






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors

@require_tokenizers
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = MvpTokenizer
				_UpperCamelCase					:       Optional[Any]							       = MvpTokenizerFast
				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       Dict							       = filter_roberta_detectors


				def 						__a					(					self      ):
											super().setUp()
											_lowercase							:   Optional[int] 							=   [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											_lowercase							:   str 							=   dict(zip(_lowerCAmelCase ,						range(len(_lowerCAmelCase      )      )      )      )
											_lowercase							:   Optional[int] 							=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											_lowercase							:   Optional[Any] 							=   {'unk_token': '<unk>'}

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


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase      ):
											return "lower newer", "lower newer"


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   int 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowercase							:   Tuple 							=   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[Any] 							=   tokenizer(_lowerCAmelCase ,						max_length=len(_lowerCAmelCase      ) ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )

																		self.assertEqual((2, 9) ,						batch.input_ids.shape      )
																		self.assertEqual((2, 9) ,						batch.attention_mask.shape      )
																		_lowercase							:   str 							=   batch.input_ids.tolist()[0]
																		self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )
																		# Test that special tokens are reset


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   str 							=   tokenizer(_lowerCAmelCase ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids' ,						_lowerCAmelCase      )
																		self.assertIn('attention_mask' ,						_lowerCAmelCase      )
																		self.assertNotIn('labels' ,						_lowerCAmelCase      )
																		self.assertNotIn('decoder_attention_mask' ,						_lowerCAmelCase      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[str] 							=   tokenizer(text_target=_lowerCAmelCase ,						max_length=3_2 ,						padding='max_length' ,						return_tensors='pt'      )
																		self.assertEqual(3_2 ,						targets['input_ids'].shape[1]      )


				@require_torch
				def 						__a					(					self      ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[int] 							=   tokenizer(
																		    ['I am a small frog' * 1_0_2_4, 'I am a small frog'] ,						padding=_lowerCAmelCase ,						truncation=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )
																		self.assertEqual(batch.input_ids.shape ,						(2, 1_0_2_4)      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   ['A long paragraph for summarization.']
											_lowercase							:   Any 							=   [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[Any] 							=   tokenizer(_lowerCAmelCase ,						text_target=_lowerCAmelCase ,						return_tensors='pt'      )
																		_lowercase							:   Any 							=   inputs['input_ids']
																		_lowercase							:   Union[str, Any] 							=   inputs['labels']
																		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()      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									_lowercase							:   Optional[int] 							=   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   self.tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   'A, <mask> AllenNLP sentence.'
																									_lowercase							:   Union[str, Any] 							=   tokenizer_r.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   tokenizer_p.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['token_type_ids']      ) ,						sum(tokens_p['token_type_ids']      )      )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ) ,						sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ) ,						)

																									_lowercase							:   Dict 							=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																									_lowercase							:   Dict 							=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																									self.assertSequenceEqual(tokens_r['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

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





'''simple docstring'''

import os



def snake_case_      (					__snake_case  :							List[Any])			->					str:
   lowerCAmelCase_			=      len(grid[0])
   lowerCAmelCase_			=      len(__snake_case)

   lowerCAmelCase_			=      0
   lowerCAmelCase_			=      0
   lowerCAmelCase_			=      0

   # Check vertically, horizontally, diagonally at the same time (only works
   # for nxn grid)
   for i in range(__snake_case):
      for j in range(n_rows - 3):
         lowerCAmelCase_			=      grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
         lowerCAmelCase_			=      grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

         # Left-to-right diagonal (\) product
         if i < n_columns - 3:
            lowerCAmelCase_			=      (
                grid[i][j]
                * grid[i + 1][j + 1]
                * grid[i + 2][j + 2]
                * grid[i + 3][j + 3]
            )

         # Right-to-left diagonal(/) product
         if i > 2:
            lowerCAmelCase_			=      (
                grid[i][j]
                * grid[i - 1][j + 1]
                * grid[i - 2][j + 2]
                * grid[i - 3][j + 3]
            )

         lowerCAmelCase_			=      max(
             __snake_case	,   __snake_case	,   __snake_case	,   __snake_case)
         if max_product > largest:
            lowerCAmelCase_			=      max_product

   return largest



def snake_case_      (					)			->					List[Any]:
   lowerCAmelCase_			=      []
   with open(os.path.dirname(__snake_case) + '''/grid.txt''') as file:
      for line in file:
         grid.append(line.strip('''\n''').split(''' '''))

   lowerCAmelCase_			=      [[int(__snake_case) for i in grid[j]] for j in range(len(__snake_case))]

   return largest_product(__snake_case)


if __name__ == "__main__":
 print(solution())





