






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			if isinstance(_UpperCAmelCase							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= np.full((len(_UpperCAmelCase), sequence_length, 2)							,       _UpperCAmelCase)
			else:
						SCREAMING_SNAKE_CASE						= np.full((len(_UpperCAmelCase), sequence_length)							,       _UpperCAmelCase)

			for i, tensor in enumerate(_UpperCAmelCase):
						if padding_side == "right":
									if isinstance(_UpperCAmelCase							,       _UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= tensor[:sequence_length]
									else:
												SCREAMING_SNAKE_CASE						= tensor[:sequence_length]
						else:
									if isinstance(_UpperCAmelCase							,       _UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= tensor[:sequence_length]
									else:
												SCREAMING_SNAKE_CASE						= tensor[:sequence_length]

			return out_tensor.tolist()






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ord(_UpperCAmelCase)
			if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
						return True
			SCREAMING_SNAKE_CASE						= unicodedata.category(_UpperCAmelCase)
			if cat.startswith('P'):
						return True
			return False







@dataclass
class 		_snake_case  (  A__					):
			_lowercase		:    PreTrainedTokenizerBase
			_lowercase		:    Union[bool, str, PaddingStrategy]			     =							True
			_lowercase		:    Optional[int]			     =							None
			_lowercase		:    Optional[int]			     =							None
			_lowercase		:    int			     =							-1_00
			_lowercase		:    str			     =							"pt"






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						import torch

						SCREAMING_SNAKE_CASE						= 'label' if 'label' in features[0].keys() else 'labels'
						SCREAMING_SNAKE_CASE						= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
						SCREAMING_SNAKE_CASE						= self.tokenizer.pad(
						    a		,					padding=self.padding		,					max_length=self.max_length		,					pad_to_multiple_of=self.pad_to_multiple_of		,					return_tensors='pt' if labels is None else None		,					)

						if labels is None:
									return batch

						SCREAMING_SNAKE_CASE						= torch.tensor(batch['entity_ids']).shape[1]
						SCREAMING_SNAKE_CASE						= self.tokenizer.padding_side
						if padding_side == "right":
									SCREAMING_SNAKE_CASE						= [
									    list(a) + [self.label_pad_token_id] * (sequence_length - len(a)) for label in labels
									]
						else:
									SCREAMING_SNAKE_CASE						= [
									    [self.label_pad_token_id] * (sequence_length - len(a)) + list(a) for label in labels
									]

						SCREAMING_SNAKE_CASE						= [feature['ner_tags'] for feature in features]
						SCREAMING_SNAKE_CASE						= padding_tensor(a		,					-1		,					a		,					a)
						SCREAMING_SNAKE_CASE						= [feature['original_entity_spans'] for feature in features]
						SCREAMING_SNAKE_CASE						= padding_tensor(a		,					(-1, -1)		,					a		,					a)
						SCREAMING_SNAKE_CASE						= {k: torch.tensor(a		,					dtype=torch.intaa) for k, v in batch.items()}

						return batch












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[int]             =    [
    [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],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

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

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= 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		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

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

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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


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

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

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

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

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




