





from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase :    Optional[Any]  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["input_values", "padding_mask"]






							def __init__( self     :					List[Any],			_UpperCAmelCase     :					int = 1,			_UpperCAmelCase     :					int = 2_4_0_0_0,			_UpperCAmelCase     :					float = 0.0,			_UpperCAmelCase     :					float = None,			_UpperCAmelCase     :					float = None,			**_UpperCAmelCase     :					str,			)				->			Tuple:





														"""simple docstring"""
														super().__init__(feature_size=_UpperCAmelCase,			sampling_rate=_UpperCAmelCase,			padding_value=_UpperCAmelCase,			**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						chunk_length_s
														SCREAMING_SNAKE_CASE__       :      int     =						overlap






							@property
							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate						)






							@property
							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1,			int((1.0 - self.overlap) * self.chunk_length						)						)






							def __call__( self     :					Any,			_UpperCAmelCase     :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],			_UpperCAmelCase     :					Optional[Union[bool, str, PaddingStrategy]] = None,			_UpperCAmelCase     :					Optional[bool] = False,			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					Optional[Union[str, TensorType]] = None,			_UpperCAmelCase     :					Optional[int] = None,			)				->			BatchFeature:





														"""simple docstring"""
														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																												    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
																												    F''' {self.sampling_rate} and not {sampling_rate}.'''						)
														else:
																					logger.warning(
																					    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																					    "Failing to do so can result in silent errors that might be hard to debug."						)

														if padding and truncation:
																					raise ValueError("Both padding and truncation were set. Make sure you only set one."						)
														elif padding is None:
																					# by default let's pad the inputs
																					SCREAMING_SNAKE_CASE__       :      str     =						True

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						bool(
														    isinstance(_UpperCAmelCase,			(list, tuple)						) and (isinstance(raw_audio[0],			(np.ndarray, tuple, list)						))						)

														if is_batched:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						[np.asarray(_UpperCAmelCase,			dtype=np.floataa						).T for audio in raw_audio]
														elif not is_batched and not isinstance(_UpperCAmelCase,			np.ndarray						):
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						np.asarray(_UpperCAmelCase,			dtype=np.floataa						)
														elif isinstance(_UpperCAmelCase,			np.ndarray						) and raw_audio.dtype is np.dtype(np.floataa						):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						raw_audio.astype(np.floataa						)

														# always return batch
														if not is_batched:
																					SCREAMING_SNAKE_CASE__       :      int     =						[np.asarray(_UpperCAmelCase						).T]

														# verify inputs are valid
														for idx, example in enumerate(_UpperCAmelCase						):
																					if example.ndim > 2:
																												raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''						)
																					if self.feature_size == 1 and example.ndim != 1:
																												raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''						)
																					if self.feature_size == 2 and example.shape[-1] != 2:
																												raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BatchFeature({"input_values": raw_audio}						)
														if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																					if truncation:
																												SCREAMING_SNAKE_CASE__       :      Any     =						min(array.shape[0] for array in raw_audio						)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						int(np.floor(max_length / self.chunk_stride						)						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						(nb_step - 1) * self.chunk_stride + self.chunk_length
																					elif padding:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						max(array.shape[0] for array in raw_audio						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						int(np.ceil(max_length / self.chunk_stride						)						)
																												SCREAMING_SNAKE_CASE__       :      str     =						(nb_step - 1) * self.chunk_stride + self.chunk_length
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						"max_length"
																					else:
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						input_values

        # normal padding on batch
														if padded_inputs is None:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(
																					    _UpperCAmelCase,			max_length=_UpperCAmelCase,			truncation=_UpperCAmelCase,			padding=_UpperCAmelCase,			return_attention_mask=_UpperCAmelCase,			)
																					if padding:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						padded_inputs.pop("attention_mask"						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						[]
														for example in padded_inputs.pop("input_values"						):
																					if self.feature_size == 1:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						example[..., None]
																					input_values.append(example.T						)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						input_values
														if return_tensors is not None:
																					SCREAMING_SNAKE_CASE__       :      int     =						padded_inputs.convert_to_tensors(_UpperCAmelCase						)

														return padded_inputs













from __future__ import annotations

import time

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

_lowerCamelCase :    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],
]

_lowerCamelCase :    str  							= [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Node | None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pos_y
														SCREAMING_SNAKE_CASE__       :      str     =						(pos_y, pos_x)
														SCREAMING_SNAKE_CASE__       :      Dict     =						goal_x
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						goal_y
														SCREAMING_SNAKE_CASE__       :      str     =						parent


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					tuple[int, int],			_UpperCAmelCase     :					tuple[int, int]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						Node(start[1],			start[0],			goal[1],			goal[0],			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						Node(goal[1],			goal[0],			goal[1],			goal[0],			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.start]
														SCREAMING_SNAKE_CASE__       :      str     =						False






							def  A_     ( self     :					Dict						)				->			Path | None:





														"""simple docstring"""
														while self.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.node_queue.pop(0						)

																					if current_node.pos == self.target.pos:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						True
																												return self.retrace_path(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Union[str, 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  A_     ( self     :					Any,			_UpperCAmelCase     :					Node						)				->			list[Node]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						[]
														for action in delta:
																					SCREAMING_SNAKE_CASE__       :      str     =						parent.pos_x + action[1]
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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  A_     ( self     :					List[str],			_UpperCAmelCase     :					Node | None						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x)						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_node.parent
														path.reverse()
														return path


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						BreadthFirstSearch(_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						False






							def  A_     ( self     :					List[str]						)				->			Path | None:





														"""simple docstring"""
														while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																					SCREAMING_SNAKE_CASE__       :      int     =						self.fwd_bfs.node_queue.pop(0						)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.bwd_bfs.node_queue.pop(0						)

																					if current_bwd_node.pos == current_fwd_node.pos:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						True
																												return self.retrace_bidirectional_path(
																												    _UpperCAmelCase,			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_bwd_node
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						current_fwd_node

																					SCREAMING_SNAKE_CASE__       :      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  A_     ( self     :					int,			_UpperCAmelCase     :					Node,			_UpperCAmelCase     :					Node						)				->			Path:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.fwd_bfs.retrace_path(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						self.bwd_bfs.retrace_path(_UpperCAmelCase						)
														bwd_path.pop()
														bwd_path.reverse()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						fwd_path + bwd_path
														return path


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

		doctest.testmod()
		_lowerCamelCase :    Optional[Any]  							= (0, 0)
		_lowerCamelCase :    Dict  							= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		_lowerCamelCase :    Dict  							= time.time()
		_lowerCamelCase :    List[Any]  							= BreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bfs.search()
		_lowerCamelCase :    Dict  							= time.time() - start_bfs_time

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

		_lowerCamelCase :    str  							= time.time()
		_lowerCamelCase :    Dict  							= BidirectionalBreadthFirstSearch(init, goal)
		_lowerCamelCase :    Optional[Any]  							= bd_bfs.search()
		_lowerCamelCase :    Optional[Any]  							= time.time() - start_bd_bfs_time

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






