
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 							UpperCAmelCase      :




	'''simple docstring'''





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






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

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

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

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

					__A   			= self.get_config()

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






	def 				UpperCamelCase_  (       self					: int ):
					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 				UpperCamelCase_  (       self					: Tuple ):
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= self.prepare_config_and_inputs()

					__A   			= True
					__A   			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					__A   			= 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 				UpperCamelCase_  (       self					: str							,A					: Tuple							,A					: List[str]							,A					: List[Any]							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: Optional[int] ):
					__A   			= NezhaModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A							,attention_mask=A							,token_type_ids=A )
					__A   			= model(A							,token_type_ids=A )
					__A   			= 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 				UpperCamelCase_  (       self					: Optional[int]							,A					: Optional[int]							,A					: Union[str, Any]							,A					: Any							,A					: Any							,A					: Dict							,A					: Union[str, Any]							,A					: List[str]							,A					: List[Any]							,A					: Union[str, Any]							,):
					__A   			= True
					__A   			= NezhaModel(A )
					model.to(A )
					model.eval()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,encoder_hidden_states=A							,encoder_attention_mask=A							,)
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,encoder_hidden_states=A							,)
					__A   			= 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 				UpperCamelCase_  (       self					: int							,A					: int							,A					: Any							,A					: List[str]							,A					: Tuple							,A					: Optional[int]							,A					: Any							,A					: Union[str, Any] ):
					__A   			= NezhaForMaskedLM(config=A )
					model.to(A )
					model.eval()
					__A   			= 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 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[str]							,A					: List[Any]							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple							,A					: Union[str, Any]							,A					: List[Any] ):
					__A   			= NezhaForNextSentencePrediction(config=A )
					model.to(A )
					model.eval()
					__A   			= model(
					    A							,attention_mask=A							,token_type_ids=A							,labels=A							,)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, 2) )






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






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







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.prepare_config_and_inputs()
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= config_and_inputs
					__A   			= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





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






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Any							,A					: Union[str, Any]							,A					: int=False ):
					__A   			= super()._prepare_for_class(A							,A							,return_labels=A )

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






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= NezhaModelTester(self )
					__A   			= ConfigTester(self							,config_class=A							,hidden_size=37 )






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






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					# This regression test was failing with PyTorch < 1.3
					(
					    (
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							(
					    __A
					)			,							
					)   			= self.model_tester.prepare_config_and_inputs_for_decoder()

					__A   			= None

					self.model_tester.create_and_check_model_as_decoder(
					    A							,A							,A							,A							,A							,A							,A							,A							,A							,)






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_next_sequence_prediction(*A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*A )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*A )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*A )






	@slow
	def 				UpperCamelCase_  (       self					: int ):
					for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= NezhaModel.from_pretrained(A )
									self.assertIsNotNone(A )







	@slow
	@require_torch_gpu
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A			,							__A   			= 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

									__A   			= True
									__A   			= model_class(config=A )

									__A   			= self._prepare_for_class(A							,A )
									__A   			= 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" ) )
													__A   			= 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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
					__A   			= torch.tensor([[0, 1, 2, 3, 4, 5]] )
					__A   			= torch.tensor([[0, 1, 1, 1, 1, 1]] )
					with torch.no_grad():
									__A   			= model(A							,attention_mask=A )[0]
					__A   			= torch.Size((1, 6, 7_68) )
					self.assertEqual(output.shape							,A )
					__A   			= 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 				UpperCamelCase_  (       self					: str ):
					__A   			= NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
					__A   			= torch.tensor([[0, 1, 2, 3, 4, 5]] )
					__A   			= torch.tensor([[1, 1, 1, 1, 1, 1]] )
					with torch.no_grad():
									__A   			= model(A							,attention_mask=A )[0]
					__A   			= torch.Size((1, 6, 2_11_28) )
					self.assertEqual(output.shape							,A )
					__A   			= 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 ) )




import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def        _UpperCamelCase							(      *lowercase__    ,       lowercase__ = None    ,       lowercase__=True    ,       lowercase__=2      ):
						from .. import __version__

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		take_from
						__SCREAMING_SNAKE_CASE						:     List[str]					=		()
						if not isinstance(args[0]    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(args,)

						for attribute, version_name, message in args:
												if version.parse(version.parse(lowercase__      ).base_version      ) >= version.parse(lowercase__      ):
																		raise ValueError(
																		    F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
																		    F''' version {__version__} is >= {version_name}'''      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
												if isinstance(lowercase__    ,       lowercase__      ) and attribute in deprecated_kwargs:
																		values += (deprecated_kwargs.pop(lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
												elif hasattr(lowercase__    ,       lowercase__      ):
																		values += (getattr(lowercase__    ,       lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
												elif deprecated_kwargs is None:
																		__SCREAMING_SNAKE_CASE						:     str					=		F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

												if warning is not None:
																		__SCREAMING_SNAKE_CASE						:     Any					=		warning + ''' ''' if standard_warn else ''''''
																		warnings.warn(warning + message    ,       lowercase__    ,       stacklevel=lowercase__      )

						if isinstance(lowercase__    ,       lowercase__      ) and len(lowercase__      ) > 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inspect.getouterframes(inspect.currentframe()      )[1]
												__SCREAMING_SNAKE_CASE						:     Dict					=		call_frame.filename
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		call_frame.lineno
												__SCREAMING_SNAKE_CASE						:     int					=		call_frame.function
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		next(iter(deprecated_kwargs.items()      )      )
												raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''      )

						if len(lowercase__      ) == 0:
												return
						elif len(lowercase__      ) == 1:
												return values[0]
						return values
