




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		)		)








from __future__ import annotations

__SCREAMING_SNAKE_CASE 						={
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					_lowerCamelCase:   dict[str, list[str]]		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    graph
											# mapping node to its parent in resulting breadth first tree
											SCREAMING_SNAKE_CASE_   	=    {}
											SCREAMING_SNAKE_CASE_   	=    source_vertex


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    {self.source_vertex}
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    [self.source_vertex]  # first in first out queue

											while queue:
																		SCREAMING_SNAKE_CASE_   	=    queue.pop(0		)
																		for adjacent_vertex in self.graph[vertex]:
																									if adjacent_vertex not in visited:
																																visited.add(_lowerCamelCase		)
																																SCREAMING_SNAKE_CASE_   	=    vertex
																																queue.append(_lowerCamelCase		)





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   str		):
											if target_vertex == self.source_vertex:
																		return self.source_vertex

											SCREAMING_SNAKE_CASE_   	=    self.parent.get(_lowerCamelCase		)
											if target_vertex_parent is None:
																		SCREAMING_SNAKE_CASE_   	=    (
																		    f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_lowerCamelCase		)

											return self.shortest_path(_lowerCamelCase		) + f"->{target_vertex}"


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=Graph(graph, """G""")
			g.breath_first_search()
			print(g.shortest_path("""D"""))
			print(g.shortest_path("""G"""))
			print(g.shortest_path("""Foo"""))


