


"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__snake_case         =					logging.get_logger(__name__)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: str													=						['''input_features''']







   def __init__(			self		,			UpperCamelCase__=80		,			UpperCamelCase__=1_6000		,			UpperCamelCase__=160		,			UpperCamelCase__=30		,			UpperCamelCase__=400		,			UpperCamelCase__=0.0		,			UpperCamelCase__=False		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(
           feature_size=UpperCamelCase__		,			sampling_rate=UpperCamelCase__		,			padding_value=UpperCamelCase__		,			return_attention_mask=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   int         =					n_fft
       snake_case					:   int         =					hop_length
       snake_case					:   Any         =					chunk_length
       snake_case					:   Union[str, Any]         =					chunk_length * sampling_rate
       snake_case					:   str         =					self.n_samples // hop_length
       snake_case					:   Any         =					sampling_rate
       snake_case					:   Tuple         =					mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2		,			num_mel_filters=UpperCamelCase__		,			min_frequency=0.0		,			max_frequency=8000.0		,			sampling_rate=UpperCamelCase__		,			norm="slaney"		,			mel_scale="slaney"		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	np.ndarray:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					spectrogram(
           UpperCamelCase__		,			window_function(self.n_fft		,			"hann"		)		,			frame_length=self.n_fft		,			hop_length=self.hop_length		,			power=2.0		,			mel_filters=self.mel_filters		,			log_mel="log10"		,			)
       snake_case					:   Tuple         =					log_spec[:, :-1]
       snake_case					:   Optional[Any]         =					np.maximum(UpperCamelCase__		,			log_spec.max() - 8.0		)
       snake_case					:   Any         =					(log_spec + 4.0) / 4.0
       return log_spec







   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = 0.0		)			->	List[np.ndarray]:

       '''simple docstring'''




       if attention_mask is not None:
           snake_case					:   Optional[Any]         =					np.array(UpperCamelCase__		,			np.intaa		)
           snake_case					:   int         =					[]

           for vector, length in zip(UpperCamelCase__		,			attention_mask.sum(-1		)		):
               snake_case					:   Optional[Any]         =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
               if length < normed_slice.shape[0]:
                   snake_case					:   Tuple         =					padding_value

               normed_input_values.append(UpperCamelCase__		)
       else:
           snake_case					:   Tuple         =					[(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

       return normed_input_values







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__ = True		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = "max_length"		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	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.__class__.__name__} was trained using a'
                   F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                   F' was sampled with {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."		)

       snake_case					:   Optional[Any]         =					isinstance(UpperCamelCase__		,			np.ndarray		) and len(raw_speech.shape		) > 1
       if is_batched_numpy and len(raw_speech.shape		) > 2:
           raise ValueError(F'Only mono-channel audio is supported for input to {self}'		)
       snake_case					:   List[str]         =					is_batched_numpy or (
           isinstance(UpperCamelCase__		,			(list, tuple)		) and (isinstance(raw_speech[0]		,			(np.ndarray, tuple, list)		))
       )

       if is_batched:
           snake_case					:   str         =					[np.asarray([speech]		,			dtype=np.floataa		).T for speech in raw_speech]
       elif not is_batched and not isinstance(UpperCamelCase__		,			np.ndarray		):
           snake_case					:   Any         =					np.asarray(UpperCamelCase__		,			dtype=np.floataa		)
       elif isinstance(UpperCamelCase__		,			np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
           snake_case					:   Union[str, Any]         =					raw_speech.astype(np.floataa		)

       # always return batch
       if not is_batched:
           snake_case					:   Any         =					[np.asarray([raw_speech]		).T]

       snake_case					:   List[str]         =					BatchFeature({"input_features": raw_speech}		)

       # convert into correct format for padding

       snake_case					:   Optional[int]         =					self.pad(
           UpperCamelCase__		,			padding=UpperCamelCase__		,			max_length=max_length if max_length else self.n_samples		,			truncation=UpperCamelCase__		,			pad_to_multiple_of=UpperCamelCase__		,			return_attention_mask=return_attention_mask or do_normalize		,			)

       # zero-mean and unit-variance normalization
       if do_normalize:
           snake_case					:   List[str]         =					self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]		,			attention_mask=padded_inputs["attention_mask"]		,			padding_value=self.padding_value		,			)
           snake_case					:   Optional[int]         =					np.stack(padded_inputs["input_features"]		,			axis=0		)

       # make sure list is in array format
       snake_case					:   int         =					padded_inputs.get("input_features"		).transpose(2		,			0		,			1		)

       snake_case					:   Any         =					[self._np_extract_fbank_features(UpperCamelCase__		) for waveform in input_features[0]]

       if isinstance(input_features[0]		,			UpperCamelCase__		):
           snake_case					:   Optional[Any]         =					[np.asarray(UpperCamelCase__		,			dtype=np.floataa		) for feature in input_features]
       else:
           snake_case					:   Optional[int]         =					input_features

       if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           snake_case					:   Dict         =					padded_inputs["attention_mask"][:, :: self.hop_length]

       if return_tensors is not None:
           snake_case					:   Optional[Any]         =					padded_inputs.convert_to_tensors(UpperCamelCase__		)

       return padded_inputs







   def 				lowerCamelCase	(			self		)			->	Dict[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					copy.deepcopy(self.__dict__		)
       snake_case					:   List[str]         =					self.__class__.__name__
       if "mel_filters" in output:
           del output["mel_filters"]
       return output









"""simple docstring"""



from __future__ import annotations

__snake_case         =					list[tuple[int, int]]

__snake_case         =					[
    [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         =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					pos_x
       snake_case					:   List[str]         =					pos_y
       snake_case					:   List[Any]         =					(pos_y, pos_x)
       snake_case					:   Optional[int]         =					goal_x
       snake_case					:   Dict         =					goal_y
       snake_case					:   Any         =					g_cost
       snake_case					:   List[Any]         =					parent
       snake_case					:   Union[str, Any]         =					self.calculate_heuristic()







   def 				lowerCamelCase	(			self		)			->	float:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					abs(self.pos_x - self.goal_x		)
       snake_case					:   Dict         =					abs(self.pos_y - self.goal_y		)
       return dx + dy







   def __lt__(			self		,			UpperCamelCase__		)			->	bool:

       '''simple docstring'''




       return self.f_cost < other.f_cost







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   int         =					Node(start[1]		,			start[0]		,			goal[1]		,			goal[0]		,			0		,			UpperCamelCase__		)
       snake_case					:   List[Any]         =					Node(goal[1]		,			goal[0]		,			goal[1]		,			goal[0]		,			9_9999		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					[self.start]
       snake_case					:   list[Node]         =					[]

       snake_case					:   Dict         =					False







   def 				lowerCamelCase	(			self		)			->	Path | None:

       '''simple docstring'''




       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           snake_case					:   str         =					self.open_nodes.pop(0		)

           if current_node.pos == self.target.pos:
               snake_case					:   Tuple         =					True
               return self.retrace_path(UpperCamelCase__		)

           self.closed_nodes.append(UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					self.get_successors(UpperCamelCase__		)

           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(UpperCamelCase__		)
               else:
                   # retrieve the best current path
                   snake_case					:   Dict         =					self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__		)		)

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

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	list[Node]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       for action in delta:
           snake_case					:   Union[str, Any]         =					parent.pos_x + action[1]
           snake_case					:   str         =					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		,			parent.g_cost + 1		,			UpperCamelCase__		,			)		)
       return successors







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Path:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					node
       snake_case					:   int         =					[]
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)		)
           snake_case					:   Any         =					current_node.parent
       path.reverse()
       return path


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

  print("""------""")

  __snake_case         =					GreedyBestFirst(init, goal)
  __snake_case         =					greedy_bf.search()
  if path:
    for pos_x, pos_y in path:
      __snake_case         =					2

    for elem in grid:
      print(elem)





