




'''simple docstring'''



from __future__ import annotations

UpperCamelCase_    	=				[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def   lowercase__(			__UpperCamelCase:					list[list[int]]      ,__UpperCamelCase:					list[int]      ,__UpperCamelCase:					list[int]      ,__UpperCamelCase:					int      ,__UpperCamelCase:					list[list[int]]      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__UpperCamelCase    )    )
      ]  # the reference grid
      SCREAMING_SNAKE_CASE   :      int    					=						1
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          [0 for col in range(len(grid[0]    )    )] for row in range(len(__UpperCamelCase    )    )
      ]  # the action grid

      SCREAMING_SNAKE_CASE   :      Tuple    					=						init[0]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						init[1]
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						g + heuristic[x][y]  # cost from starting cell to destination cell
      SCREAMING_SNAKE_CASE   :      int    					=						[[f, g, x, y]]

      SCREAMING_SNAKE_CASE   :      List[str]    					=						False  # flag that is set when search is complete
      SCREAMING_SNAKE_CASE   :      Dict    					=						False  # flag set if we can't find expand

      while not found and not resign:
            if len(__UpperCamelCase    ) == 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()
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						cell.pop()
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						next_cell[2]
                  SCREAMING_SNAKE_CASE   :      Dict    					=						next_cell[3]
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						next_cell[1]

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

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


if __name__ == "__main__":
      UpperCamelCase_    	=				[
          [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],
      ]

      UpperCamelCase_    	=				[0, 0]
      # all coordinates are given in format [y,x]
      UpperCamelCase_    	=				[len(grid) - 1, len(grid[0]) - 1]
      UpperCamelCase_    	=				1

      # the cost map which pushes the path closer to the goal
      UpperCamelCase_    	=				[[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])):
                  UpperCamelCase_    	=				abs(i - goal[0]) + abs(j - goal[1])
                  if grid[i][j] == 1:
                        # added extra penalty in the heuristic map
                        UpperCamelCase_    	=				9_9

      UpperCamelCase_	,    UpperCamelCase_    	=				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 unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



