"""simple docstring"""






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 lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				str=13   , _snake_case			:				Dict=7   , _snake_case			:				Dict=True   , _snake_case			:				str=True   , _snake_case			:				Dict=True   , _snake_case			:				Any=True   , _snake_case			:				Any=99   , _snake_case			:				List[Any]=32   , _snake_case			:				str=5   , _snake_case			:				str=4   , _snake_case			:				str=37   , _snake_case			:				Tuple="gelu"   , _snake_case			:				str=0.1   , _snake_case			:				List[str]=0.1   , _snake_case			:				Tuple=128   , _snake_case			:				Dict=32   , _snake_case			:				Optional[Any]=16   , _snake_case			:				Optional[int]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				Any=4   , _snake_case			:				int=None   , )							->       Optional[Any]:
									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 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									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 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									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=_snake_case   , initializer_range=self.initializer_range   , )



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									(
									    (
									    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 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				str   , _snake_case			:				Dict   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				Tuple   , _snake_case			:				Dict   , _snake_case			:				Optional[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				NezhaModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									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			:				List[Any]   , _snake_case			:				str   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				Dict   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				Any   , )							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				NezhaModel(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , encoder_hidden_states=_snake_case   , encoder_attention_mask=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , encoder_hidden_states=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									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			:				Any   , _snake_case			:				str   , _snake_case			:				List[str]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Optional[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				NezhaForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				str   , _snake_case			:				int   , _snake_case			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				NezhaForNextSentencePrediction(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, 2)		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Dict   , _snake_case			:				str		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				NezhaForPreTraining(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , next_sentence_label=_snake_case   , )
									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			:				str   , _snake_case			:				Dict   , _snake_case			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				str   , _snake_case			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				NezhaForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , start_positions=_snake_case   , end_positions=_snake_case   , )
									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			:				str   , _snake_case			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Optional[Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				NezhaForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				Any   , _snake_case			:				List[str]   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				Dict		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				NezhaForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				List[str]   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				Tuple   , _snake_case			:				List[Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				NezhaForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									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(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


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



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				Dict   , _snake_case			:				Tuple=False		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				super()._prepare_for_class(_snake_case   , _snake_case   , return_labels=_snake_case		)

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



					def 			lowerCAmelCase_					(						self			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				NezhaModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Any:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[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(
									    _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , )



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_next_sequence_prediction(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				NezhaModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					@slow
					@require_torch_gpu
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Union[str, 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=_snake_case		)

													SCREAMING_SNAKE_CASE__		  =				self._prepare_for_class(_snake_case   , _snake_case		)
													SCREAMING_SNAKE_CASE__		  =				torch.jit.trace(
													    _snake_case   , (inputs_dict["input_ids"].to("cpu"		), inputs_dict["attention_mask"].to("cpu"		))		)

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



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									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(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 6, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]		)

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Dict:
									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(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 6, 21128)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(
									    [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]		)

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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




