




"""simple docstring"""


from __future__ import annotations

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


def       UpperCAmelCase__ (snake_case__							:      list[list[int]]	,     snake_case__							:      list[int]	,     snake_case__							:      list[int]	,     snake_case__							:      int	,     snake_case__							:      list[list[int]]	,     ):
  """simple docstring"""

  _snake_case							:	List[Any]			=						[
      [0 for col in range(len(grid[0]					)					)] for row in range(len(snake_case__					)					)
  ]  # the reference grid
  _snake_case							:	List[str]			=						1
  _snake_case							:	Union[str, Any]			=						[
      [0 for col in range(len(grid[0]					)					)] for row in range(len(snake_case__					)					)
  ]  # the action grid

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

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

      if x == goal[0] and y == goal[1]:
        _snake_case							:	Optional[int]			=						True
      else:
        for i in range(len(snake_case__					)					):  # to try out different valid actions
          _snake_case							:	Dict			=						x + DIRECTIONS[i][0]
          _snake_case							:	Dict			=						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							:	List[str]			=						g + cost
              _snake_case							:	str			=						ga + heuristic[xa][ya]
              cell.append([fa, ga, xa, ya]					)
              _snake_case							:	str			=						1
              _snake_case							:	Any			=						i
  _snake_case							:	List[Any]			=						[]
  _snake_case							:	List[str]			=						goal[0]
  _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]:
    _snake_case							:	Any			=						x - DIRECTIONS[action[x][y]][0]
    _snake_case							:	List[Any]			=						y - DIRECTIONS[action[x][y]][1]
    _snake_case							:	Optional[int]			=						xa
    _snake_case							:	int			=						ya
    invpath.append([x, y]					)

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


if __name__ == "__main__":
     A_      			=       [
         [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_      			=       [0, 0]
     # all coordinates are given in format [y,x]
     A_      			=       [len(grid) - 1, len(grid[0]) - 1]
     A_      			=       1

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

     A_			,					A_      			=       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 os
import sys
import unittest


A_      			=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
A_      			=       os.path.join(git_repo_path, '''src''', '''diffusers''')






class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						find_backend("""    if not is_torch_available():"""	)
   self.assertEqual(a_,   """torch"""	)

   # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
   # self.assertEqual(backend_with_underscore, "tensorflow_text")

   _snake_case							:	Any			=						find_backend("""    if not (is_torch_available() and is_transformers_available()):"""	)
   self.assertEqual(a_,   """torch_and_transformers"""	)

   # double_backend_with_underscore = find_backend(
   #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
   # )
   # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

   _snake_case							:	Union[str, Any]			=						find_backend(
       """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""	)
   self.assertEqual(a_,   """torch_and_transformers_and_onnx"""	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						read_init()
   # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
   self.assertIn("""torch""",   a_	)
   self.assertIn("""torch_and_transformers""",   a_	)
   self.assertIn("""flax_and_transformers""",   a_	)
   self.assertIn("""torch_and_transformers_and_onnx""",   a_	)

   # Likewise, we can't assert on the exact content of a key
   self.assertIn("""UNet2DModel""",   objects["""torch"""]	)
   self.assertIn("""FlaxUNet2DConditionModel""",   objects["""flax"""]	)
   self.assertIn("""StableDiffusionPipeline""",   objects["""torch_and_transformers"""]	)
   self.assertIn("""FlaxStableDiffusionPipeline""",   objects["""flax_and_transformers"""]	)
   self.assertIn("""LMSDiscreteScheduler""",   objects["""torch_and_scipy"""]	)
   self.assertIn("""OnnxStableDiffusionPipeline""",   objects["""torch_and_transformers_and_onnx"""]	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						create_dummy_object("""CONSTANT""",   """'torch'"""	)
   self.assertEqual(a_,   """\nCONSTANT = None\n"""	)

   _snake_case							:	Optional[int]			=						create_dummy_object("""function""",   """'torch'"""	)
   self.assertEqual(
       a_,   """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""	)

   _snake_case							:	List[Any]			=						"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
   _snake_case							:	Union[str, Any]			=						create_dummy_object("""FakeClass""",   """'torch'"""	)
   self.assertEqual(a_,   a_	)


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




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
   _snake_case							:	List[Any]			=						create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}	)
   self.assertEqual(dummy_files["""torch"""],   a_	)


