



'''simple docstring'''

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     ["image_processor", "tokenizer"]
	__magic_name__		:    int	    =     "AutoImageProcessor"
	__magic_name__		:    List[str]	    =     "AutoTokenizer"


	def __init__(self : str,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Any=None,					**__UpperCAmelCase : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.''',					__UpperCAmelCase,					)
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= kwargs.pop('''feature_extractor'''      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError('''You need to specify an `image_processor`.'''      )
					if tokenizer is None:
									raise ValueError('''You need to specify a `tokenizer`.'''      )

					super().__init__(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.image_processor
					SCREAMING_SNAKE_CASE  :						int      					= False


	def __call__(self : Union[str, Any],					*__UpperCAmelCase : str,					**__UpperCAmelCase : Any      )     ->			Dict:

					"""simple docstring"""

					if self._in_target_context_manager:
									return self.current_processor(*__UpperCAmelCase,					**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= kwargs.pop('''images''',					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= kwargs.pop('''text''',					__UpperCAmelCase      )
					if len(__UpperCAmelCase      ) > 0:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= args[0]
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= args[1:]

					if images is None and text is None:
									raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

					if images is not None:
									SCREAMING_SNAKE_CASE  :						int      					= self.image_processor(__UpperCAmelCase,					*__UpperCAmelCase,					**__UpperCAmelCase      )
					if text is not None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.tokenizer(__UpperCAmelCase,					**__UpperCAmelCase      )

					if text is None:
									return inputs
					elif images is None:
									return encodings
					else:
									SCREAMING_SNAKE_CASE  :						List[str]      					= encodings['''input_ids''']
									return inputs


	def 				lowercase__  (self : Any,					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					return self.tokenizer.batch_decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Dict,					*__UpperCAmelCase : str,					**__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					return self.tokenizer.decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	@contextmanager
	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
					    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
					    '''your images inputs, or in a separate call.'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= True
					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenizer
					yield
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processor
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple=False,					__UpperCAmelCase : Union[str, Any]=None      )     ->			Optional[int]:

					"""simple docstring"""

					if added_vocab is None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.tokenizer.get_added_vocab()

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= {}

					while tokens:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.search(R'''<s_(.*?)>''',					__UpperCAmelCase,					re.IGNORECASE      )
									if start_token is None:
													break
									SCREAMING_SNAKE_CASE  :						Dict      					= start_token.group(1      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= re.search(RF'''</s_{key}>''',					__UpperCAmelCase,					re.IGNORECASE      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= start_token.group()
									if end_token is None:
													SCREAMING_SNAKE_CASE  :						Tuple      					= tokens.replace(__UpperCAmelCase,					''''''      )
									else:
													SCREAMING_SNAKE_CASE  :						List[Any]      					= end_token.group()
													SCREAMING_SNAKE_CASE  :						Any      					= re.escape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						List[str]      					= re.escape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''',					__UpperCAmelCase,					re.IGNORECASE      )
													if content is not None:
																	SCREAMING_SNAKE_CASE  :						Tuple      					= content.group(1      ).strip()
																	if r"<s_" in content and r"</s_" in content:  # non-leaf node
																					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenajson(__UpperCAmelCase,					is_inner_value=__UpperCAmelCase,					added_vocab=__UpperCAmelCase      )
																					if value:
																									if len(__UpperCAmelCase      ) == 1:
																													SCREAMING_SNAKE_CASE  :						List[Any]      					= value[0]
																									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= value
																	else:  # leaf nodes
																					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
																					for leaf in content.split(R'''<sep/>'''      ):
																									SCREAMING_SNAKE_CASE  :						str      					= leaf.strip()
																									if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																													SCREAMING_SNAKE_CASE  :						List[str]      					= leaf[1:-2]  # for categorical special tokens
																									output[key].append(__UpperCAmelCase      )
																					if len(output[key]      ) == 1:
																									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= output[key][0]

													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tokens[tokens.find(__UpperCAmelCase      ) + len(__UpperCAmelCase      ) :].strip()
													if tokens[:6] == r"<sep/>":  # non-leaf nodes
																	return [output] + self.tokenajson(tokens[6:],					is_inner_value=__UpperCAmelCase,					added_vocab=__UpperCAmelCase      )

					if len(__UpperCAmelCase      ):
									return [output] if is_inner_value else output
					else:
									return [] if is_inner_value else {"text_sequence": tokens}


	@property
	def 				lowercase__  (self : Union[str, Any]      )     ->			int:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					__UpperCAmelCase,					)
					return self.image_processor_class






	@property
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					__UpperCAmelCase,					)
					return self.image_processor









'''simple docstring'''

from __future__ import annotations

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

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

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

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

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

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


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

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

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

	snake_case_			,				snake_case_			=     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])




