
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class       a		(   __UpperCAmelCase					):
							lowercase_		:						str   							=  'EncodecFeatureExtractor'
							lowercase_		:						List[Any]   							=  ('T5Tokenizer', 'T5TokenizerFast')







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







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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Dict=None      ,				snake_case__  :  Optional[Any]=None      ,				snake_case__  :  str=True ):
								"""simple docstring"""







								return self.tokenizer.get_decoder_prompt_ids(task=snake_case__      ,				language=snake_case__      ,				no_timestamps=snake_case__ )







							def __call__(						self  :  Tuple      ,				*snake_case__  :  Union[str, Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								if self._in_target_context_manager:
									return self.current_processor(*snake_case__      ,				**snake_case__ )

								__lowerCAmelCase         =							kwargs.pop("audio"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("sampling_rate"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("text"      ,				snake_case__ )
								if len(snake_case__ ) > 0:
									__lowerCAmelCase         =							args[0]
									__lowerCAmelCase         =							args[1:]

								if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process." )

								if text is not None:
									__lowerCAmelCase         =							self.tokenizer(snake_case__      ,				**snake_case__ )

								if audio is not None:
									__lowerCAmelCase         =							self.feature_extractor(snake_case__      ,				*snake_case__      ,				sampling_rate=snake_case__      ,				**snake_case__ )

								if audio is None:
									return inputs

								elif text is None:
									return audio_inputs

								else:
									__lowerCAmelCase         =							audio_inputs["input_values"]
									if "padding_mask" in audio_inputs:
										__lowerCAmelCase         =							audio_inputs["padding_mask"]
									return inputs







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







								__lowerCAmelCase         =							kwargs.pop("audio"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("padding_mask"      ,				snake_case__ )

								if len(snake_case__ ) > 0:
									__lowerCAmelCase         =							args[0]
									__lowerCAmelCase         =							args[1:]

								if audio_values is not None:
									return self._decode_audio(snake_case__      ,				padding_mask=snake_case__ )
								else:
									return self.tokenizer.batch_decode(*snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Any      ,				*snake_case__  :  Any      ,				**snake_case__  :  str ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Optional = None ):
								"""simple docstring"""







								__lowerCAmelCase         =							to_numpy(snake_case__ )
								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							audio_values.shape

								if padding_mask is None:
									return list(snake_case__ )

								__lowerCAmelCase         =							to_numpy(snake_case__ )

								# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
								# token (so that the generated audio values are **not** treated as padded tokens)
								__lowerCAmelCase         =							seq_len - padding_mask.shape[-1]
								__lowerCAmelCase         =							1 - self.feature_extractor.padding_value
								__lowerCAmelCase         =							np.pad(snake_case__      ,				((0, 0), (0, difference))      ,				"constant"      ,				constant_values=snake_case__ )

								__lowerCAmelCase         =							audio_values.tolist()
								for i in range(snake_case__ ):
									__lowerCAmelCase         =							np.asarray(audio_values[i] )[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									__lowerCAmelCase         =							sliced_audio.reshape(snake_case__      ,				-1 )

								return audio_values








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)






