



'''simple docstring'''
from __future__ import annotations

snake_case_					:      Any    			=    list[tuple[int, int]]

snake_case_					:      Dict    			=    [
    [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],
]

snake_case_					:      Tuple    			=    ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				lowercase__      :





	def __init__(					self   :							List[Any]					,lowerCamelCase__   :							int					,lowerCamelCase__   :							int					,lowerCamelCase__   :							int					,lowerCamelCase__   :							int					,lowerCamelCase__   :							float					,lowerCamelCase__   :							Node | None					,):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  pos_x
							_UpperCamelCase   :						List[str]												=  pos_y
							_UpperCamelCase   :						Optional[Any]												=  (pos_y, pos_x)
							_UpperCamelCase   :						Dict												=  goal_x
							_UpperCamelCase   :						List[str]												=  goal_y
							_UpperCamelCase   :						Optional[Any]												=  g_cost
							_UpperCamelCase   :						int												=  parent
							_UpperCamelCase   :						Any												=  self.calculate_heuristic()





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  abs(self.pos_x - self.goal_x					)
							_UpperCamelCase   :						List[str]												=  abs(self.pos_y - self.goal_y					)
							return dx + dy





	def __lt__(					self   :							Union[str, Any]					,lowerCamelCase__   :							int					):







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






class 				lowercase__      :





	def __init__(					self   :							str					,lowerCamelCase__   :							tuple[int, int]					,lowerCamelCase__   :							tuple[int, int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  Node(start[1]					,start[0]					,goal[1]					,goal[0]					,0					,lowerCamelCase__					)
							_UpperCamelCase   :						str												=  Node(goal[1]					,goal[0]					,goal[1]					,goal[0]					,99999					,lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  [self.start]
							_UpperCamelCase   :						list[Node]												=  []

							_UpperCamelCase   :						Dict												=  False





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													_UpperCamelCase   :						List[str]												=  self.open_nodes.pop(0					)

													if current_node.pos == self.target.pos:
																			_UpperCamelCase   :						List[Any]												=  True
																			return self.retrace_path(lowerCamelCase__					)

													self.closed_nodes.append(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  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
																									_UpperCamelCase   :						Any												=  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__					)

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





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Node					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  []
							for action in delta:
													_UpperCamelCase   :						Dict												=  parent.pos_x + action[1]
													_UpperCamelCase   :						str												=  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       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							Node | None					):







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


if __name__ == "__main__":
					snake_case_					:      Any    			=    (0, 0)
					snake_case_					:      str    			=    (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print('------')

					snake_case_					:      List[str]    			=    GreedyBestFirst(init, goal)
					snake_case_					:      Optional[int]    			=    greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															snake_case_					:      Optional[Any]    			=    2

										for elem in grid:
															print(elem)







'''simple docstring'''
from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


snake_case_					:      List[Any]    			=    logging.get_logger(__name__)






@add_end_docstrings(lowercase  )
class 				lowercase__      (				lowercase  ):





	def __init__(					self   :							Tuple					,**lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)

							if self.framework != "pt":
													raise ValueError(F'The {self.__class__} is only available in PyTorch.'					)
							# No specific FOR_XXX available yet





	def __call__(					self   :							Dict					,lowerCamelCase__   :							Union[np.ndarray, bytes, str]					,**lowerCamelCase__   :							Any					):







							'''simple docstring'''
							return super().__call__(lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					,**lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  {}
							if "candidate_labels" in kwargs:
													_UpperCamelCase   :						Any												=  kwargs['candidate_labels']
							if "hypothesis_template" in kwargs:
													_UpperCamelCase   :						Any												=  kwargs['hypothesis_template']

							return preprocess_params, {}, {}





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str=None					,lowerCamelCase__   :							List[Any]="This is a sound of {}."					):







							'''simple docstring'''
							if isinstance(lowerCamelCase__					,lowerCamelCase__					):
													if audio.startswith('http://'					) or audio.startswith('https://'					):
																			# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																			# like http_huggingface_co.png
																			_UpperCamelCase   :						Optional[Any]												=  requests.get(lowerCamelCase__					).content
													else:
																			with open(lowerCamelCase__					,'rb'					) as f:
																									_UpperCamelCase   :						List[str]												=  f.read()

							if isinstance(lowerCamelCase__					,lowerCamelCase__					):
													_UpperCamelCase   :						int												=  ffmpeg_read(lowerCamelCase__					,self.feature_extractor.sampling_rate					)

							if not isinstance(lowerCamelCase__					,np.ndarray					):
													raise ValueError('We expect a numpy ndarray as input'					)
							if len(audio.shape					) != 1:
													raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'					)

							_UpperCamelCase   :						Dict												=  self.feature_extractor(
							    [audio]					,sampling_rate=self.feature_extractor.sampling_rate					,return_tensors='pt'					)
							_UpperCamelCase   :						List[Any]												=  candidate_labels
							_UpperCamelCase   :						Tuple												=  [hypothesis_template.format(lowerCamelCase__					) for x in candidate_labels]
							_UpperCamelCase   :						Dict												=  self.tokenizer(lowerCamelCase__					,return_tensors=self.framework					,padding=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  [text_inputs]
							return inputs





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  model_inputs.pop('candidate_labels'					)
							_UpperCamelCase   :						str												=  model_inputs.pop('text_inputs'					)
							if isinstance(text_inputs[0]					,lowerCamelCase__					):
													_UpperCamelCase   :						Union[str, Any]												=  text_inputs[0]
							else:
													# Batching case.
													_UpperCamelCase   :						int												=  text_inputs[0][0]

							_UpperCamelCase   :						List[str]												=  self.model(**lowerCamelCase__					,**lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  {
							    'candidate_labels': candidate_labels,
							    'logits': outputs.logits_per_audio,
							}
							return model_outputs





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  model_outputs.pop('candidate_labels'					)
							_UpperCamelCase   :						int												=  model_outputs['logits'][0]

							if self.framework == "pt":
													_UpperCamelCase   :						Tuple												=  logits.softmax(dim=0					)
													_UpperCamelCase   :						str												=  probs.tolist()
							else:
													raise ValueError('`tf` framework not supported.'					)

							_UpperCamelCase   :						Union[str, Any]												=  [
							    {'score': score, 'label': candidate_label}
							    for score, candidate_label in sorted(zip(lowerCamelCase__					,lowerCamelCase__					)					,key=lambda lowerCamelCase__					: -x[0]					)
							]
							return result


