






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A__	:			Tuple			   = 0

A__	:			Any			   = [
    [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__	:			Optional[Any]			   = [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A__	:			str			   = tuple[int, int]
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Any,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Node | None,  ):

								'''simple docstring'''
								lowercase__    =			pos_x
								lowercase__    =			pos_y
								lowercase__    =			(pos_y, pos_x)
								lowercase__    =			goal_x
								lowercase__    =			goal_y
								lowercase__    =			g_cost
								lowercase__    =			parent
								lowercase__    =			self.calculate_heuristic()
								lowercase__    =			self.g_cost + self.h_cost




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.pos_x - self.goal_x
								lowercase__    =			self.pos_y - self.goal_y
								if HEURISTIC == 1:
												return abs(lowerCamelCase  ) + abs(lowerCamelCase  )
								else:
												return sqrt(dy**2 + dx**2  )




				def __lt__(     self						:    Union[str, Any],  lowerCamelCase						:    Node  ):

								'''simple docstring'''
								return self.f_cost < other.f_cost



class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    TPosition,  lowerCamelCase						:    TPosition  ):

								'''simple docstring'''
								lowercase__    =			Node(start[1],  start[0],  goal[1],  goal[0],  0,  lowerCamelCase  )
								lowercase__    =			Node(goal[1],  goal[0],  goal[1],  goal[0],  99_999,  lowerCamelCase  )

								lowercase__    =			[self.start]
								lowercase__    =			[]

								lowercase__    =			False




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								while self.open_nodes:
												# Open Nodes are sorted using __lt__
												self.open_nodes.sort()
												lowercase__    =			self.open_nodes.pop(0  )

												if current_node.pos == self.target.pos:
																return self.retrace_path(lowerCamelCase  )

												self.closed_nodes.append(lowerCamelCase  )
												lowercase__    =			self.get_successors(lowerCamelCase  )

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

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

								return [self.start.pos]




				def 				lowercase__   (     self						:    Tuple,  lowerCamelCase						:    Node  ):

								'''simple docstring'''
								lowercase__    =			[]
								for action in delta:
												lowercase__    =			parent.pos_x + action[1]
												lowercase__    =			parent.pos_y + action[0]
												if not (0 <= pos_x <= len(grid[0]  ) - 1 and 0 <= pos_y <= len(lowerCamelCase  ) - 1):
																continue

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

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




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    Node | None  ):

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



class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Any,  lowerCamelCase						:    TPosition,  lowerCamelCase						:    TPosition  ):

								'''simple docstring'''
								lowercase__    =			AStar(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			AStar(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			False




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
												self.fwd_astar.open_nodes.sort()
												self.bwd_astar.open_nodes.sort()
												lowercase__    =			self.fwd_astar.open_nodes.pop(0  )
												lowercase__    =			self.bwd_astar.open_nodes.pop(0  )

												if current_bwd_node.pos == current_fwd_node.pos:
																return self.retrace_bidirectional_path(
																    lowerCamelCase,  lowerCamelCase  )

												self.fwd_astar.closed_nodes.append(lowerCamelCase  )
												self.bwd_astar.closed_nodes.append(lowerCamelCase  )

												lowercase__    =			current_bwd_node
												lowercase__    =			current_fwd_node

												lowercase__    =			{
												    self.fwd_astar: self.fwd_astar.get_successors(lowerCamelCase  ),
												    self.bwd_astar: self.bwd_astar.get_successors(lowerCamelCase  ),
												}

												for astar in [self.fwd_astar, self.bwd_astar]:
																for child_node in successors[astar]:
																				if child_node in astar.closed_nodes:
																								continue

																				if child_node not in astar.open_nodes:
																								astar.open_nodes.append(lowerCamelCase  )
																				else:
																								# retrieve the best current path
																								lowercase__    =			astar.open_nodes.pop(
																								    astar.open_nodes.index(lowerCamelCase  )  )

																								if child_node.g_cost < better_node.g_cost:
																												astar.open_nodes.append(lowerCamelCase  )
																								else:
																												astar.open_nodes.append(lowerCamelCase  )

								return [self.fwd_astar.start.pos]




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    Node,  lowerCamelCase						:    Node  ):

								'''simple docstring'''
								lowercase__    =			self.fwd_astar.retrace_path(lowerCamelCase  )
								lowercase__    =			self.bwd_astar.retrace_path(lowerCamelCase  )
								bwd_path.pop()
								bwd_path.reverse()
								lowercase__    =			fwd_path + bwd_path
								return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							A__	:			Optional[Any]			   = (0, 0)
							A__	:			List[Any]			   = (len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							A__	:			str			   = time.time()
							A__	:			List[str]			   = AStar(init, goal)
							A__	:			Optional[Any]			   = a_star.search()
							A__	:			Optional[int]			   = time.time() - start_time
							print(F"AStar execution time = {end_time:f} seconds")

							A__	:			Optional[int]			   = time.time()
							A__	:			Optional[Any]			   = BidirectionalAStar(init, goal)
							A__	:			Tuple			   = time.time() - bd_start_time
							print(F"BidirectionalAStar execution time = {bd_end_time:f} seconds")












from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


A__	:			Dict			   = logging.get_logger(__name__)
@add_end_docstrings(
    A__		,r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """		,)
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								if self.framework == "tf":
												lowercase__    =			tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
								elif self.framework == "pt":
												lowercase__    =			torch.nonzero(input_ids == self.tokenizer.mask_token_id,  as_tuple=lowerCamelCase  )
								else:
												raise ValueError('''Unsupported framework'''  )
								return masked_index




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								lowercase__    =			self.get_masked_index(lowerCamelCase  )
								lowercase__    =			np.prod(masked_index.shape  )
								if numel < 1:
												raise PipelineException(
												    '''fill-mask''',  self.model.base_model_prefix,  F"""No mask_token ({self.tokenizer.mask_token}) found on the input""",  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):
												for model_input in model_inputs:
																self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]  )
								else:
												for input_ids in model_inputs["input_ids"]:
																self._ensure_exactly_one_mask_token(lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int]=None,  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								if return_tensors is None:
												lowercase__    =			self.framework
								lowercase__    =			self.tokenizer(lowerCamelCase,  return_tensors=lowerCamelCase  )
								self.ensure_exactly_one_mask_token(lowerCamelCase  )
								return model_inputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model(**lowerCamelCase  )
								lowercase__    =			model_inputs['''input_ids''']
								return model_outputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Tuple=5,  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								# Cap top_k if there are targets
								if target_ids is not None and target_ids.shape[0] < top_k:
												lowercase__    =			target_ids.shape[0]
								lowercase__    =			model_outputs['''input_ids'''][0]
								lowercase__    =			model_outputs['''logits''']

								if self.framework == "tf":
												lowercase__    =			tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

												lowercase__    =			outputs.numpy()

												lowercase__    =			outputs[0, masked_index, :]
												lowercase__    =			stable_softmax(lowerCamelCase,  axis=-1  )
												if target_ids is not None:
																lowercase__    =			tf.gather_nd(tf.squeeze(lowerCamelCase,  0  ),  target_ids.reshape(-1,  1  )  )
																lowercase__    =			tf.expand_dims(lowerCamelCase,  0  )

												lowercase__    =			tf.math.top_k(lowerCamelCase,  k=lowerCamelCase  )
												lowercase__ ,						lowercase__    =			topk.values.numpy(), topk.indices.numpy()
								else:
												lowercase__    =			torch.nonzero(input_ids == self.tokenizer.mask_token_id,  as_tuple=lowerCamelCase  ).squeeze(-1  )
												# Fill mask pipeline supports only one ${mask_token} per sample

												lowercase__    =			outputs[0, masked_index, :]
												lowercase__    =			logits.softmax(dim=-1  )
												if target_ids is not None:
																lowercase__    =			probs[..., target_ids]

												lowercase__ ,						lowercase__    =			probs.topk(lowerCamelCase  )

								lowercase__    =			[]
								lowercase__    =			values.shape[0] == 1
								for i, (_values, _predictions) in enumerate(zip(values.tolist(),  predictions.tolist()  )  ):
												lowercase__    =			[]
												for v, p in zip(_values,  _predictions  ):
																# Copy is important since we're going to modify this array in place
																lowercase__    =			input_ids.numpy().copy()
																if target_ids is not None:
																				lowercase__    =			target_ids[p].tolist()

																lowercase__    =			p
																# Filter padding out:
																lowercase__    =			tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
																# Originally we skip special tokens to give readable output.
																# For multi masks though, the other [MASK] would be removed otherwise
																# making the output look odd, so we add them back
																lowercase__    =			self.tokenizer.decode(lowerCamelCase,  skip_special_tokens=lowerCamelCase  )
																lowercase__    =			{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]  ), '''sequence''': sequence}
																row.append(lowerCamelCase  )
												result.append(lowerCamelCase  )
								if single_mask:
												return result[0]
								return result




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict=None  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):
												lowercase__    =			[targets]
								try:
												lowercase__    =			self.tokenizer.get_vocab()
								except Exception:
												lowercase__    =			{}
								lowercase__    =			[]
								for target in targets:
												lowercase__    =			vocab.get(lowerCamelCase,  lowerCamelCase  )
												if id_ is None:
																lowercase__    =			self.tokenizer(
																    lowerCamelCase,  add_special_tokens=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  max_length=1,  truncation=lowerCamelCase,  )['''input_ids''']
																if len(lowerCamelCase  ) == 0:
																				logger.warning(
																				    F"""The specified target token `{target}` does not exist in the model vocabulary. """
																				    '''We cannot replace it with anything meaningful, ignoring it'''  )
																				continue
																lowercase__    =			input_ids[0]
																# XXX: If users encounter this pass
																# it becomes pretty slow, so let's make sure
																# The warning enables them to fix the input to
																# get faster performance.
																logger.warning(
																    F"""The specified target token `{target}` does not exist in the model vocabulary. """
																    F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`."""  )
												target_ids.append(id_  )
								lowercase__    =			list(set(lowerCamelCase  )  )
								if len(lowerCamelCase  ) == 0:
												raise ValueError('''At least one target must be provided when passed.'''  )
								lowercase__    =			np.array(lowerCamelCase  )
								return target_ids




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    int=None,  lowerCamelCase						:    Any=None  ):

								'''simple docstring'''
								lowercase__    =			{}

								if targets is not None:
												lowercase__    =			self.get_target_ids(lowerCamelCase,  lowerCamelCase  )
												lowercase__    =			target_ids

								if top_k is not None:
												lowercase__    =			top_k

								if self.tokenizer.mask_token_id is None:
												raise PipelineException(
												    '''fill-mask''',  self.model.base_model_prefix,  '''The tokenizer does not define a `mask_token`.'''  )
								return {}, {}, postprocess_params




				def __call__(     self						:    List[Any],  lowerCamelCase						:    Optional[Any],  *lowerCamelCase						:    Optional[Any],  **lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			super().__call__(lowerCamelCase,  **lowerCamelCase  )
								if isinstance(lowerCamelCase,  lowerCamelCase  ) and len(lowerCamelCase  ) == 1:
												return outputs[0]
								return outputs




