




'''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 						a_				:




	def __init__(       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Union[str, Any]=1_3       ,		__lowerCAmelCase     :  str=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple=True       ,		__lowerCAmelCase     :  Union[str, Any]=True       ,		__lowerCAmelCase     :  str=9_9       ,		__lowerCAmelCase     :  List[str]=3_2       ,		__lowerCAmelCase     :  List[Any]=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  Union[str, Any]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Optional[int]=0.1       ,		__lowerCAmelCase     :  Optional[Any]=0.1       ,		__lowerCAmelCase     :  str=1_2_8       ,		__lowerCAmelCase     :  str=3_2       ,		__lowerCAmelCase     :  Optional[Any]=1_6       ,		__lowerCAmelCase     :  List[str]=2       ,		__lowerCAmelCase     :  Tuple=0.02       ,		__lowerCAmelCase     :  int=3       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  Tuple=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_input_mask
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   scope




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

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

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

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

							__snake_case						 =   self.get_config()

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




	def   lowercase__   (       self     :  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=__lowerCAmelCase       ,		initializer_range=self.initializer_range       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   self.prepare_config_and_inputs()

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




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   NezhaForPreTraining(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__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   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   NezhaForQuestionAnswering(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__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   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   NezhaForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__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   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Optional[Any]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   NezhaForTokenClassification(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__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   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Dict      ):
							__snake_case						 =   self.num_choices
							__snake_case						 =   NezhaForMultipleChoice(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()
							__snake_case						 =   input_ids.unsqueeze(1      ).expand(-1       ,		self.num_choices       ,		-1      ).contiguous()
							__snake_case						 =   token_type_ids.unsqueeze(1      ).expand(-1       ,		self.num_choices       ,		-1      ).contiguous()
							__snake_case						 =   input_mask.unsqueeze(1      ).expand(-1       ,		self.num_choices       ,		-1      ).contiguous()
							__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   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[Any]			  =      (
	    (
	        NezhaModel,
	        NezhaForMaskedLM,
	        NezhaForMultipleChoice,
	        NezhaForNextSentencePrediction,
	        NezhaForPreTraining,
	        NezhaForQuestionAnswering,
	        NezhaForSequenceClassification,
	        NezhaForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     int			  =      (
	    {
	        '''feature-extraction''': NezhaModel,
	        '''fill-mask''': NezhaForMaskedLM,
	        '''question-answering''': NezhaForQuestionAnswering,
	        '''text-classification''': NezhaForSequenceClassification,
	        '''token-classification''': NezhaForTokenClassification,
	        '''zero-shot''': NezhaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase_       :     List[str]			  =      True




	def   lowercase__   (       self     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Dict=False      ):
							__snake_case						 =   super()._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase       ,		return_labels=__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   NezhaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							# This regression test was failing with PyTorch < 1.3
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   self.model_tester.prepare_config_and_inputs_for_decoder()

							__snake_case						 =   None

							self.model_tester.create_and_check_model_as_decoder(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  Any      ):
							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   NezhaModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )




	@slow
	@require_torch_gpu
	def   lowercase__   (       self     :  Tuple      ):
							__snake_case ,       __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

													__snake_case						 =   True
													__snake_case						 =   model_class(config=__lowerCAmelCase      )

													__snake_case						 =   self._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase      )
													__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'      )      )
																			__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 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   NezhaModel.from_pretrained('sijunhe/nezha-cn-base'      )
							__snake_case						 =   torch.tensor([[0, 1, 2, 3, 4, 5]]      )
							__snake_case						 =   torch.tensor([[0, 1, 1, 1, 1, 1]]      )
							with torch.no_grad():
													__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]
							__snake_case						 =   torch.Size((1, 6, 7_6_8)      )
							self.assertEqual(output.shape       ,		__lowerCAmelCase      )
							__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]       ,		__lowerCAmelCase       ,		atol=1E-4      )      )




	@slow
	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'      )
							__snake_case						 =   torch.tensor([[0, 1, 2, 3, 4, 5]]      )
							__snake_case						 =   torch.tensor([[1, 1, 1, 1, 1, 1]]      )
							with torch.no_grad():
													__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]
							__snake_case						 =   torch.Size((1, 6, 2_1_1_2_8)      )
							self.assertEqual(output.shape       ,		__lowerCAmelCase      )
							__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]       ,		__lowerCAmelCase       ,		atol=1E-4      )      )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

