



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 					_snake_case      (       lowercase__):
				UpperCamelCase__					:       List[Any]  		=["""image_processor""", """tokenizer"""]
				UpperCamelCase__					:       Dict  		="""OwlViTImageProcessor"""
				UpperCamelCase__					:       str  		=("""CLIPTokenizer""", """CLIPTokenizerFast""")




				def __init__(    self       :    List[Any],       __lowercase       :    Any=None,       __lowercase       :    Dict=None,       **__lowercase       :    Optional[Any]    ):
							lowercase__       	=					None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead.",       a_,       )
										lowercase__       	=					kwargs.pop("feature_extractor"    )

							lowercase__       	=					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__(a_,       a_    )




				def __call__(    self       :    Any,       __lowercase       :    Tuple=None,       __lowercase       :    int=None,       __lowercase       :    Tuple=None,       __lowercase       :    Optional[Any]="max_length",       __lowercase       :    int="np",       **__lowercase       :    str    ):

							if text is None and query_images is None and images is None:
										raise ValueError(
										    "You have to specify at least one text or query image or image. All three cannot be none."    )

							if text is not None:
										if isinstance(a_,       a_    ) or (isinstance(a_,       a_    ) and not isinstance(text[0],       a_    )):
													lowercase__       	=					[self.tokenizer(a_,       padding=a_,       return_tensors=a_,       **a_    )]

										elif isinstance(a_,       a_    ) and isinstance(text[0],       a_    ):
													lowercase__       	=					[]

													# Maximum number of queries across batch
													lowercase__       	=					max([len(a_    ) for t in text]    )

													# Pad all batch samples to max number of text queries
													for t in text:
																if len(a_    ) != max_num_queries:
																			lowercase__       	=					t + [" "] * (max_num_queries - len(a_    ))

																lowercase__       	=					self.tokenizer(a_,       padding=a_,       return_tensors=a_,       **a_    )
																encodings.append(a_    )
										else:
													raise TypeError("Input text should be a string, a list of strings or a nested list of strings"    )

										if return_tensors == "np":
													lowercase__       	=					np.concatenate([encoding["input_ids"] for encoding in encodings],       axis=0    )
													lowercase__       	=					np.concatenate([encoding["attention_mask"] for encoding in encodings],       axis=0    )

										elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													lowercase__       	=					jnp.concatenate([encoding["input_ids"] for encoding in encodings],       axis=0    )
													lowercase__       	=					jnp.concatenate([encoding["attention_mask"] for encoding in encodings],       axis=0    )

										elif return_tensors == "pt" and is_torch_available():
													import torch

													lowercase__       	=					torch.cat([encoding["input_ids"] for encoding in encodings],       dim=0    )
													lowercase__       	=					torch.cat([encoding["attention_mask"] for encoding in encodings],       dim=0    )

										elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													lowercase__       	=					tf.stack([encoding["input_ids"] for encoding in encodings],       axis=0    )
													lowercase__       	=					tf.stack([encoding["attention_mask"] for encoding in encodings],       axis=0    )

										else:
													raise ValueError("Target return tensor type could not be returned"    )

										lowercase__       	=					BatchEncoding()
										lowercase__       	=					input_ids
										lowercase__       	=					attention_mask

							if query_images is not None:
										lowercase__       	=					BatchEncoding()
										lowercase__       	=					self.image_processor(
										    a_,       return_tensors=a_,       **a_    ).pixel_values
										lowercase__       	=					query_pixel_values

							if images is not None:
										lowercase__       	=					self.image_processor(a_,       return_tensors=a_,       **a_    )

							if text is not None and images is not None:
										lowercase__       	=					image_features.pixel_values
										return encoding
							elif query_images is not None and images is not None:
										lowercase__       	=					image_features.pixel_values
										return encoding
							elif text is not None or query_images is not None:
										return encoding
							else:
										return BatchEncoding(data=dict(**a_    ),       tensor_type=a_    )




				def 				A__   (    self       :    str,       *__lowercase       :    str,       **__lowercase       :    Union[str, Any]    ):
							return self.image_processor.post_process(*a_,       **a_    )




				def 				A__   (    self       :    Tuple,       *__lowercase       :    Optional[int],       **__lowercase       :    List[Any]    ):
							return self.image_processor.post_process_object_detection(*a_,       **a_    )




				def 				A__   (    self       :    List[Any],       *__lowercase       :    str,       **__lowercase       :    int    ):
							return self.image_processor.post_process_image_guided_detection(*a_,       **a_    )




				def 				A__   (    self       :    Optional[Any],       *__lowercase       :    str,       **__lowercase       :    int    ):
							return self.tokenizer.batch_decode(*a_,       **a_    )




				def 				A__   (    self       :    Tuple,       *__lowercase       :    List[str],       **__lowercase       :    List[Any]    ):
							return self.tokenizer.decode(*a_,       **a_    )




				@property
				def 				A__   (    self       :    int    ):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",       a_,       )
							return self.image_processor_class




				@property
				def 				A__   (    self       :    int    ):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",       a_,       )
							return self.image_processor









import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







						"""simple docstring"""
						return self.position == cell.position



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.position						)



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						print(self.w						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

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


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