





'''simple docstring'''



from __future__ import annotations

__magic_name__ :    List[str]			 =      [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						):







  '''simple docstring'''



  _snake_case  		=					[
      [0 for col in range(len(grid[0]							)							)] for row in range(len(snake_case__							)							)
  ]  # the reference grid
  _snake_case  		=					1
  _snake_case  		=					[
      [0 for col in range(len(grid[0]							)							)] for row in range(len(snake_case__							)							)
  ]  # the action grid

  _snake_case  		=					init[0]
  _snake_case  		=					init[1]
  _snake_case  		=					0
  _snake_case  		=					g + heuristic[x][y]  # cost from starting cell to destination cell
  _snake_case  		=					[[f, g, x, y]]

  _snake_case  		=					False  # flag that is set when search is complete
  _snake_case  		=					False  # flag set if we can't find expand

  while not found and not resign:
    if len(snake_case__							) == 0:
      raise ValueError("Algorithm is unable to find solution"							)
    else:  # to choose the least costliest action so as to move closer to the goal
      cell.sort()
      cell.reverse()
      _snake_case  		=					cell.pop()
      _snake_case  		=					next_cell[2]
      _snake_case  		=					next_cell[3]
      _snake_case  		=					next_cell[1]

      if x == goal[0] and y == goal[1]:
        _snake_case  		=					True
      else:
        for i in range(len(snake_case__							)							):  # to try out different valid actions
          _snake_case  		=					x + DIRECTIONS[i][0]
          _snake_case  		=					y + DIRECTIONS[i][1]
          if xa >= 0 and xa < len(snake_case__							) and ya >= 0 and ya < len(grid[0]							):
            if closed[xa][ya] == 0 and grid[xa][ya] == 0:
              _snake_case  		=					g + cost
              _snake_case  		=					ga + heuristic[xa][ya]
              cell.append([fa, ga, xa, ya]							)
              _snake_case  		=					1
              _snake_case  		=					i
  _snake_case  		=					[]
  _snake_case  		=					goal[0]
  _snake_case  		=					goal[1]
  invpath.append([x, y]							)  # we get the reverse path from here
  while x != init[0] or y != init[1]:
    _snake_case  		=					x - DIRECTIONS[action[x][y]][0]
    _snake_case  		=					y - DIRECTIONS[action[x][y]][1]
    _snake_case  		=					xa
    _snake_case  		=					ya
    invpath.append([x, y]							)

  _snake_case  		=					[]
  for i in range(len(snake_case__							)							):
    path.append(invpath[len(snake_case__							) - 1 - i]							)
  return path, action


if __name__ == "__main__":
 __magic_name__ :    Union[str, Any]			 =      [
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 1, 0],
     [0, 0, 0, 0, 1, 0],
 ]

 __magic_name__ :    int			 =      [0, 0]
 # all coordinates are given in format [y,x]
 __magic_name__ :    Any			 =      [len(grid) - 1, len(grid[0]) - 1]
 __magic_name__ :    Optional[int]			 =      1

 # the cost map which pushes the path closer to the goal
 __magic_name__ :    Tuple			 =      [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
 for i in range(len(grid)):
  for j in range(len(grid[0])):
   __magic_name__ :    Tuple			 =      abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    __magic_name__ :    int			 =      99

 __magic_name__ :    List[str]			 =      search(grid, init, goal, cost, heuristic)

 print("""ACTION MAP""")
 for i in range(len(action)):
  print(action[i])

 for i in range(len(path)):
  print(path[i])






"""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)
UpperCamelCase__				:str			   =	logging.getLogger()





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(snake_case__		)
			Path(snake_case__		).open('''w'''		).writelines(snake_case__		)


UpperCamelCase__				:Dict			   =	"""patrickvonplaten/t5-tiny-random"""
UpperCamelCase__				:List[Any]			   =	"""sshleifer/bart-tiny-random"""
UpperCamelCase__				:str			   =	"""sshleifer/tiny-mbart"""

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	Path(self.get_auto_remove_tmp_dir()   ) / '''utest_input.source'''
					_UpperCamelCase					:Union[str, Any]					     =	input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()
					_UpperCamelCase					:int					     =	[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
					_dump_articles(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	str(Path(self.get_auto_remove_tmp_dir()   ) / '''scores.json'''   )
					_UpperCamelCase					:Any					     =	'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					_UpperCamelCase					:Union[str, Any]					     =	f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

					with patch.object(SCREAMING_SNAKE_CASE__					,				'''argv'''					,				SCREAMING_SNAKE_CASE__   ):
								run_generate()
								assert Path(SCREAMING_SNAKE_CASE__   ).exists()
								# os.remove(Path(output_file_name))
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )
		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )





		@parameterized.expand([T5_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	Path(self.get_auto_remove_tmp_dir()   ) / '''utest_input.source'''
					_UpperCamelCase					:Any					     =	input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''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!''',
					    ],
					}

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

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

