



import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    NystromformerForMaskedLM,
			    NystromformerForMultipleChoice,
			    NystromformerForQuestionAnswering,
			    NystromformerForSequenceClassification,
			    NystromformerForTokenClassification,
			    NystromformerModel,
			)
			from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_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=512				,							_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 NystromformerConfig(
													    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				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NystromformerModel(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)		)






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NystromformerForMaskedLM(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									= NystromformerForQuestionAnswering(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									= NystromformerForSequenceClassification(_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									= NystromformerForTokenClassification(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									= NystromformerForMultipleChoice(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_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			str      =  (
						    (
						        NystromformerModel,
						        NystromformerForMaskedLM,
						        NystromformerForMultipleChoice,
						        NystromformerForQuestionAnswering,
						        NystromformerForSequenceClassification,
						        NystromformerForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase__      :			Tuple      =  (
						    {
						        '''feature-extraction''': NystromformerModel,
						        '''fill-mask''': NystromformerForMaskedLM,
						        '''question-answering''': NystromformerForQuestionAnswering,
						        '''text-classification''': NystromformerForSequenceClassification,
						        '''token-classification''': NystromformerForTokenClassification,
						        '''zero-shot''': NystromformerForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase__      :			Union[str, Any]      =  False
						UpperCamelCase__      :			List[Any]      =  False






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NystromformerModelTester(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 type in ["absolute", "relative_key", "relative_key_query"]:
																				__SCREAMING_SNAKE_CASE									= type
																				self.model_tester.create_and_check_model(*_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_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 NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									= NystromformerModel.from_pretrained(_A		)
																				self.assertIsNotNone(_A		)







@require_torch
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= NystromformerModel.from_pretrained('uw-madison/nystromformer-512'		)
													__SCREAMING_SNAKE_CASE									= torch.tensor([[0, 1, 2, 3, 4, 5]]		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(_A		)[0]

													__SCREAMING_SNAKE_CASE									= torch.Size((1, 6, 768)		)
													self.assertEqual(output.shape				,							_A		)

													__SCREAMING_SNAKE_CASE									= torch.tensor(
													    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]		)

													self.assertTrue(torch.allclose(output[:, :3, :3]				,							_A				,							atol=1e-4		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'the [MASK] of Belgium is Brussels'

													__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'		)
													__SCREAMING_SNAKE_CASE									= NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'		)

													__SCREAMING_SNAKE_CASE									= tokenizer(_A				,							return_tensors='pt'		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(encoding.input_ids		).logits

													__SCREAMING_SNAKE_CASE									= token_logits[:, 2, :].argmax(-1		)[0]

													self.assertEqual(tokenizer.decode(_A		)				,							'capital'		)











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() = }''')






