

from __future__ import annotations

UpperCAmelCase__ :     Optional[int]				    =    [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A						,    )     ->						tuple[list[list[int]], list[list[int]]]:
      SCREAMING_SNAKE_CASE :      List[str]       =   [
          [0 for col in range(len(grid[0]			)			)] for row in range(len(_A			)			)
      ]  # the reference grid
      SCREAMING_SNAKE_CASE :      Any       =   1
      SCREAMING_SNAKE_CASE :      List[str]       =   [
          [0 for col in range(len(grid[0]			)			)] for row in range(len(_A			)			)
      ]  # the action grid

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

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

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

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

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


if __name__ == "__main__":
  UpperCAmelCase__ :     Tuple				    =    [
      [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__ :     List[str]				    =    [0, 0]
  # all coordinates are given in format [y,x]
  UpperCAmelCase__ :     List[str]				    =    [len(grid) - 1, len(grid[0]) - 1]
  UpperCAmelCase__ :     int				    =    1

  # the cost map which pushes the path closer to the goal
  UpperCAmelCase__ :     Optional[Any]				    =    [[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__ :     Optional[Any]				    =    abs(i - goal[0]) + abs(j - goal[1])
      if grid[i][j] == 1:
        # added extra penalty in the heuristic map
        UpperCAmelCase__ :     Optional[int]				    =    99

  UpperCAmelCase__    ,   UpperCAmelCase__ :     int				    =    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])







import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase__ :     int				    =    get_tests_dir("""fixtures""")







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   mock.Mock()
        SCREAMING_SNAKE_CASE :      Optional[int]       =   5_0_0
        SCREAMING_SNAKE_CASE :      List[str]       =   {}
        SCREAMING_SNAKE_CASE :      List[Any]       =   HTTPError
        SCREAMING_SNAKE_CASE :      int       =   {}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE :      List[Any]       =   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch("""requests.Session.request"""				,				return_value=UpperCAmelCase__		) as mock_head:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
              # This check we did call the fake head request
              mock_head.assert_called()




  def  _lowercase      (			self    : Optional[Any]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   WavaVecaFeatureExtractor.from_pretrained(
            """https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"""		)







@is_staging_test
class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  @classmethod
  def  _lowercase      (			cls    : List[str]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   TOKEN
        HfFolder.save_token(UpperCAmelCase__		)


  @classmethod
  def  _lowercase      (			cls    : List[str]		)					->Optional[int]:
        """simple docstring"""







        try:
              delete_repo(token=cls._token				,				repo_id="""test-feature-extractor"""		)
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token				,				repo_id="""valid_org/test-feature-extractor-org"""		)
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token				,				repo_id="""test-dynamic-feature-extractor"""		)
        except HTTPError:
              pass


  def  _lowercase      (			self    : int		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__		)
        feature_extractor.push_to_hub("""test-feature-extractor"""				,				use_auth_token=self._token		)

        SCREAMING_SNAKE_CASE :      Any       =   WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"		)
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(UpperCAmelCase__				,				getattr(UpperCAmelCase__				,				UpperCAmelCase__		)		)

        # Reset repo
        delete_repo(token=self._token				,				repo_id="""test-feature-extractor"""		)

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  UpperCAmelCase__				,				repo_id="""test-feature-extractor"""				,				push_to_hub=UpperCAmelCase__				,				use_auth_token=self._token		)

        SCREAMING_SNAKE_CASE :      List[str]       =   WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"		)
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(UpperCAmelCase__				,				getattr(UpperCAmelCase__				,				UpperCAmelCase__		)		)


  def  _lowercase      (			self    : List[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__		)
        feature_extractor.push_to_hub("""valid_org/test-feature-extractor"""				,				use_auth_token=self._token		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor"""		)
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(UpperCAmelCase__				,				getattr(UpperCAmelCase__				,				UpperCAmelCase__		)		)

        # Reset repo
        delete_repo(token=self._token				,				repo_id="""valid_org/test-feature-extractor"""		)

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  UpperCAmelCase__				,				repo_id="""valid_org/test-feature-extractor-org"""				,				push_to_hub=UpperCAmelCase__				,				use_auth_token=self._token		)

        SCREAMING_SNAKE_CASE :      List[Any]       =   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org"""		)
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(UpperCAmelCase__				,				getattr(UpperCAmelCase__				,				UpperCAmelCase__		)		)




  def  _lowercase      (			self    : Optional[Any]		)					->Optional[int]:
        """simple docstring"""







        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE :      Any       =   CustomFeatureExtractor.from_pretrained(UpperCAmelCase__		)

        feature_extractor.push_to_hub("""test-dynamic-feature-extractor"""				,				use_auth_token=self._token		)

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map				,				{"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor"""}				,				)

        SCREAMING_SNAKE_CASE :      Any       =   AutoFeatureExtractor.from_pretrained(
            f"{USER}/test-dynamic-feature-extractor"				,				trust_remote_code=UpperCAmelCase__		)
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__				,				"""CustomFeatureExtractor"""		)




