
from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def  lowerCAmelCase__			(  )				->		tuple[list[int], int]:
			lowerCAmelCase__      :      List[Any]         =		[randint(-1_000       ,							1_000     ) for i in range(10     )]
			lowerCAmelCase__      :      str         =		randint(-5_000       ,							5_000     )
			return (arr, r)


lowerCamelCase__          =		make_dataset()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[int, ...]:
			for triplet in permutations(SCREAMING_SNAKE_CASE_       ,							3     ):
						if sum(SCREAMING_SNAKE_CASE_     ) == target:
									return tuple(sorted(SCREAMING_SNAKE_CASE_     )     )
			return (0, 0, 0)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[int, int, int]:
			arr.sort()
			lowerCAmelCase__      :      List[str]         =		len(SCREAMING_SNAKE_CASE_     )
			for i in range(n - 1     ):
						lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)
def  lowerCAmelCase__			(  )				->		tuple[float, float]:
			lowerCAmelCase__      :      Union[str, Any]         =		'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			lowerCAmelCase__      :      Tuple         =		'\ntriplet_sum1(*dataset)\n'
			lowerCAmelCase__      :      str         =		'\ntriplet_sum2(*dataset)\n'
			lowerCAmelCase__      :      int         =		repeat(setup=SCREAMING_SNAKE_CASE_       ,							stmt=SCREAMING_SNAKE_CASE_       ,							repeat=5       ,							number=10_000     )
			lowerCAmelCase__      :      str         =		repeat(setup=SCREAMING_SNAKE_CASE_       ,							stmt=SCREAMING_SNAKE_CASE_       ,							repeat=5       ,							number=10_000     )
			return (min(SCREAMING_SNAKE_CASE_     ), min(SCREAMING_SNAKE_CASE_     ))


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					lowerCamelCase__          =		solution_times()
					print(F"""The time for naive implementation is {times[0]}.""")
					print(F"""The time for optimized implementation is {times[1]}.""")

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					:      str    ,     a					:      Optional[Any]    ,     a					:      int=13    ,     a					:      str=7    ,     a					:      str=True    ,     a					:      List[str]=True    ,     a					:      Optional[Any]=True    ,     a					:      int=True    ,     a					:      List[Any]=99    ,     a					:      List[Any]=32    ,     a					:      Tuple=5    ,     a					:      Any=4    ,     a					:      Optional[int]=37    ,     a					:      Tuple="gelu"    ,     a					:      Any=0.1    ,     a					:      int=0.1    ,     a					:      List[Any]=128    ,     a					:      Union[str, Any]=32    ,     a					:      Union[str, Any]=16    ,     a					:      Dict=2    ,     a					:      List[Any]=0.0_2    ,     a					:      Optional[Any]=3    ,     a					:      List[Any]=4    ,     a					:      Optional[int]=None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		parent
							lowerCAmelCase__      :      Dict         =		batch_size
							lowerCAmelCase__      :      Optional[Any]         =		seq_length
							lowerCAmelCase__      :      Optional[Any]         =		is_training
							lowerCAmelCase__      :      Union[str, Any]         =		use_input_mask
							lowerCAmelCase__      :      List[Any]         =		use_token_type_ids
							lowerCAmelCase__      :      str         =		use_labels
							lowerCAmelCase__      :      Optional[Any]         =		vocab_size
							lowerCAmelCase__      :      Union[str, Any]         =		hidden_size
							lowerCAmelCase__      :      Optional[int]         =		num_hidden_layers
							lowerCAmelCase__      :      Optional[int]         =		num_attention_heads
							lowerCAmelCase__      :      List[Any]         =		intermediate_size
							lowerCAmelCase__      :      List[str]         =		hidden_act
							lowerCAmelCase__      :      List[Any]         =		hidden_dropout_prob
							lowerCAmelCase__      :      Optional[int]         =		attention_probs_dropout_prob
							lowerCAmelCase__      :      Dict         =		max_position_embeddings
							lowerCAmelCase__      :      Any         =		type_vocab_size
							lowerCAmelCase__      :      Any         =		type_sequence_label_size
							lowerCAmelCase__      :      List[Any]         =		initializer_range
							lowerCAmelCase__      :      Dict         =		num_labels
							lowerCAmelCase__      :      Any         =		num_choices
							lowerCAmelCase__      :      Union[str, Any]         =		scope






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size   )

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

							lowerCAmelCase__      :      Tuple         =		None
							if self.use_token_type_ids:
										lowerCAmelCase__      :      List[Any]         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size   )

							lowerCAmelCase__      :      Optional[int]         =		None
							lowerCAmelCase__      :      Optional[Any]         =		None
							lowerCAmelCase__      :      Optional[int]         =		None
							if self.use_labels:
										lowerCAmelCase__      :      Any         =		ids_tensor([self.batch_size]    ,     self.type_sequence_label_size   )
										lowerCAmelCase__      :      Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels   )
										lowerCAmelCase__      :      List[Any]         =		ids_tensor([self.batch_size]    ,     self.num_choices   )

							lowerCAmelCase__      :      Dict         =		self.get_config()

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






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return NezhaConfig(
							    vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     is_decoder=a    ,     initializer_range=self.initializer_range    ,     )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      List[Any]         =		self.prepare_config_and_inputs()

							lowerCAmelCase__      :      List[Any]         =		True
							lowerCAmelCase__      :      Tuple         =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							lowerCAmelCase__      :      Tuple         =		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]    ,     a					:      Optional[int]    ,     a					:      Tuple    ,     a					:      Optional[int]    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[str]    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		NezhaModel(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Dict         =		model(a    ,     attention_mask=a    ,     token_type_ids=a   )
							lowerCAmelCase__      :      List[str]         =		model(a    ,     token_type_ids=a   )
							lowerCAmelCase__      :      Any         =		model(a   )
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)   )
							self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size)   )






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Union[str, Any]    ,     a					:      Dict    ,     a					:      List[Any]    ,     a					:      Optional[Any]    ,     a					:      int    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[str]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		True
							lowerCAmelCase__      :      Optional[int]         =		NezhaModel(a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Any         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     encoder_hidden_states=a    ,     encoder_attention_mask=a    ,     )
							lowerCAmelCase__      :      Dict         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     encoder_hidden_states=a    ,     )
							lowerCAmelCase__      :      List[str]         =		model(a    ,     attention_mask=a    ,     token_type_ids=a   )
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)   )
							self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size)   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      str    ,     a					:      List[str]    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		NezhaForMaskedLM(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Dict         =		model(a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a   )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)   )






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Optional[int]    ,     a					:      List[Any]    ,     a					:      int    ,     a					:      List[str]    ,     a					:      Union[str, Any]    ,     a					:      int    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		NezhaForNextSentencePrediction(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      str         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, 2)   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Optional[int]    ,     a					:      str    ,     a					:      List[str]    ,     a					:      int    ,     a					:      Dict    ,     a					:      Optional[Any]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		NezhaForPreTraining(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Optional[int]         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a    ,     next_sentence_label=a    ,     )
							self.parent.assertEqual(result.prediction_logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)   )
							self.parent.assertEqual(result.seq_relationship_logits.shape    ,     (self.batch_size, 2)   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Dict    ,     a					:      List[str]    ,     a					:      Any    ,     a					:      Any    ,     a					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		NezhaForQuestionAnswering(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Tuple         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     start_positions=a    ,     end_positions=a    ,     )
							self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length)   )
							self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length)   )






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      str    ,     a					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      Optional[Any]    ,     a					:      Dict    ,     a					:      str    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.num_labels
							lowerCAmelCase__      :      Optional[Any]         =		NezhaForSequenceClassification(a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Tuple         =		model(a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a   )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)   )






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Dict    ,     a					:      str    ,     a					:      Optional[Any]    ,     a					:      Optional[int]    ,     a					:      List[str]    ,     a					:      Dict    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.num_labels
							lowerCAmelCase__      :      str         =		NezhaForTokenClassification(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      Any         =		model(a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a   )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Tuple    ,     a					:      List[Any]    ,     a					:      Optional[int]    ,     a					:      Optional[int]    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.num_choices
							lowerCAmelCase__      :      Any         =		NezhaForMultipleChoice(config=a   )
							model.to(a   )
							model.eval()
							lowerCAmelCase__      :      int         =		input_ids.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      str         =		token_type_ids.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      Optional[Any]         =		input_mask.unsqueeze(1   ).expand(-1    ,     self.num_choices    ,     -1   ).contiguous()
							lowerCAmelCase__      :      Any         =		model(
							    a    ,     attention_mask=a    ,     token_type_ids=a    ,     labels=a    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      Any         =		config_and_inputs
							lowerCAmelCase__      :      str         =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   unittest.TestCase	):
				lowercase			    =  (
				    (
				        NezhaModel,
				        NezhaForMaskedLM,
				        NezhaForMultipleChoice,
				        NezhaForNextSentencePrediction,
				        NezhaForPreTraining,
				        NezhaForQuestionAnswering,
				        NezhaForSequenceClassification,
				        NezhaForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase			    =  (
				    {
				        'feature-extraction': NezhaModel,
				        'fill-mask': NezhaForMaskedLM,
				        'question-answering': NezhaForQuestionAnswering,
				        'text-classification': NezhaForSequenceClassification,
				        'token-classification': NezhaForTokenClassification,
				        'zero-shot': NezhaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase			    =  True






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Tuple    ,     a					:      int    ,     a					:      Dict=False   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		super()._prepare_for_class(a    ,     a    ,     return_labels=a   )

							if return_labels:
										if model_class in get_values(a   ):
													lowerCAmelCase__      :      Tuple         =		torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)    ,     dtype=torch.long    ,     device=a   )
													lowerCAmelCase__      :      Optional[Any]         =		torch.zeros(
													    self.model_tester.batch_size    ,     dtype=torch.long    ,     device=a   )
							return inputs_dict






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		NezhaModelTester(self   )
							lowerCAmelCase__      :      Optional[int]         =		ConfigTester(self    ,     config_class=a    ,     hidden_size=37   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							self.config_tester.run_common_tests()






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							(
							    (
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				(
							    lowerCAmelCase__
							)      ,				
							)      :      str         =		self.model_tester.prepare_config_and_inputs_for_decoder()

							lowerCAmelCase__      :      str         =		None

							self.model_tester.create_and_check_model_as_decoder(
							    a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     a    ,     )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*a   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_next_sequence_prediction(*a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*a   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*a   )






				@slow
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__      :      Optional[Any]         =		NezhaModel.from_pretrained(a   )
										self.assertIsNotNone(a   )






				@slow
				@require_torch_gpu
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		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

										lowerCAmelCase__      :      Dict         =		True
										lowerCAmelCase__      :      Any         =		model_class(config=a   )

										lowerCAmelCase__      :      Union[str, Any]         =		self._prepare_for_class(a    ,     a   )
										lowerCAmelCase__      :      int         =		torch.jit.trace(
										    a    ,     (inputs_dict['input_ids'].to('cpu'   ), inputs_dict['attention_mask'].to('cpu'   ))   )

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







@require_torch
class      A__							(     unittest.TestCase	):






				@slow
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		NezhaModel.from_pretrained('sijunhe/nezha-cn-base'   )
							lowerCAmelCase__      :      Any         =		torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowerCAmelCase__      :      Union[str, Any]         =		torch.tensor([[0, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     attention_mask=a   )[0]
							lowerCAmelCase__      :      Union[str, Any]         =		torch.Size((1, 6, 768)   )
							self.assertEqual(output.shape    ,     a   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]   )

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






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[0, 1, 2, 3, 4, 5]]   )
							lowerCAmelCase__      :      Optional[int]         =		torch.tensor([[1, 1, 1, 1, 1, 1]]   )
							with torch.no_grad():
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     attention_mask=a   )[0]
							lowerCAmelCase__      :      int         =		torch.Size((1, 6, 21_128)   )
							self.assertEqual(output.shape    ,     a   )
							lowerCAmelCase__      :      List[Any]         =		torch.tensor(
							    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]   )

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