 1, 2, 3, 4, 5]]		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[0, 1, 1, 1, 1, 1]]		)
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					attention_mask=_lowerCamelCase		)[0]
											SCREAMING_SNAKE_CASE_   	=    torch.Size((1, 6, 7_68)		)
											self.assertEqual(output.shape		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    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]		,					_lowerCamelCase		,					atol=1E-4		)		)





				@slow
				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[0, 1, 2, 3, 4, 5]]		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[1, 1, 1, 1, 1, 1]]		)
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					attention_mask=_lowerCamelCase		)[0]
											SCREAMING_SNAKE_CASE_   	=    torch.Size((1, 6, 2_11_28)		)
											self.assertEqual(output.shape		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    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]		,					_lowerCamelCase		,					atol=1E-4		)		)





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    fname.split(os.path.sep     )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''  ,    _lowerCAmelCase     ).groups()[0]






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    file_names
											SCREAMING_SNAKE_CASE_   	=    image_transform
											SCREAMING_SNAKE_CASE_   	=    label_to_id


				def __len__(							self:   Any		):
											return len(self.file_names		)





				def __getitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.file_names[idx]
											SCREAMING_SNAKE_CASE_   	=    PIL.Image.open(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    raw_image.convert('''RGB'''		)
											if self.image_transform is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.image_transform(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    extract_label(_lowerCamelCase		)
											if self.label_to_id is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.label_to_id[label]
											return {"image": image, "label": label}






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(
														    cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='''all'''  ,    project_dir=args.project_dir     )
							else:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(cpu=args.cpu  ,    mixed_precision=args.mixed_precision     )

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_   	=    config['''lr''']
							SCREAMING_SNAKE_CASE_   	=    int(config['''num_epochs''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''seed''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''batch_size''']     )
							SCREAMING_SNAKE_CASE_   	=    config['''image_size''']
							if not isinstance(_lowerCAmelCase  ,    (list, tuple)     ):
														SCREAMING_SNAKE_CASE_   	=    (image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps  ,    '''isdigit'''     ):
														if args.checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					SCREAMING_SNAKE_CASE_   	=    int(args.checkpointing_steps     )
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    os.path.split(_lowerCAmelCase     )[-1].split('''.'''     )[0]
														accelerator.init_trackers(_lowerCAmelCase  ,    _lowerCAmelCase     )

							# Grab all the image filenames
							SCREAMING_SNAKE_CASE_   	=    [os.path.join(args.data_dir  ,    _lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('''.jpg'''     )]

							# Build the label correspondences
							SCREAMING_SNAKE_CASE_   	=    [extract_label(_lowerCAmelCase     ) for fname in file_names]
							SCREAMING_SNAKE_CASE_   	=    list(set(_lowerCAmelCase     )     )
							id_to_label.sort()
							SCREAMING_SNAKE_CASE_   	=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase     )}

							# Set the seed before splitting the data.
							np.random.seed(_lowerCAmelCase     )
							torch.manual_seed(_lowerCAmelCase     )
							torch.cuda.manual_seed_all(_lowerCAmelCase     )

							# Split our filenames between train and validation
							SCREAMING_SNAKE_CASE_   	=    np.random.permutation(len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    int(0.8 * len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    random_perm[:cut]
							SCREAMING_SNAKE_CASE_   	=    random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							SCREAMING_SNAKE_CASE_   	=    Compose([RandomResizedCrop(_lowerCAmelCase  ,    scale=(0.5, 1.0)     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset(
							    [file_names[i] for i in train_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# For evaluation, we use a deterministic Resize
							SCREAMING_SNAKE_CASE_   	=    Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset([file_names[i] for i in eval_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_   	=    create_model('''resnet50d'''  ,    pretrained=_lowerCAmelCase  ,    num_classes=len(_lowerCAmelCase     )     )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							SCREAMING_SNAKE_CASE_   	=    model.to(accelerator.device     )

							# Freezing the base model
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_   	=    False
							for param in model.get_classifier().parameters():
														SCREAMING_SNAKE_CASE_   	=    True

							# We normalize the batches of images to be a bit faster.
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''mean''']     )[None, :, None, None].to(accelerator.device     )
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''std''']     )[None, :, None, None].to(accelerator.device     )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_   	=    torch.optim.Adam(params=model.parameters()  ,    lr=lr / 2_5     )

							# Instantiate learning rate scheduler
							SCREAMING_SNAKE_CASE_   	=    OneCycleLR(optimizer=_lowerCAmelCase  ,    max_lr=_lowerCAmelCase  ,    epochs=_lowerCAmelCase  ,    steps_per_epoch=len(_lowerCAmelCase     )     )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE_   	=    0
							# We also need to keep track of the starting epoch so files are named properly
							SCREAMING_SNAKE_CASE_   	=    0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
																					accelerator.load_state(args.resume_from_checkpoint     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.basename(args.resume_from_checkpoint     )
														else:
																					# Get the most recent checkpoint
																					SCREAMING_SNAKE_CASE_   	=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime     )
																					SCREAMING_SNAKE_CASE_   	=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														SCREAMING_SNAKE_CASE_   	=    os.path.splitext(_lowerCAmelCase     )[0]

														if "epoch" in training_difference:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''epoch_'''  ,    ''''''     )     ) + 1
																					SCREAMING_SNAKE_CASE_   	=    None
														else:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''step_'''  ,    ''''''     )     )
																					SCREAMING_SNAKE_CASE_   	=    resume_step // len(_lowerCAmelCase     )
																					resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
							for epoch in range(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														model.train()
														if args.with_tracking:
																					SCREAMING_SNAKE_CASE_   	=    0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					SCREAMING_SNAKE_CASE_   	=    accelerator.skip_first_batches(_lowerCAmelCase  ,    _lowerCAmelCase     )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					SCREAMING_SNAKE_CASE_   	=    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.cross_entropy(_lowerCAmelCase  ,    batch['''label''']     )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase     )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																																			accelerator.save_state(_lowerCAmelCase     )
														model.eval()
														SCREAMING_SNAKE_CASE_   	=    0
														SCREAMING_SNAKE_CASE_   	=    0
														for step, batch in enumerate(_lowerCAmelCase     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    outputs.argmax(dim=-1     )
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather_for_metrics((predictions, batch['''label'''])     )
																					SCREAMING_SNAKE_CASE_   	=    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														SCREAMING_SNAKE_CASE_   	=    accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"     )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 1_0_0 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(_lowerCAmelCase     ),
																					        '''epoch''': epoch,
																					    }  ,    step=_lowerCAmelCase  ,    )
														if checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																					accelerator.save_state(_lowerCAmelCase     )

							if args.with_tracking:
														accelerator.end_training()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    argparse.ArgumentParser(description='''Simple example of training script.'''     )
							parser.add_argument('''--data_dir'''  ,    required=_lowerCAmelCase  ,    help='''The data folder on disk.'''     )
							parser.add_argument('''--fp16'''  ,    action='''store_true'''  ,    help='''If passed, will use FP16 training.'''     )
							parser.add_argument(
							    '''--mixed_precision'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''  ,    )
							parser.add_argument('''--cpu'''  ,    action='''store_true'''  ,    help='''If passed, will train on the CPU.'''     )
							parser.add_argument(
							    '''--checkpointing_steps'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,    )
							parser.add_argument(
							    '''--output_dir'''  ,    type=_lowerCAmelCase  ,    default='''.'''  ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,    )
							parser.add_argument(
							    '''--resume_from_checkpoint'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''If the training should continue from a checkpoint folder.'''  ,    )
							parser.add_argument(
							    '''--with_tracking'''  ,    action='''store_true'''  ,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,    )
							parser.add_argument(
							    '''--project_dir'''  ,    type=_lowerCAmelCase  ,    default='''logs'''  ,    help='''Location on where to store experiment tracking logs` and relevent project information'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(_lowerCAmelCase  ,    _lowerCAmelCase     )


if __name__ == "__main__":
			main()


