'''simple docstring'''




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_A								=   logging.getLogger()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					'\n'.join(SCREAMING_SNAKE_CASE_							)
     Path(SCREAMING_SNAKE_CASE_							).open('w'							).writelines(SCREAMING_SNAKE_CASE_							)


_A								=   'patrickvonplaten/t5-tiny-random'
_A								=   'sshleifer/bart-tiny-random'
_A								=   'sshleifer/tiny-mbart'

_A								=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					Path(self.get_auto_remove_tmp_dir()				) / 'utest_input.source'
        lowercase_					:       Optional[int]     				=					input_file_name.parent / 'utest_output.txt'
        assert not output_file_name.exists()
        lowercase_					:       Optional[Any]     				=					[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
        _dump_articles(_a       ,				_a				)

        lowercase_					:       Optional[int]     				=					str(Path(self.get_auto_remove_tmp_dir()				) / 'scores.json'				)
        lowercase_					:       List[str]     				=					'translation_en_to_de' if model == T5_TINY else 'summarization'
        lowercase_					:       Optional[int]     				=					f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

        with patch.object(_a       ,				'argv'       ,				_a				):
             run_generate()
             assert Path(_a				).exists()
             # os.remove(Path(output_file_name))
   def 		_lowerCamelCase (self				)    ->     Tuple:
        self.run_eval_tester(_a				)
   @parameterized.expand([BART_TINY, MBART_TINY]				)
   @slow
   def 		_lowerCamelCase (self       ,				_a				)    ->     Any:
        self.run_eval_tester(_a				)





   @parameterized.expand([T5_TINY, MBART_TINY]				)
   @slow
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Optional[int]     				=					Path(self.get_auto_remove_tmp_dir()				) / 'utest_input.source'
        lowercase_					:       str     				=					input_file_name.parent / 'utest_output.txt'
        assert not output_file_name.exists()

        lowercase_					:       List[str]     				=					{
            'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
            'de': [
                'Maschinelles Lernen ist großartig, oder?',
                'Ich esse gerne Bananen',
                'Morgen ist wieder ein toller Tag!',
            ],
        }

        lowercase_					:       Tuple     				=					Path(self.get_auto_remove_tmp_dir()				)
        lowercase_					:       List[str]     				=					str(tmp_dir / 'scores.json'				)
        lowercase_					:       Union[str, Any]     				=					str(tmp_dir / 'val.target'				)
        _dump_articles(_a       ,				text['en']				)
        _dump_articles(_a       ,				text['de']				)
        lowercase_					:       str     				=					'translation_en_to_de' if model == T5_TINY else 'summarization'
        lowercase_					:       List[Any]     				=					f'''
            run_eval_search.py
            {model}
            {str(_a				)}
            {str(_a				)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
        testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']				)

        with patch.object(_a       ,				'argv'       ,				_a				):
             with CaptureStdout() as cs:
                  run_search()
             lowercase_					:       Tuple     				=					[' num_beams | length_penalty', model, 'Best score args']
             lowercase_					:       List[Any]     				=					['Info']
             if "translation" in task:
                  expected_strings.append('bleu'				)
             else:
                  expected_strings.extend(_a				)
             for w in expected_strings:
                  assert w in cs.out
             for w in un_expected_strings:
                  assert w not in cs.out
             assert Path(_a				).exists()
             os.remove(Path(_a				)				)





'''simple docstring'''




import os

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(grid[0]							)
     lowercase_					:       Union[str, Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       int     				=					0
     lowercase_					:       Union[str, Any]     				=					0

     # Check vertically, horizontally, diagonally at the same time (only works
     # for nxn grid)
     for i in range(SCREAMING_SNAKE_CASE_							):
          for j in range(n_rows - 3							):
               lowercase_					:       Dict     				=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
               lowercase_					:       Optional[Any]     				=					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:
                    lowercase_					:       Tuple     				=					(
                        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:
                    lowercase_					:       int     				=					(
                        grid[i][j]
                        * grid[i - 1][j + 1]
                        * grid[i - 2][j + 2]
                        * grid[i - 3][j + 3]
                    )

               lowercase_					:       Optional[int]     				=					max(
                   SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               if max_product > largest:
                    lowercase_					:       List[Any]     				=					max_product

     return largest

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					[]
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/grid.txt'							) as file:
          for line in file:
               grid.append(line.strip('\n'							).split(' '							)							)

     lowercase_					:       Dict     				=					[[int(SCREAMING_SNAKE_CASE_							) for i in grid[j]] for j in range(len(SCREAMING_SNAKE_CASE_							)							)]

     return largest_product(SCREAMING_SNAKE_CASE_							)


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




