

'''simple docstring'''
from __future__ import annotations

import time

_snake_case         =  list[tuple[int, int]]

_snake_case         =  [
    [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],
]

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


class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       pos_x
      _lowercase      :				Union[str, Any]      					=       pos_y
      _lowercase      :				Union[str, Any]      					=       (pos_y, pos_x)
      _lowercase      :				Tuple      					=       goal_x
      _lowercase      :				Optional[int]      					=       goal_y
      _lowercase      :				Union[str, Any]      					=       parent




class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       Node(start[1]      ,    start[0]      ,    goal[1]      ,    goal[0]      ,    _UpperCamelCase  )
      _lowercase      :				List[str]      					=       Node(goal[1]      ,    goal[0]      ,    goal[1]      ,    goal[0]      ,    _UpperCamelCase  )

      _lowercase      :				List[str]      					=       [self.start]
      _lowercase      :				Optional[Any]      					=       False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      while self.node_queue:
         _lowercase      :				Optional[int]      					=       self.node_queue.pop(0  )

         if current_node.pos == self.target.pos:
            _lowercase      :				Any      					=       True
            return self.retrace_path(_UpperCamelCase  )

         _lowercase      :				Optional[Any]      					=       self.get_successors(_UpperCamelCase  )

         for node in successors:
            self.node_queue.append(_UpperCamelCase  )

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       []
      for action in delta:
         _lowercase      :				Dict      					=       parent.pos_x + action[1]
         _lowercase      :				int      					=       parent.pos_y + action[0]
         if not (0 <= pos_x <= len(grid[0]  ) - 1 and 0 <= pos_y <= len(_UpperCamelCase  ) - 1):
            continue

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

         successors.append(
             Node(_UpperCamelCase      ,    _UpperCamelCase      ,    self.target.pos_y      ,    self.target.pos_x      ,    _UpperCamelCase  )  )
      return successors






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       node
      _lowercase      :				int      					=       []
      while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)  )
         _lowercase      :				Optional[Any]      					=       current_node.parent
      path.reverse()
      return path




class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       BreadthFirstSearch(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       BreadthFirstSearch(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				List[str]      					=       False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
         _lowercase      :				Any      					=       self.fwd_bfs.node_queue.pop(0  )
         _lowercase      :				Optional[Any]      					=       self.bwd_bfs.node_queue.pop(0  )

         if current_bwd_node.pos == current_fwd_node.pos:
            _lowercase      :				int      					=       True
            return self.retrace_bidirectional_path(
                _UpperCamelCase      ,    _UpperCamelCase  )

         _lowercase      :				List[str]      					=       current_bwd_node
         _lowercase      :				Tuple      					=       current_fwd_node

         _lowercase      :				List[str]      					=       {
             self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCamelCase  ),
             self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCamelCase  ),
         }

         for bfs in [self.fwd_bfs, self.bwd_bfs]:
            for node in successors[bfs]:
               bfs.node_queue.append(_UpperCamelCase  )

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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       self.fwd_bfs.retrace_path(_UpperCamelCase  )
      _lowercase      :				List[str]      					=       self.bwd_bfs.retrace_path(_UpperCamelCase  )
      bwd_path.pop()
      bwd_path.reverse()
      _lowercase      :				Union[str, Any]      					=       fwd_path + bwd_path
      return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    import doctest

    doctest.testmod()
    _snake_case         =  (0, 0)
    _snake_case         =  (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    _snake_case         =  time.time()
    _snake_case         =  BreadthFirstSearch(init, goal)
    _snake_case         =  bfs.search()
    _snake_case         =  time.time() - start_bfs_time

    print('Unidirectional BFS computation time : ', bfs_time)

    _snake_case         =  time.time()
    _snake_case         =  BidirectionalBreadthFirstSearch(init, goal)
    _snake_case         =  bd_bfs.search()
    _snake_case         =  time.time() - start_bd_bfs_time

    print('Bidirectional BFS computation time : ', bd_bfs_time)









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






