


from __future__ import annotations

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

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    Optional[Any]    ,					_A			:    int    ,					_A			:    Dict    ,					_A			:    Tuple    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          [0 for col in range(len(grid[0]						)						)] for row in range(len(_UpperCAmelCase						)						)
      ]  # the reference grid
      lowerCamelCase__		:				List[Any]    =  1
      lowerCamelCase__		:				Optional[int]    =  [
          [0 for col in range(len(grid[0]						)						)] for row in range(len(_UpperCAmelCase						)						)
      ]  # the action grid

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

      lowerCamelCase__		:				Optional[int]    =  False  # flag that is set when search is complete
      lowerCamelCase__		:				Optional[int]    =  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()
                  lowerCamelCase__		:				Union[str, Any]    =  cell.pop()
                  lowerCamelCase__		:				List[Any]    =  next_cell[2]
                  lowerCamelCase__		:				str    =  next_cell[3]
                  lowerCamelCase__		:				int    =  next_cell[1]

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

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


if __name__ == "__main__":
    A      :    Dict		=					[
        [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],
    ]

    A      :    List[Any]		=					[0, 0]
    # all coordinates are given in format [y,x]
    A      :    Tuple		=					[len(grid) - 1, len(grid[0]) - 1]
    A      :    Any		=					1

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

    A      :    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 unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible

class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.bin''',
               '''safety_checker/model.safetensors''',
               '''vae/diffusion_pytorch_model.bin''',
               '''vae/diffusion_pytorch_model.safetensors''',
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.bin''',
               '''safety_checker/model.safetensors''',
               '''vae/diffusion_pytorch_model.bin''',
               '''vae/diffusion_pytorch_model.safetensors''',
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
               '''unet/diffusion_pytorch_model.bin''',
               # Removed: 'unet/diffusion_pytorch_model.safetensors',
           ]
           self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
           ]
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.bin''',
               '''safety_checker/model.safetensors''',
               '''vae/diffusion_pytorch_model.bin''',
               '''vae/diffusion_pytorch_model.safetensors''',
               '''text_encoder/pytorch_model.bin''',
               # Removed: 'text_encoder/model.safetensors',
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.fp16.bin''',
               '''safety_checker/model.fp16.safetensors''',
               '''vae/diffusion_pytorch_model.fp16.bin''',
               '''vae/diffusion_pytorch_model.fp16.safetensors''',
               '''text_encoder/pytorch_model.fp16.bin''',
               '''text_encoder/model.fp16.safetensors''',
               '''unet/diffusion_pytorch_model.fp16.bin''',
               '''unet/diffusion_pytorch_model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.fp16.bin''',
               '''unet/diffusion_pytorch_model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				[
               '''unet/diffusion_pytorch_model.bin''',
               '''unet/diffusion_pytorch_model.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.fp16.bin''',
               '''safety_checker/model.fp16.safetensors''',
               '''vae/diffusion_pytorch_model.fp16.bin''',
               '''vae/diffusion_pytorch_model.fp16.safetensors''',
               '''text_encoder/pytorch_model.fp16.bin''',
               '''text_encoder/model.fp16.safetensors''',
               '''unet/diffusion_pytorch_model.fp16.bin''',
               # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
           ]
           __a						       =				'''fp16'''
           self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.fp16.bin''',
               '''text_encoder/model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				[
               '''text_encoder/pytorch_model.bin''',
               '''text_encoder/model.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				[
               '''safety_checker/pytorch_model.fp16.bin''',
               '''safety_checker/model.fp16.safetensors''',
               '''vae/diffusion_pytorch_model.fp16.bin''',
               '''vae/diffusion_pytorch_model.fp16.safetensors''',
               '''text_encoder/pytorch_model.fp16.bin''',
               # 'text_encoder/model.fp16.safetensors',
               '''unet/diffusion_pytorch_model.fp16.bin''',
               '''unet/diffusion_pytorch_model.fp16.safetensors''',
           ]
           __a						       =				'''fp16'''
           self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE     ,		variant=__SCREAMING_SNAKE_CASE))





