



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  DebertaTokenizer
						UpperCamelCase__      :			List[str]      =  True
						UpperCamelCase__      :			Any      =  DebertaTokenizerFast






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													__SCREAMING_SNAKE_CASE									= [
													    'l',
													    'o',
													    'w',
													    'e',
													    'r',
													    's',
													    't',
													    'i',
													    'd',
													    'n',
													    '\u0120',
													    '\u0120l',
													    '\u0120n',
													    '\u0120lo',
													    '\u0120low',
													    'er',
													    '\u0120lowest',
													    '\u0120newer',
													    '\u0120wider',
													    '[UNK]',
													]
													__SCREAMING_SNAKE_CASE									= dict(zip(_A				,							range(len(_A		)		)		)		)
													__SCREAMING_SNAKE_CASE									= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
													__SCREAMING_SNAKE_CASE									= {'unk_token': '[UNK]'}

													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['merges_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as fp:
																				fp.write(json.dumps(_A		) + '\n'		)
													with open(self.merges_file				,							'w'				,							encoding='utf-8'		) as fp:
																				fp.write('\n'.join(_A		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													kwargs.update(self.special_tokens_map		)
													return self.tokenizer_class.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'lower newer'
													__SCREAMING_SNAKE_CASE									= 'lower newer'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_tokenizer()
													__SCREAMING_SNAKE_CASE									= 'lower newer'
													__SCREAMING_SNAKE_CASE									= ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize(_A		)
													self.assertListEqual(_A				,							_A		)

													__SCREAMING_SNAKE_CASE									= tokens + [tokenizer.unk_token]
													__SCREAMING_SNAKE_CASE									= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_tokenizer()
													__SCREAMING_SNAKE_CASE									= tokenizer('Hello'				,							'World'		)
													__SCREAMING_SNAKE_CASE									= [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
													self.assertListEqual(tokd['token_type_ids']				,							_A		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class.from_pretrained('microsoft/deberta-base'		)

													__SCREAMING_SNAKE_CASE									= tokenizer.encode('sequence builders'				,							add_special_tokens=_A		)
													__SCREAMING_SNAKE_CASE									= tokenizer.encode('multi-sequence build'				,							add_special_tokens=_A		)

													__SCREAMING_SNAKE_CASE									= tokenizer.encode(
													    'sequence builders'				,							add_special_tokens=_A				,							add_prefix_space=_A		)
													__SCREAMING_SNAKE_CASE									= tokenizer.encode(
													    'sequence builders'				,							'multi-sequence build'				,							add_special_tokens=_A				,							add_prefix_space=_A		)

													__SCREAMING_SNAKE_CASE									= tokenizer.build_inputs_with_special_tokens(_A		)
													__SCREAMING_SNAKE_CASE									= tokenizer.build_inputs_with_special_tokens(_A				,							_A		)

													assert encoded_sentence == encoded_text_from_decode
													assert encoded_pair == encoded_pair_from_decode






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [self.tokenizer_class]
													if self.test_rust_tokenizer:
																				tokenizer_classes.append(self.rust_tokenizer_class		)

													for tokenizer_class in tokenizer_classes:
																				__SCREAMING_SNAKE_CASE									= tokenizer_class.from_pretrained('microsoft/deberta-base'		)

																				__SCREAMING_SNAKE_CASE									= [
																				    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																				    'ALBERT incorporates two parameter reduction techniques',
																				    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																				    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																				    ' vocabulary embedding.',
																				]

																				__SCREAMING_SNAKE_CASE									= tokenizer(_A				,							padding=_A		)
																				__SCREAMING_SNAKE_CASE									= [tokenizer.decode(_A				,							skip_special_tokens=_A		) for seq in encoding['input_ids']]

																				# fmt: off
																				__SCREAMING_SNAKE_CASE									= {
																				    'input_ids': [
																				        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
																				    ],
																				    'token_type_ids': [
																				        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																				    ],
																				    'attention_mask': [
																				        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																				        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																				    ]
																				}
																				# fmt: on

																				__SCREAMING_SNAKE_CASE									= [
																				    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																				    'ALBERT incorporates two parameter reduction techniques',
																				    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																				    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																				    ' vocabulary embedding.',
																				]

																				self.assertDictEqual(encoding.data				,							_A		)

																				for expected, decoded in zip(_A				,							_A		):
																											self.assertEqual(_A				,							_A		)











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() = }''')






