




from __future__ import annotations

lowerCamelCase    :		str			   =       list[tuple[int, int]]

lowerCamelCase    :		Tuple			   =       [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase    :		Optional[int]			   =       ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class   A:



 '''simple docstring'''


 def __init__(    self :  List[Any]						,							A_ :  int						,							A_ :  int						,							A_ :  int						,							A_ :  int						,							A_ :  float						,							A_ :  Node | None						,							)       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   pos_x
       lowerCamelCase_							=   pos_y
       lowerCamelCase_							=   (pos_y, pos_x)
       lowerCamelCase_							=   goal_x
       lowerCamelCase_							=   goal_y
       lowerCamelCase_							=   g_cost
       lowerCamelCase_							=   parent
       lowerCamelCase_							=   self.calculate_heuristic()

 def     a__			(    self :  Tuple  )       ->     float:

       """simple docstring"""






       lowerCamelCase_							=   abs(self.pos_x - self.goal_x  )
       lowerCamelCase_							=   abs(self.pos_y - self.goal_y  )
       return dx + dy





 def __lt__(    self :  int						,							A_ :  Optional[int]  )       ->     bool:

       """simple docstring"""






       return self.f_cost < other.f_cost







class   A:



 '''simple docstring'''


 def __init__(    self :  Optional[int]						,							A_ :  tuple[int, int]						,							A_ :  tuple[int, int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   Node(start[1]						,							start[0]						,							goal[1]						,							goal[0]						,							0						,							A_  )
       lowerCamelCase_							=   Node(goal[1]						,							goal[0]						,							goal[1]						,							goal[0]						,							99999						,							A_  )

       lowerCamelCase_							=   [self.start]
       lowerCamelCase_							=   []

       lowerCamelCase_							=   False

 def     a__			(    self :  Dict  )       ->     Path | None:

       """simple docstring"""






       while self.open_nodes:
             # Open Nodes are sorted using __lt__
             self.open_nodes.sort()
             lowerCamelCase_							=   self.open_nodes.pop(0  )

             if current_node.pos == self.target.pos:
                   lowerCamelCase_							=   True
                   return self.retrace_path(A_  )

             self.closed_nodes.append(A_  )
             lowerCamelCase_							=   self.get_successors(A_  )

             for child_node in successors:
                   if child_node in self.closed_nodes:
                         continue

                   if child_node not in self.open_nodes:
                         self.open_nodes.append(A_  )
                   else:
                         # retrieve the best current path
                         lowerCamelCase_							=   self.open_nodes.pop(self.open_nodes.index(A_  )  )

                         if child_node.g_cost < better_node.g_cost:
                               self.open_nodes.append(A_  )
                         else:
                               self.open_nodes.append(A_  )

       if not self.reached:
             return [self.start.pos]
       return None

 def     a__			(    self :  Tuple						,							A_ :  Node  )       ->     list[Node]:

       """simple docstring"""






       lowerCamelCase_							=   []
       for action in delta:
             lowerCamelCase_							=   parent.pos_x + action[1]
             lowerCamelCase_							=   parent.pos_y + action[0]

             if not (0 <= pos_x <= len(grid[0]  ) - 1 and 0 <= pos_y <= len(A_  ) - 1):
                   continue

             if grid[pos_y][pos_x] != 0:
                   continue

             successors.append(
                 Node(
                     A_						,							A_						,							self.target.pos_y						,							self.target.pos_x						,							parent.g_cost + 1						,							A_						,							)  )
       return successors





 def     a__			(    self :  List[str]						,							A_ :  Node | None  )       ->     Path:

       """simple docstring"""






       lowerCamelCase_							=   node
       lowerCamelCase_							=   []
       while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x)  )
             lowerCamelCase_							=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       (0, 0)
 lowerCamelCase    :		Optional[Any]			   =       (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 print("------")

 lowerCamelCase    :		List[str]			   =       GreedyBestFirst(init, goal)
 lowerCamelCase    :		int			   =       greedy_bf.search()
 if path:
  for pos_x, pos_y in path:
   lowerCamelCase    :		Optional[int]			   =       2

  for elem in grid:
   print(elem)









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   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''OwlViTImageProcessor'''
 UpperCamelCase						       =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   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_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   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 :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       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_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

                   # Maximum number of queries across batch
                   lowerCamelCase_							=   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:
                               lowerCamelCase_							=   t + [' '] * (max_num_queries - len(A_  ))

                         lowerCamelCase_							=   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":
                   lowerCamelCase_							=   np.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   np.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

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

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

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

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

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

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

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

             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   input_ids
             lowerCamelCase_							=   attention_mask

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

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       if text is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif query_images is not None and images is not None:
             lowerCamelCase_							=   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 :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

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

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






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

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






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

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

       """simple docstring"""






       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 :  str  )       ->     List[str]:

       """simple docstring"""






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



