






'''simple docstring'''







from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_A			:    List[Any]					  =0

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

_A			:    Optional[Any]					  =tuple[int, int]

class _lowercase			:




       def __init__(							self:       List[str]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Node | None    ,							):
              lowerCamelCase__      :			Dict 						= pos_x
              lowerCamelCase__      :			int 						= pos_y
              lowerCamelCase__      :			List[Any] 						= (pos_y, pos_x)
              lowerCamelCase__      :			Dict 						= goal_x
              lowerCamelCase__      :			Any 						= goal_y
              lowerCamelCase__      :			List[Any] 						= g_cost
              lowerCamelCase__      :			Optional[Any] 						= parent
              lowerCamelCase__      :			Any 						= self.calculate_heuristic()
              lowerCamelCase__      :			List[Any] 						= self.g_cost + self.h_cost




       def 	lowerCamelCase_       (							self:       str  ):
              lowerCamelCase__      :			int 						= self.pos_x - self.goal_x
              lowerCamelCase__      :			Union[str, Any] 						= self.pos_y - self.goal_y
              if HEURISTIC == 1:
                     return abs(UpperCamelCase__  ) + abs(UpperCamelCase__  )
              else:
                     return sqrt(dy**2 + dx**2  )




       def __lt__(							self:       Optional[int]    ,							UpperCamelCase__:       Node  ):
              return self.f_cost < other.f_cost



class _lowercase			:




       def __init__(							self:       str    ,							UpperCamelCase__:       TPosition    ,							UpperCamelCase__:       TPosition  ):
              lowerCamelCase__      :			Tuple 						= Node(start[1]    ,							start[0]    ,							goal[1]    ,							goal[0]    ,							0    ,							UpperCamelCase__  )
              lowerCamelCase__      :			Union[str, Any] 						= Node(goal[1]    ,							goal[0]    ,							goal[1]    ,							goal[0]    ,							99_999    ,							UpperCamelCase__  )

              lowerCamelCase__      :			Union[str, Any] 						= [self.start]
              lowerCamelCase__      :			list[Node] 						= []

              lowerCamelCase__      :			List[Any] 						= False




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

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

                     self.closed_nodes.append(UpperCamelCase__  )
                     lowerCamelCase__      :			Tuple 						= self.get_successors(UpperCamelCase__  )

                     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(UpperCamelCase__  )
                            else:
                                   # retrieve the best current path
                                   lowerCamelCase__      :			int 						= self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__  )  )

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

              return [self.start.pos]




       def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Node  ):
              lowerCamelCase__      :			List[str] 						= []
              for action in delta:
                     lowerCamelCase__      :			List[str] 						= parent.pos_x + action[1]
                     lowerCamelCase__      :			Tuple 						= 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    ,							parent.g_cost + 1    ,							UpperCamelCase__    ,							)  )
              return successors




       def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Node | None  ):
              lowerCamelCase__      :			Dict 						= node
              lowerCamelCase__      :			Optional[Any] 						= []
              while current_node is not None:
                     path.append((current_node.pos_y, current_node.pos_x)  )
                     lowerCamelCase__      :			List[Any] 						= current_node.parent
              path.reverse()
              return path



class _lowercase			:




       def __init__(							self:       Dict    ,							UpperCamelCase__:       TPosition    ,							UpperCamelCase__:       TPosition  ):
              lowerCamelCase__      :			Dict 						= AStar(UpperCamelCase__    ,							UpperCamelCase__  )
              lowerCamelCase__      :			str 						= AStar(UpperCamelCase__    ,							UpperCamelCase__  )
              lowerCamelCase__      :			Optional[Any] 						= False




       def 	lowerCamelCase_       (							self:       List[str]  ):
              while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                     self.fwd_astar.open_nodes.sort()
                     self.bwd_astar.open_nodes.sort()
                     lowerCamelCase__      :			List[Any] 						= self.fwd_astar.open_nodes.pop(0  )
                     lowerCamelCase__      :			List[Any] 						= self.bwd_astar.open_nodes.pop(0  )

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

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

                     lowerCamelCase__      :			Optional[Any] 						= current_bwd_node
                     lowerCamelCase__      :			Optional[int] 						= current_fwd_node

                     lowerCamelCase__      :			Optional[int] 						= {
                         self.fwd_astar: self.fwd_astar.get_successors(UpperCamelCase__  ),
                         self.bwd_astar: self.bwd_astar.get_successors(UpperCamelCase__  ),
                     }

                     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(UpperCamelCase__  )
                                   else:
                                          # retrieve the best current path
                                          lowerCamelCase__      :			Union[str, Any] 						= astar.open_nodes.pop(
                                              astar.open_nodes.index(UpperCamelCase__  )  )

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

              return [self.fwd_astar.start.pos]




       def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Node    ,							UpperCamelCase__:       Node  ):
              lowerCamelCase__      :			Optional[Any] 						= self.fwd_astar.retrace_path(UpperCamelCase__  )
              lowerCamelCase__      :			List[Any] 						= self.bwd_astar.retrace_path(UpperCamelCase__  )
              bwd_path.pop()
              bwd_path.reverse()
              lowerCamelCase__      :			Any 						= fwd_path + bwd_path
              return path


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

   _A			:    Optional[Any]					  =time.time()
   _A			:    List[Any]					  =AStar(init, goal)
   _A			:    Dict					  =a_star.search()
   _A			:    Any					  =time.time() - start_time
   print(F'AStar execution time = {end_time:f} seconds')

   _A			:    List[Any]					  =time.time()
   _A			:    str					  =BidirectionalAStar(init, goal)
   _A			:    Optional[Any]					  =time.time() - bd_start_time
   print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')









'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			int 						= loader
														lowerCamelCase__      :			str 						= infer
														lowerCamelCase__      :			Optional[int] 						= params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					lowerCamelCase__      :			Optional[int] 						= None
														lowerCamelCase__      :			int 						= loader_batch_size

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					lowerCamelCase__      :			int 						= {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			Tuple 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			str 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			int 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			lowerCamelCase__      :			List[str] 						= None
																												elif isinstance(element[self._loader_batch_index]    ,							torch.Tensor  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			lowerCamelCase__      :			Optional[Any] 						= element[self._loader_batch_index].unsqueeze(0  )
																												elif isinstance(element[self._loader_batch_index]    ,							np.ndarray  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			lowerCamelCase__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														lowerCamelCase__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**self.params  )
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												lowerCamelCase__      :			Union[str, Any] 						= observed_batch_size
																					# Setting internal index to unwrap the batch
																					lowerCamelCase__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= next(self.subiterator  )
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					lowerCamelCase__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# Extremely similar to PipelineIterator in its unpacking mechanism
														# BUT, we have an extra required item which is the presence of `is_last`
														# That is because everything is flattened by `PipelineChunkIterator` we
														# need to keep track of how to regroup here in the original `process`
														# boundaries so that `process` and `postprocess` see the same data.

														# This iterator accumulates items (possibly while unbatching) until it
														# its a `is_last` and then just passes it on to the caller.
														lowerCamelCase__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												lowerCamelCase__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			lowerCamelCase__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




							def __len__(							self:       str  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

