






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	__SCREAMING_SNAKE_CASE(						__lowerCamelCase     ,	unittest.TestCase						):
							_UpperCAmelCase			     =							LEDTokenizer
							_UpperCAmelCase			     =							LEDTokenizerFast
							_UpperCAmelCase			     =							True
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		Union[str, Any]:
											super().setUp()
											snake_case__						=  [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											snake_case__						=  dict(zip(UpperCamelCase							,						range(len(UpperCamelCase    )    )    )    )
											snake_case__						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											snake_case__						=  {'unk_token': '<unk>'}

											snake_case__						=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['vocab_file']    )
											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(UpperCamelCase    ) + '\n'    )
											with open(self.merges_file							,						'w'							,						encoding='utf-8'    ) as fp:
															fp.write('\n'.join(UpperCamelCase    )    )
							def lowerCAmelCase_			(  self:   Optional[int]							,						**UpperCamelCase:   Any    )		->		List[Any]:
											kwargs.update(self.special_tokens_map    )
											return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCamelCase    )
							def lowerCAmelCase_			(  self:   int							,						**UpperCamelCase:   List[Any]    )		->		Optional[Any]:
											kwargs.update(self.special_tokens_map    )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCamelCase    )
							def lowerCAmelCase_			(  self:   Optional[Any]							,						UpperCamelCase:   Union[str, Any]    )		->		List[str]:
											return "lower newer", "lower newer"
							@cached_property
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		List[str]:
											return LEDTokenizer.from_pretrained('allenai/led-base-16384'    )
							@cached_property
							def lowerCAmelCase_			(  self:   int    )		->		Any:
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'    )
							@require_torch
							def lowerCAmelCase_			(  self:   Tuple    )		->		Union[str, Any]:
											snake_case__						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case__						=  [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  tokenizer(UpperCamelCase							,						max_length=len(UpperCamelCase    )							,						padding=UpperCamelCase							,						return_tensors='pt'    )
															self.assertIsInstance(UpperCamelCase							,						UpperCamelCase    )

															self.assertEqual((2, 9)							,						batch.input_ids.shape    )
															self.assertEqual((2, 9)							,						batch.attention_mask.shape    )
															snake_case__						=  batch.input_ids.tolist()[0]
															self.assertListEqual(UpperCamelCase							,						UpperCamelCase    )
							@require_torch
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		List[str]:
											snake_case__						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  tokenizer(UpperCamelCase							,						padding=UpperCamelCase							,						return_tensors='pt'    )
															self.assertIn('input_ids'							,						UpperCamelCase    )
															self.assertIn('attention_mask'							,						UpperCamelCase    )
															self.assertNotIn('labels'							,						UpperCamelCase    )
															self.assertNotIn('decoder_attention_mask'							,						UpperCamelCase    )
							@require_torch
							def lowerCAmelCase_			(  self:   int    )		->		Optional[int]:
											snake_case__						=  [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  tokenizer(text_target=UpperCamelCase							,						max_length=32							,						padding='max_length'							,						return_tensors='pt'    )
															self.assertEqual(32							,						targets['input_ids'].shape[1]    )
							@require_torch
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Any:






											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  tokenizer(
															    ['I am a small frog' * 10_24, 'I am a small frog']							,						padding=UpperCamelCase							,						truncation=UpperCamelCase							,						return_tensors='pt'    )
															self.assertIsInstance(UpperCamelCase							,						UpperCamelCase    )
															self.assertEqual(batch.input_ids.shape							,						(2, 51_22)    )
							@require_torch
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Any:
											snake_case__						=  ['A long paragraph for summarization.']
											snake_case__						=  [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  tokenizer(UpperCamelCase							,						return_tensors='pt'    )
															snake_case__						=  tokenizer(text_target=UpperCamelCase							,						return_tensors='pt'    )
															snake_case__						=  inputs['input_ids']
															snake_case__						=  targets['input_ids']
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )
							@require_torch
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		int:






											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															snake_case__						=  ['Summary of the text.', 'Another summary.']
															snake_case__						=  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

															snake_case__						=  tokenizer(UpperCamelCase							,						padding=UpperCamelCase    )
															snake_case__						=  [[0] * len(UpperCamelCase    ) for x in encoded_output['input_ids']]
															snake_case__						=  tokenizer.pad(UpperCamelCase    )
															self.assertSequenceEqual(outputs['global_attention_mask']							,						UpperCamelCase    )
							def lowerCAmelCase_			(  self:   Tuple    )		->		Any:
											pass


							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Tuple:






											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																			snake_case__						=  self.rust_tokenizer_class.from_pretrained(UpperCamelCase							,						**UpperCamelCase    )
																			snake_case__						=  self.tokenizer_class.from_pretrained(UpperCamelCase							,						**UpperCamelCase    )
																			snake_case__						=  'A, <mask> AllenNLP sentence.'
																			snake_case__						=  tokenizer_r.encode_plus(UpperCamelCase							,						add_special_tokens=UpperCamelCase							,						return_token_type_ids=UpperCamelCase    )
																			snake_case__						=  tokenizer_p.encode_plus(UpperCamelCase							,						add_special_tokens=UpperCamelCase							,						return_token_type_ids=UpperCamelCase    )
																			self.assertEqual(sum(tokens_r['token_type_ids']    )							,						sum(tokens_p['token_type_ids']    )    )
																			self.assertEqual(
																			    sum(tokens_r['attention_mask']    ) / len(tokens_r['attention_mask']    )							,						sum(tokens_p['attention_mask']    ) / len(tokens_p['attention_mask']    )							,						)

																			snake_case__						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
																			snake_case__						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )
																			self.assertSequenceEqual(tokens_p['input_ids']							,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
																			self.assertSequenceEqual(tokens_r['input_ids']							,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

																			self.assertSequenceEqual(
																			    UpperCamelCase							,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
																			self.assertSequenceEqual(
																			    UpperCamelCase							,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )






import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


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