

import numpy as np






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]							):
		__A :			Union[str, Any]		    =			(0, 0)
		__A :			Optional[Any]		    =			None
		__A :			int		    =			0
		__A :			List[Any]		    =			0
		__A :			Any		    =			0




	def __eq__(     self						:     str					,      __A						:     Dict							):
		return self.position == cell.position




	def        lowerCAmelCase_       (     self						:     int							):
		print(self.position							)






class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     List[str]=(5, 5)							):
		__A :			str		    =			np.zeros(__A							)
		__A :			str		    =			world_size[0]
		__A :			Union[str, Any]		    =			world_size[1]




	def        lowerCAmelCase_       (     self						:     Dict							):
		print(self.w							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     str							):
		__A :			int		    =			[
		    (-1, -1),
		    (-1, 0),
		    (-1, 1),
		    (0, -1),
		    (0, 1),
		    (1, -1),
		    (1, 0),
		    (1, 1),
		]
		__A :			Optional[int]		    =			cell.position[0]
		__A :			List[str]		    =			cell.position[1]
		__A :			Optional[int]		    =			[]
		for n in neughbour_cord:
			__A :			Optional[int]		    =			current_x + n[0]
			__A :			int		    =			current_y + n[1]
			if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
				__A :			int		    =			Cell()
				__A :			Optional[Any]		    =			(x, y)
				__A :			Optional[Any]		    =			cell
				neighbours.append(__A							)
		return neighbours






def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Optional[int]			,a__    :   List[str]       )					->				Any:
	__A :			Dict		    =			[]
	__A :			Dict		    =			[]
	_open.append(a__       )

	while _open:
		__A :			List[str]		    =			np.argmin([n.f for n in _open]       )
		__A :			Dict		    =			_open[min_f]
		_closed.append(_open.pop(a__       )       )
		if current == goal:
			break
		for n in world.get_neigbours(a__       ):
			for c in _closed:
				if c == n:
					continue
			__A :			Tuple		    =			current.g + 1
			__A    ,				__A :			int		    =			n.position
			__A    ,				__A :			str		    =			goal.position
			__A :			Optional[int]		    =			(ya - ya) ** 2 + (xa - xa) ** 2
			__A :			Any		    =			n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(a__       )
	__A :			Any		    =			[]
	while current.parent is not None:
		path.append(current.position       )
		__A :			Tuple		    =			current.parent
	path.append(current.position       )
	return path[::-1]


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   Gridworld()
			# Start position and goal
			UpperCAmelCase_							:					Any		 =   Cell()
			UpperCAmelCase_							:					Tuple		 =   (0, 0)
			UpperCAmelCase_							:					Tuple		 =   Cell()
			UpperCAmelCase_							:					str		 =   (4, 4)
			print(f"""path from {start.position} to {goal.position}""")
			UpperCAmelCase_							:					Tuple		 =   astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						UpperCAmelCase_							:					Optional[int]		 =   1
			print(world.w)












'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

		def __init__(   self					:   int  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Dict=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""  ,      __magic_name__  ,      )
								__snake_case					:      List[Any]							      =     kwargs.pop("""feature_extractor"""			)

					__snake_case					:      List[str]							      =     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__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

					if text is None and images is None:
								raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

					if text is not None:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if text is not None and images is not None:
								__snake_case					:      Union[str, Any]							      =     image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**__magic_name__			)  ,      tensor_type=__magic_name__			)

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


					"""simple docstring"""

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

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


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

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

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


					"""simple docstring"""

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


