
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Tuple   							=  ['image_processor', 'tokenizer']
							lowercase_		:						List[str]   							=  'Pix2StructImageProcessor'
							lowercase_		:						Union[str, Any]   							=  ('T5Tokenizer', 'T5TokenizerFast')







							def __init__(						self  :  Union[str, Any]      ,				snake_case__  :  Dict      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							False
								super().__init__(snake_case__      ,				snake_case__ )







							def __call__(						self  :  List[Any]      ,				snake_case__  :  str=None      ,				snake_case__  :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[bool, str, PaddingStrategy] = False      ,				snake_case__  :  Union[bool, str, TruncationStrategy] = None      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  Optional[int] = 2_048      ,				snake_case__  :  int = 0      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  bool = False      ,				snake_case__  :  bool = False      ,				snake_case__  :  bool = False      ,				snake_case__  :  bool = False      ,				snake_case__  :  bool = False      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







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

								# Get only text
								if images is None and not self.image_processor.is_vqa:
									__lowerCAmelCase         =							self.tokenizer
									__lowerCAmelCase         =							self.tokenizer(
									    text=snake_case__      ,				add_special_tokens=snake_case__      ,				padding=snake_case__      ,				truncation=snake_case__      ,				max_length=snake_case__      ,				stride=snake_case__      ,				pad_to_multiple_of=snake_case__      ,				return_attention_mask=snake_case__      ,				return_overflowing_tokens=snake_case__      ,				return_special_tokens_mask=snake_case__      ,				return_offsets_mapping=snake_case__      ,				return_token_type_ids=snake_case__      ,				return_length=snake_case__      ,				verbose=snake_case__      ,				return_tensors=snake_case__      ,				**snake_case__      ,				)
									return text_encoding

								if not self.image_processor.is_vqa:
									# add pixel_values
									__lowerCAmelCase         =							self.image_processor(
									    snake_case__      ,				return_tensors=snake_case__      ,				max_patches=snake_case__      ,				**snake_case__ )
								else:
									# add pixel_values and bbox
									__lowerCAmelCase         =							self.image_processor(
									    snake_case__      ,				return_tensors=snake_case__      ,				max_patches=snake_case__      ,				header_text=snake_case__      ,				**snake_case__ )

								if text is not None and not self.image_processor.is_vqa:
									__lowerCAmelCase         =							self.tokenizer(
									    text=snake_case__      ,				add_special_tokens=snake_case__      ,				padding=snake_case__      ,				truncation=snake_case__      ,				max_length=snake_case__      ,				stride=snake_case__      ,				pad_to_multiple_of=snake_case__      ,				return_attention_mask=snake_case__      ,				return_overflowing_tokens=snake_case__      ,				return_special_tokens_mask=snake_case__      ,				return_offsets_mapping=snake_case__      ,				return_token_type_ids=snake_case__      ,				return_length=snake_case__      ,				verbose=snake_case__      ,				return_tensors=snake_case__      ,				**snake_case__      ,				)

									if "attention_mask" in text_encoding:
										__lowerCAmelCase         =							text_encoding.pop("attention_mask" )
									if "input_ids" in text_encoding:
										__lowerCAmelCase         =							text_encoding.pop("input_ids" )
								else:
									__lowerCAmelCase         =							None

								if text_encoding is not None:
									encoding_image_processor.update(snake_case__ )

								return encoding_image_processor







							def       UpperCAmelCase__					(						self  :  List[str]      ,				*snake_case__  :  Optional[int]      ,				**snake_case__  :  int ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				*snake_case__  :  Tuple      ,				**snake_case__  :  str ):
								"""simple docstring"""







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







							@property
							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.tokenizer.model_input_names
								__lowerCAmelCase         =							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [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],
]

UpperCamelCase_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""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       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







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







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""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(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									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(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

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

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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  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(snake_case__ ) - 1):
										continue

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

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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""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__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






