"""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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["input_features"]







				def __init__( self    :       Any			,					lowerCamelCase_    :       List[Any]=8_0			,					lowerCamelCase_    :       Union[str, Any]=1_6_0_0_0			,					lowerCamelCase_    :       Tuple=1_6_0			,					lowerCamelCase_    :       Any=3_0			,					lowerCamelCase_    :       Dict=4_0_0			,					lowerCamelCase_    :       Optional[int]=0.0			,					lowerCamelCase_    :       List[str]=False			,					**lowerCamelCase_    :       Optional[Any]			,					):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       np.array						):

					"""simple docstring"""






					_lowercase :    List[str]							=							spectrogram(
					    lowerCamelCase_			,					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'			,					)
					_lowercase :    Any							=							log_spec[:, :-1]
					_lowercase :    Optional[int]							=							np.maximum(lowerCamelCase_			,					log_spec.max() - 8.0						)
					_lowercase :    Optional[int]							=							(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 							__UpperCAmelCase					( lowerCamelCase_    :       List[np.ndarray]			,					lowerCamelCase_    :       List[np.ndarray]			,					lowerCamelCase_    :       float = 0.0						):

					"""simple docstring"""






					if attention_mask is not None:
						_lowercase :    Optional[int]							=							np.array(lowerCamelCase_			,					np.intaa						)
						_lowercase :    Union[str, Any]							=							[]

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

							normed_input_values.append(lowerCamelCase_						)
					else:
						_lowercase :    Union[str, Any]							=							[(x - x.mean()) / np.sqrt(x.var() + 1E-7						) for x in input_values]

					return normed_input_values







				def __call__( self    :       Optional[Any]			,					lowerCamelCase_    :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[str] = "max_length"			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					**lowerCamelCase_    :       int			,					):

					"""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.'						)

					_lowercase :    Optional[int]							=							isinstance(lowerCamelCase_			,					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}'''						)
					_lowercase :    Optional[Any]							=							is_batched_numpy or (
					    isinstance(lowerCamelCase_			,					(list, tuple)						) and (isinstance(raw_speech[0]			,					(np.ndarray, tuple, list)						))
					)

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

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

					_lowercase :    str							=							BatchFeature({'input_features': raw_speech}						)

					# convert into correct format for padding

					_lowercase :    Union[str, Any]							=							self.pad(
					    lowerCamelCase_			,					padding=lowerCamelCase_			,					max_length=max_length if max_length else self.n_samples			,					truncation=lowerCamelCase_			,					pad_to_multiple_of=lowerCamelCase_			,					return_attention_mask=return_attention_mask or do_normalize			,					)

					# zero-mean and unit-variance normalization
					if do_normalize:
						_lowercase :    Union[str, Any]							=							self.zero_mean_unit_var_norm(
						    padded_inputs['input_features']			,					attention_mask=padded_inputs['attention_mask']			,					padding_value=self.padding_value			,					)
						_lowercase :    Dict							=							np.stack(padded_inputs['input_features']			,					axis=0						)

					# make sure list is in array format
					_lowercase :    Optional[int]							=							padded_inputs.get('input_features'						).transpose(2			,					0			,					1						)

					_lowercase :    List[Any]							=							[self._np_extract_fbank_features(lowerCamelCase_						) for waveform in input_features[0]]

					if isinstance(input_features[0]			,					lowerCamelCase_						):
						_lowercase :    Union[str, Any]							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for feature in input_features]
					else:
						_lowercase :    List[Any]							=							input_features

					if return_attention_mask:
						# rescale from sample (48000) to feature (3000)
						_lowercase :    int							=							padded_inputs['attention_mask'][:, :: self.hop_length]

					if return_tensors is not None:
						_lowercase :    Tuple							=							padded_inputs.convert_to_tensors(lowerCamelCase_						)

					return padded_inputs







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    Dict							=							copy.deepcopy(self.__dict__						)
					_lowercase :    Dict							=							self.__class__.__name__
					if "mel_filters" in output:
						del output["mel_filters"]
					return output




"""simple docstring"""







from __future__ import annotations

SCREAMING_SNAKE_CASE									=						[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



