
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


lowercase						:      List[str]									= logging.get_logger(__name__)




@add_end_docstrings(__lowercase)
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     **a	:Optional[Any]		)	->				List[Any]:
								super().__init__(**__UpperCamelCase		)

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







	def __call__(      self	:Optional[int]  ,     a	:Union[np.ndarray, bytes, str]  ,     **a	:Tuple		)	->				List[str]:
								return super().__call__(__UpperCamelCase  ,     **__UpperCamelCase		)







	def      _lowerCamelCase       (      self	:Any  ,     **a	:Any		)	->				Union[str, Any]:
								__UpperCamelCase			:     Dict         =      {}
								if "candidate_labels" in kwargs:
															__UpperCamelCase			:     List[str]         =      kwargs["candidate_labels"]
								if "hypothesis_template" in kwargs:
															__UpperCamelCase			:     str         =      kwargs["hypothesis_template"]

								return preprocess_params, {}, {}







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Union[str, Any]  ,     a	:List[Any]=None  ,     a	:Tuple="This is a sound of {}."		)	->				int:
								if isinstance(__UpperCamelCase  ,     __UpperCamelCase		):
															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			:     Any         =      requests.get(__UpperCamelCase		).content
															else:
																						with open(__UpperCamelCase  ,     "rb"		) as f:
																													__UpperCamelCase			:     str         =      f.read()

								if isinstance(__UpperCamelCase  ,     __UpperCamelCase		):
															__UpperCamelCase			:     Tuple         =      ffmpeg_read(__UpperCamelCase  ,     self.feature_extractor.sampling_rate		)

								if not isinstance(__UpperCamelCase  ,     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			:     Tuple         =      self.feature_extractor(
								    [audio]  ,     sampling_rate=self.feature_extractor.sampling_rate  ,     return_tensors="pt"		)
								__UpperCamelCase			:     Tuple         =      candidate_labels
								__UpperCamelCase			:     Dict         =      [hypothesis_template.format(__UpperCamelCase		) for x in candidate_labels]
								__UpperCamelCase			:     Tuple         =      self.tokenizer(__UpperCamelCase  ,     return_tensors=self.framework  ,     padding=__UpperCamelCase		)
								__UpperCamelCase			:     Optional[Any]         =      [text_inputs]
								return inputs







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[int]		)	->				Any:
								__UpperCamelCase			:     Optional[int]         =      model_inputs.pop("candidate_labels"		)
								__UpperCamelCase			:     List[str]         =      model_inputs.pop("text_inputs"		)
								if isinstance(text_inputs[0]  ,     __UpperCamelCase		):
															__UpperCamelCase			:     Optional[Any]         =      text_inputs[0]
								else:
															# Batching case.
															__UpperCamelCase			:     Optional[int]         =      text_inputs[0][0]

								__UpperCamelCase			:     Dict         =      self.model(**__UpperCamelCase  ,     **__UpperCamelCase		)

								__UpperCamelCase			:     int         =      {
								    "candidate_labels": candidate_labels,
								    "logits": outputs.logits_per_audio,
								}
								return model_outputs







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:Any		)	->				List[Any]:
								__UpperCamelCase			:     Union[str, Any]         =      model_outputs.pop("candidate_labels"		)
								__UpperCamelCase			:     Union[str, Any]         =      model_outputs["logits"][0]

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

								__UpperCamelCase			:     Any         =      [
								    {"score": score, "label": candidate_label}
								    for score, candidate_label in sorted(zip(__UpperCamelCase  ,     __UpperCamelCase		)  ,     key=lambda a		: -x[0]		)
								]
								return result

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      Optional[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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







	def __lt__(      self	:Union[str, Any]  ,     a	:Dict		)	->				bool:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

															if current_node.pos == self.target.pos:
																						__UpperCamelCase			:     Dict         =      True
																						return self.retrace_path(a		)

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, Any]         =      self.get_successors(a		)

															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(a		)
																						else:
																													# retrieve the best current path
																													__UpperCamelCase			:     Union[str, Any]         =      self.open_nodes.pop(self.open_nodes.index(a		)		)

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

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







	def      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      parent.pos_x + action[1]
															__UpperCamelCase			:     List[Any]         =      parent.pos_y + action[0]

															if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(a		) - 1):
																						continue

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

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







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


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

		print('------')

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

				for elem in grid:
						print(elem)