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



					def       lowerCAmelCase_       (    self      :					int		)				->							List[str]:
									(
									    (
									    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      :					Optional[Any] ,			_snake_case      :					Optional[int] ,			_snake_case      :					Any ,			_snake_case      :					Any ,			_snake_case      :					Dict ,			_snake_case      :					Dict ,			_snake_case      :					Union[str, Any] ,			_snake_case      :					Tuple		)				->							Optional[Any]:
									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      :					Union[str, Any] ,			_snake_case      :					List[Any] ,			_snake_case      :					List[str] ,			_snake_case      :					Union[str, Any] ,			_snake_case      :					Optional[int] ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Any ,			_snake_case      :					int ,			_snake_case      :					List[str] ,			_snake_case      :					List[Any] ,			)				->							Any:
									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      :					List[str] ,			_snake_case      :					Dict ,			_snake_case      :					Union[str, Any] ,			_snake_case      :					Tuple ,			_snake_case      :					Dict ,			_snake_case      :					Tuple ,			_snake_case      :					Tuple		)				->							List[str]:
									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      :					Any ,			_snake_case      :					str ,			_snake_case      :					Optional[int] ,			_snake_case      :					Any ,			_snake_case      :					str ,			_snake_case      :					Optional[int] ,			_snake_case      :					Any ,			_snake_case      :					Optional[Any]		)				->							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      :					int ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Any ,			_snake_case      :					List[str] ,			_snake_case      :					int ,			_snake_case      :					Any		)				->							Dict:
									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      :					List[Any] ,			_snake_case      :					int ,			_snake_case      :					Tuple ,			_snake_case      :					Any ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Tuple ,			_snake_case      :					Optional[int]		)				->							Optional[Any]:
									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      :					int ,			_snake_case      :					Any ,			_snake_case      :					int ,			_snake_case      :					int ,			_snake_case      :					Optional[int] ,			_snake_case      :					Dict ,			_snake_case      :					Any ,			_snake_case      :					str		)				->							Any:
									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      :					Optional[int] ,			_snake_case      :					List[str] ,			_snake_case      :					Dict ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Tuple ,			_snake_case      :					str ,			_snake_case      :					str ,			_snake_case      :					List[str]		)				->							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      :					str ,			_snake_case      :					List[Any] ,			_snake_case      :					Union[str, Any] ,			_snake_case      :					Dict ,			_snake_case      :					List[Any] ,			_snake_case      :					Dict ,			_snake_case      :					List[Any]		)				->							int:
									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      :					Dict		)				->							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 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      :					Optional[Any] ,			_snake_case      :					Dict ,			_snake_case      :					int ,			_snake_case      :					Union[str, Any]=False		)				->							List[str]:
									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      :					Optional[Any]		)				->							str:
									SCREAMING_SNAKE_CASE__			=					NezhaModelTester(self		)
									SCREAMING_SNAKE_CASE__			=					ConfigTester(self ,			config_class=_snake_case ,			hidden_size=37		)



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							int:
									self.config_tester.run_common_tests()



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



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



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



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



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							str:
									SCREAMING_SNAKE_CASE__			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							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      :					Optional[int]		)				->							List[Any]:
									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      :					Optional[int]		)				->							Optional[Any]:
									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      :					Tuple		)				->							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      :					Dict		)				->							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(_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      :					str		)				->							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		)		)












def       _SCREAMING_SNAKE_CASE (							__snake_case = 1_0_0_0 )				->						int:
       _UpperCAmelCase	,			_UpperCAmelCase							=    1, 1
       _UpperCAmelCase							=    []
       for i in range(1   ,      n + 1 ):
              _UpperCAmelCase							=    prev_numerator + 2 * prev_denominator
              _UpperCAmelCase							=    prev_numerator + prev_denominator
              if len(str(__snake_case ) ) > len(str(__snake_case ) ):
                     result.append(__snake_case )
              _UpperCAmelCase							=    numerator
              _UpperCAmelCase							=    denominator

       return len(__snake_case )


if __name__ == "__main__":
  print(F"{solution() = }")