



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        UpperCAmelCase_  :

						'''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				,							):







													'''simple docstring'''

													__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		):







													'''simple docstring'''

													__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		):







													'''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 	_A     (   self		):







													'''simple docstring'''

													(
													    (
													    __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				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaModel(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(_A				,							attention_mask=_A				,							token_type_ids=_A		)
													__SCREAMING_SNAKE_CASE									= model(_A				,							token_type_ids=_A		)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= True
													__SCREAMING_SNAKE_CASE									= NezhaModel(_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(
													    _A				,							attention_mask=_A				,							token_type_ids=_A				,							encoder_hidden_states=_A				,							encoder_attention_mask=_A				,							)
													__SCREAMING_SNAKE_CASE									= model(
													    _A				,							attention_mask=_A				,							token_type_ids=_A				,							encoder_hidden_states=_A				,							)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaForMaskedLM(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaForNextSentencePrediction(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(
													    _A				,							attention_mask=_A				,							token_type_ids=_A				,							labels=_A				,							)
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, 2)		)






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaForPreTraining(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaForQuestionAnswering(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.num_labels
													__SCREAMING_SNAKE_CASE									= NezhaForSequenceClassification(_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= model(_A				,							attention_mask=_A				,							token_type_ids=_A				,							labels=_A		)
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)		)






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.num_labels
													__SCREAMING_SNAKE_CASE									= NezhaForTokenClassification(config=_A		)
													model.to(_A		)
													model.eval()
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.num_choices
													__SCREAMING_SNAKE_CASE									= NezhaForMultipleChoice(config=_A		)
													model.to(_A		)
													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(
													    _A				,							attention_mask=_A				,							token_type_ids=_A				,							labels=_A				,							)
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)		)






						def 	_A     (   self		):







													'''simple docstring'''

													__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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




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






						def 	_A     (   self				,							_A				,							_A				,							_A=False		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= super()._prepare_for_class(_A				,							_A				,							return_labels=_A		)

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NezhaModelTester(self		)
													__SCREAMING_SNAKE_CASE									= ConfigTester(self				,							config_class=_A				,							hidden_size=37		)






						def 	_A     (   self		):







													'''simple docstring'''

													self.config_tester.run_common_tests()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													(
													    (
													    __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(
													    _A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_next_sequence_prediction(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*_A		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									= NezhaModel.from_pretrained(_A		)
																				self.assertIsNotNone(_A		)






						@slow
						@require_torch_gpu
						def 	_A     (   self		):







													'''simple docstring'''

													__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=_A		)

																				__SCREAMING_SNAKE_CASE									= self._prepare_for_class(_A				,							_A		)
																				__SCREAMING_SNAKE_CASE									= 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'		)		)
																											__SCREAMING_SNAKE_CASE									= 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        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							attention_mask=_A		)[0]
													__SCREAMING_SNAKE_CASE									= torch.Size((1, 6, 768)		)
													self.assertEqual(output.shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__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(_A				,							attention_mask=_A		)[0]
													__SCREAMING_SNAKE_CASE									= torch.Size((1, 6, 21_128)		)
													self.assertEqual(output.shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= 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		)		)











import os






def      __lowercase       (			a__ = "input.txt"       )    ->			int:

							with open(os.path.join(os.path.dirname(a__       )							,       a__       )       ) as input_file:
														__SCREAMING_SNAKE_CASE									= [
														    [int(a__       ) for element in line.split(','       )]
														    for line in input_file.readlines()
														]

							__SCREAMING_SNAKE_CASE									= len(a__       )
							__SCREAMING_SNAKE_CASE									= len(matrix[0]       )

							__SCREAMING_SNAKE_CASE									= [[-1 for _ in range(a__       )] for _ in range(a__       )]
							for i in range(a__       ):
														__SCREAMING_SNAKE_CASE									= matrix[i][0]

							for j in range(1							,       a__       ):
														for i in range(a__       ):
																					__SCREAMING_SNAKE_CASE									= minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1							,       a__       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i - 1][j] + matrix[i][j]       )

														for i in range(rows - 2							,       -1							,       -1       ):
																					__SCREAMING_SNAKE_CASE									= min(
																					    minimal_path_sums[i][j]							,       minimal_path_sums[i + 1][j] + matrix[i][j]       )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums       )


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






