



from __future__ import annotations

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

lowerCAmelCase:      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],
]

lowerCAmelCase:      Optional[Any]		 =		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class 							a__:







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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  abs(self.pos_x - self.goal_x			)
      a		:      Any       =  abs(self.pos_y - self.goal_y			)
      return dx + dy







    def __lt__(   self				:						Tuple		,    __snake_case				:						List[Any]			):
      return self.f_cost < other.f_cost

class 							a__:







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						tuple[int, int]		,    __snake_case				:						tuple[int, int]			):
      a		:      List[str]       =  Node(start[1]		,    start[0]		,    goal[1]		,    goal[0]		,    0		,    __snake_case			)
      a		:      Any       =  Node(goal[1]		,    goal[0]		,    goal[1]		,    goal[0]		,    9_99_99		,    __snake_case			)

      a		:      Any       =  [self.start]
      a		:      list[Node]       =  []

      a		:      List[Any]       =  False







    def     lowercase_ (   self				:						List[str]			):
      while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        a		:      Dict       =  self.open_nodes.pop(0			)

        if current_node.pos == self.target.pos:
          a		:      Union[str, Any]       =  True
          return self.retrace_path(__snake_case			)

        self.closed_nodes.append(__snake_case			)
        a		:      Optional[Any]       =  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
            a		:      Optional[int]       =  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     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Node			):
      a		:      Tuple       =  []
      for action in delta:
        a		:      Optional[int]       =  parent.pos_x + action[1]
        a		:      Tuple       =  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     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Node | None			):
      a		:      int       =  node
      a		:      Optional[int]       =  []
      while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)			)
        a		:      Tuple       =  current_node.parent
      path.reverse()
      return path


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

     print('------')

     lowerCAmelCase:      List[str]		 =		GreedyBestFirst(init, goal)
     lowerCAmelCase:      str		 =		greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               lowerCAmelCase:      Tuple		 =		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


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

@add_end_docstrings(lowerCamelCase__			)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    **__snake_case				:						List[str]			):
      super().__init__(**__snake_case			)

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







    def __call__(   self				:						Any		,    __snake_case				:						Union[np.ndarray, bytes, str]		,    **__snake_case				:						Optional[Any]			):
      return super().__call__(__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Dict		,    **__snake_case				:						Optional[int]			):
      a		:      str       =  {}
      if "candidate_labels" in kwargs:
        a		:      Optional[int]       =  kwargs['candidate_labels']
      if "hypothesis_template" in kwargs:
        a		:      Any       =  kwargs['hypothesis_template']

      return preprocess_params, {}, {}







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

      if isinstance(__snake_case		,    __snake_case			):
        a		:      Optional[Any]       =  ffmpeg_read(__snake_case		,    self.feature_extractor.sampling_rate			)

      if not isinstance(__snake_case		,    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'			)

      a		:      str       =  self.feature_extractor(
          [audio]		,    sampling_rate=self.feature_extractor.sampling_rate		,    return_tensors='pt'			)
      a		:      Tuple       =  candidate_labels
      a		:      Union[str, Any]       =  [hypothesis_template.format(__snake_case			) for x in candidate_labels]
      a		:      int       =  self.tokenizer(__snake_case		,    return_tensors=self.framework		,    padding=__snake_case			)
      a		:      Any       =  [text_inputs]
      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str			):
      a		:      Any       =  model_inputs.pop('candidate_labels'			)
      a		:      List[Any]       =  model_inputs.pop('text_inputs'			)
      if isinstance(text_inputs[0]		,    __snake_case			):
        a		:      str       =  text_inputs[0]
      else:
        # Batching case.
        a		:      Dict       =  text_inputs[0][0]

      a		:      List[Any]       =  self.model(**__snake_case		,    **__snake_case			)

      a		:      Any       =  {
          'candidate_labels': candidate_labels,
          'logits': outputs.logits_per_audio,
      }
      return model_outputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str			):
      a		:      Optional[int]       =  model_outputs.pop('candidate_labels'			)
      a		:      List[str]       =  model_outputs['logits'][0]

      if self.framework == "pt":
        a		:      str       =  logits.softmax(dim=0			)
        a		:      List[Any]       =  probs.tolist()
      else:
        raise ValueError('`tf` framework not supported.'			)

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