





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:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=13     ,					_A=7     ,					_A=True     ,					_A=True     ,					_A=True     ,					_A=True     ,					_A=99     ,					_A=32     ,					_A=5     ,					_A=4     ,					_A=37     ,					_A="gelu"     ,					_A=0.1     ,					_A=0.1     ,					_A=128     ,					_A=32     ,					_A=16     ,					_A=2     ,					_A=0.0_2     ,					_A=3     ,					_A=4     ,					_A=None     ,					):
									__A	:						Any       =				parent
									__A	:						List[Any]       =				batch_size
									__A	:						List[Any]       =				seq_length
									__A	:						str       =				is_training
									__A	:						Tuple       =				use_input_mask
									__A	:						List[Any]       =				use_token_type_ids
									__A	:						List[str]       =				use_labels
									__A	:						Any       =				vocab_size
									__A	:						List[Any]       =				hidden_size
									__A	:						Optional[Any]       =				num_hidden_layers
									__A	:						List[str]       =				num_attention_heads
									__A	:						int       =				intermediate_size
									__A	:						Optional[Any]       =				hidden_act
									__A	:						Tuple       =				hidden_dropout_prob
									__A	:						List[str]       =				attention_probs_dropout_prob
									__A	:						Dict       =				max_position_embeddings
									__A	:						Tuple       =				type_vocab_size
									__A	:						List[Any]       =				type_sequence_label_size
									__A	:						Dict       =				initializer_range
									__A	:						List[Any]       =				num_labels
									__A	:						List[Any]       =				num_choices
									__A	:						List[str]       =				scope



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

									__A	:						List[Any]       =				None
									if self.use_input_mask:
														__A	:						Optional[int]       =				random_attention_mask([self.batch_size, self.seq_length]     )

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

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

									__A	:						Optional[Any]       =				self.get_config()

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



				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ):
									(
									    (
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				
									)	:						int       =				self.prepare_config_and_inputs()

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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				NezhaForPreTraining(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						int       =				NezhaForQuestionAnswering(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						str       =				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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				self.num_labels
									__A	:						str       =				NezhaForSequenceClassification(_A     )
									model.to(_A     )
									model.eval()
									__A	:						List[Any]       =				model(_A     ,					attention_mask=_A     ,					token_type_ids=_A     ,					labels=_A     )
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						str       =				self.num_labels
									__A	:						Tuple       =				NezhaForTokenClassification(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						int       =				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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				self.num_choices
									__A	:						Tuple       =				NezhaForMultipleChoice(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						str       =				input_ids.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
									__A	:						List[Any]       =				token_type_ids.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
									__A	:						Optional[Any]       =				input_mask.unsqueeze(1     ).expand(-1     ,					self.num_choices     ,					-1     ).contiguous()
									__A	:						Tuple       =				model(
									    _A     ,					attention_mask=_A     ,					token_type_ids=_A     ,					labels=_A     ,					)
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)     )





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				self.prepare_config_and_inputs()
									(
									    (
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				
									)	:						Optional[Any]       =				config_and_inputs
									__A	:						List[str]       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_torch
class 						_A(					snake_case__				, snake_case__				, snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	(
				    (
				        NezhaModel,
				        NezhaForMaskedLM,
				        NezhaForMultipleChoice,
				        NezhaForNextSentencePrediction,
				        NezhaForPreTraining,
				        NezhaForQuestionAnswering,
				        NezhaForSequenceClassification,
				        NezhaForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase    :  Union[str, Any]					    =	(
				    {
				        '''feature-extraction''': NezhaModel,
				        '''fill-mask''': NezhaForMaskedLM,
				        '''question-answering''': NezhaForQuestionAnswering,
				        '''text-classification''': NezhaForSequenceClassification,
				        '''token-classification''': NezhaForTokenClassification,
				        '''zero-shot''': NezhaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase    :  Union[str, Any]					    =	True



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A=False     ):
									__A	:						Tuple       =				super()._prepare_for_class(_A     ,					_A     ,					return_labels=_A     )

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				NezhaModelTester(self     )
									__A	:						Any       =				ConfigTester(self     ,					config_class=_A     ,					hidden_size=37     )



				def 					UpperCAmelCase_		(			self     ):
									self.config_tester.run_common_tests()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									# This regression test was failing with PyTorch < 1.3
									(
									    (
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				
									)	:						Dict       =				self.model_tester.prepare_config_and_inputs_for_decoder()

									__A	:						Optional[Any]       =				None

									self.model_tester.create_and_check_model_as_decoder(
									    _A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					)



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_next_sequence_prediction(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_A     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__A	:						Dict       =				NezhaModel.from_pretrained(_A     )
														self.assertIsNotNone(_A     )





				@slow
				@require_torch_gpu
				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						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

														__A	:						int       =				True
														__A	:						List[str]       =				model_class(config=_A     )

														__A	:						Dict       =				self._prepare_for_class(_A     ,					_A     )
														__A	:						Optional[Any]       =				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'     )     )
																			__A	:						Optional[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   ):


				"""simple docstring"""


				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				NezhaModel.from_pretrained('sijunhe/nezha-cn-base'     )
									__A	:						Dict       =				torch.tensor([[0, 1, 2, 3, 4, 5]]     )
									__A	:						int       =				torch.tensor([[0, 1, 1, 1, 1, 1]]     )
									with torch.no_grad():
														__A	:						List[str]       =				model(_A     ,					attention_mask=_A     )[0]
									__A	:						Union[str, Any]       =				torch.Size((1, 6, 768)     )
									self.assertEqual(output.shape     ,					_A     )
									__A	:						Union[str, Any]       =				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 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'     )
									__A	:						Optional[int]       =				torch.tensor([[0, 1, 2, 3, 4, 5]]     )
									__A	:						Optional[int]       =				torch.tensor([[1, 1, 1, 1, 1, 1]]     )
									with torch.no_grad():
														__A	:						Optional[int]       =				model(_A     ,					attention_mask=_A     )[0]
									__A	:						Tuple       =				torch.Size((1, 6, 21128)     )
									self.assertEqual(output.shape     ,					_A     )
									__A	:						int       =				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     )     )











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					return str(a  ) == str(a  )[::-1]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					return int(a  ) + int(str(a  )[::-1]  )







def  _SCREAMING_SNAKE_CASE				(   a = 1_00_00  )      ->      int:
					__A	:						int       =				[]
					for num in range(1					,						a  ):
										__A	:						List[str]       =				0
										__A	:						List[Any]       =				num
										while iterations < 50:
															__A	:						str       =				sum_reverse(a  )
															iterations += 1
															if is_palindrome(a  ):
																				break
										else:
															lychrel_nums.append(a  )
					return len(a  )


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




