






"""simple docstring"""



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

import numpy as np
import torch

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


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				__UpperCAmelCase		):
							snake_case_     =							["""input_features""", """is_longer"""]


							def __init__(			self      :						Optional[int]			,    snake_case      :						Optional[int]=64			,    snake_case      :						Dict=4_8000			,    snake_case      :						List[Any]=480			,    snake_case      :						Dict=10			,    snake_case      :						Any=1024			,    snake_case      :						Optional[Any]=0.0			,    snake_case      :						Any=False			,    snake_case      :						Union[str, Any] = 0			,    snake_case      :						Union[str, Any] = 1_4000			,    snake_case      :						Dict = None			,    snake_case      :						int = "fusion"			,    snake_case      :						int = "repeatpad"			,    **snake_case      :						Tuple			,    ):
													'''simple docstring'''





													super().__init__(
													    feature_size=UpperCAmelCase_			,    sampling_rate=UpperCAmelCase_			,    padding_value=UpperCAmelCase_			,    return_attention_mask=UpperCAmelCase_			,    **UpperCAmelCase_			,    )
													A__						:  Dict		  =  top_db
													A__						:  int		  =  truncation
													A__						:  int		  =  padding
													A__						:  Union[str, Any]		  =  fft_window_size
													A__						:  Tuple		  =  (fft_window_size >> 1) + 1
													A__						:  Any		  =  hop_length
													A__						:  Tuple		  =  max_length_s
													A__						:  int		  =  max_length_s * sampling_rate
													A__						:  Union[str, Any]		  =  sampling_rate
													A__						:  str		  =  frequency_min
													A__						:  List[Any]		  =  frequency_max
													A__						:  Tuple		  =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins			,    num_mel_filters=UpperCAmelCase_			,    min_frequency=UpperCAmelCase_			,    max_frequency=UpperCAmelCase_			,    sampling_rate=UpperCAmelCase_			,    norm=UpperCAmelCase_			,    mel_scale="""htk"""			,    )
													A__						:  Optional[int]		  =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins			,    num_mel_filters=UpperCAmelCase_			,    min_frequency=UpperCAmelCase_			,    max_frequency=UpperCAmelCase_			,    sampling_rate=UpperCAmelCase_			,    norm="""slaney"""			,    mel_scale="""slaney"""			,    )


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Any			,    snake_case      :						Optional[int] = None							):
													'''simple docstring'''





													A__						:  List[Any]		  =  spectrogram(
													    UpperCAmelCase_			,    window_function(self.fft_window_size			,    """hann"""							)			,    frame_length=self.fft_window_size			,    hop_length=self.hop_length			,    power=2.0			,    mel_filters=UpperCAmelCase_			,    log_mel="""dB"""			,    )
													return log_mel_spectrogram.T


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Union[str, Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  np.array_split(list(range(0			,    total_frames - chunk_frames + 1							)							)			,    3							)
													if len(ranges[1]							) == 0:
																			# if the audio is too short, we just use the first chunk
																			A__						:  Union[str, Any]		  =  [0]
													if len(ranges[2]							) == 0:
																			# if the audio is too short, we just use the first chunk
																			A__						:  Tuple		  =  [0]
													# randomly choose index for each part
													A__						:  List[Any]		  =  np.random.choice(ranges[0]							)
													A__						:  int		  =  np.random.choice(ranges[1]							)
													A__						:  Any		  =  np.random.choice(ranges[2]							)

													A__						:  Union[str, Any]		  =  mel[idx_front : idx_front + chunk_frames, :]
													A__						:  Optional[int]		  =  mel[idx_middle : idx_middle + chunk_frames, :]
													A__						:  str		  =  mel[idx_back : idx_back + chunk_frames, :]

													A__						:  Optional[Any]		  =  torch.tensor(mel[None, None, :]							)
													A__						:  Optional[int]		  =  torch.nn.functional.interpolate(
													    UpperCAmelCase_			,    size=[chunk_frames, 64]			,    mode="""bilinear"""			,    align_corners=UpperCAmelCase_							)
													A__						:  Optional[int]		  =  mel_shrink[0][0].numpy()
													A__						:  Optional[Any]		  =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,    axis=0							)
													return mel_fusion


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Optional[Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict			,    snake_case      :						List[str]							):
													'''simple docstring'''





													if waveform.shape[0] > max_length:
																			if truncation == "rand_trunc":
																									A__						:  List[str]		  =  True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									A__						:  List[str]		  =  len(UpperCAmelCase_							) - max_length
																									A__						:  Optional[Any]		  =  np.random.randint(0			,    overflow + 1							)
																									A__						:  Optional[int]		  =  waveform[idx : idx + max_length]
																									A__						:  Any		  =  self._np_extract_fbank_features(UpperCAmelCase_			,    self.mel_filters_slaney							)[None, :]
																			elif truncation == "fusion":
																									A__						:  Tuple		  =  self._np_extract_fbank_features(UpperCAmelCase_			,    self.mel_filters							)
																									A__						:  List[Any]		  =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									A__						:  Optional[int]		  =  mel.shape[0]
																									if chunk_frames == total_frames:
																															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																															# In this case, we just use the whole audio.
																															A__						:  Dict		  =  np.stack([mel, mel, mel, mel]			,    axis=0							)
																															A__						:  Tuple		  =  False
																									else:
																															A__						:  Union[str, Any]		  =  self._random_mel_fusion(UpperCAmelCase_			,    UpperCAmelCase_			,    UpperCAmelCase_							)
																															A__						:  List[str]		  =  True
																			else:
																									raise NotImplementedError(F'data_truncating {truncation} not implemented'							)

													else:
																			A__						:  Union[str, Any]		  =  False
																			# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																			if waveform.shape[0] < max_length:
																									if padding == "repeat":
																															A__						:  Dict		  =  int(max_length / len(UpperCAmelCase_							)							)
																															A__						:  int		  =  np.stack(np.tile(UpperCAmelCase_			,    n_repeat + 1							)							)[:max_length]
																									if padding == "repeatpad":
																															A__						:  Tuple		  =  int(max_length / len(UpperCAmelCase_							)							)
																															A__						:  Union[str, Any]		  =  np.stack(np.tile(UpperCAmelCase_			,    UpperCAmelCase_							)							)
																									A__						:  Dict		  =  np.pad(UpperCAmelCase_			,    (0, max_length - waveform.shape[0])			,    mode="""constant"""			,    constant_values=0							)

																			if truncation == "fusion":
																									A__						:  str		  =  self._np_extract_fbank_features(UpperCAmelCase_			,    self.mel_filters							)
																									A__						:  Optional[Any]		  =  np.stack([input_mel, input_mel, input_mel, input_mel]			,    axis=0							)
																			else:
																									A__						:  Any		  =  self._np_extract_fbank_features(UpperCAmelCase_			,    self.mel_filters_slaney							)[None, :]

													return input_mel, longer


							def __call__(			self      :						Dict			,    snake_case      :						Any			,    snake_case      :						List[Any] = None			,    snake_case      :						Dict = None			,    snake_case      :						str = None			,    snake_case      :						Optional[int] = None			,    snake_case      :						Tuple = None			,    **snake_case      :						Optional[Any]			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  truncation if truncation is not None else self.truncation
													A__						:  Tuple		  =  padding if padding else self.padding

													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."""							)

													A__						:  List[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}'							)
													A__						:  Dict		  =  is_batched_numpy or (
													    isinstance(UpperCAmelCase_			,    (list, tuple)							) and (isinstance(raw_speech[0]			,    (np.ndarray, tuple, list)							))
													)

													if is_batched:
																			A__						:  int		  =  [np.asarray(UpperCAmelCase_			,    dtype=np.floataa							) for speech in raw_speech]
													elif not is_batched and not isinstance(UpperCAmelCase_			,    np.ndarray							):
																			A__						:  int		  =  np.asarray(UpperCAmelCase_			,    dtype=np.floataa							)
													elif isinstance(UpperCAmelCase_			,    np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																			A__						:  Any		  =  raw_speech.astype(np.floataa							)

													# always return batch
													if not is_batched:
																			A__						:  Optional[int]		  =  [np.asarray(UpperCAmelCase_							)]

													# convert to mel spectrogram, truncate and pad if needed.
													A__						:  Tuple		  =  [
													    self._get_input_mel(UpperCAmelCase_			,    max_length if max_length else self.nb_max_samples			,    UpperCAmelCase_			,    UpperCAmelCase_							)
													    for waveform in raw_speech
													]

													A__						:  Tuple		  =  []
													A__						:  List[str]		  =  []
													for mel, longer in padded_inputs:
																			input_mel.append(UpperCAmelCase_							)
																			is_longer.append(UpperCAmelCase_							)

													if truncation == "fusion" and sum(UpperCAmelCase_							) == 0:
																			# if no audio is longer than 10s, then randomly select one audio to be longer
																			A__						:  str		  =  np.random.randint(0			,    len(UpperCAmelCase_							)							)
																			A__						:  Tuple		  =  True

													if isinstance(input_mel[0]			,    UpperCAmelCase_							):
																			A__						:  int		  =  [np.asarray(UpperCAmelCase_			,    dtype=np.floataa							) for feature in input_mel]

													# is_longer is a list of bool
													A__						:  Dict		  =  [[longer] for longer in is_longer]

													A__						:  List[str]		  =  {"""input_features""": input_mel, """is_longer""": is_longer}
													A__						:  Dict		  =  BatchFeature(UpperCAmelCase_							)

													if return_tensors is not None:
																			A__						:  List[str]		  =  input_features.convert_to_tensors(UpperCAmelCase_							)

													return input_features














"""simple docstring"""



from __future__ import annotations

A_    =							[]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->bool:
						for i in range(len(UpperCAmelCase__      )      ):
												if board[row][i] == 1:
																		return False
						for i in range(len(UpperCAmelCase__      )      ):
												if board[i][column] == 1:
																		return False
						for i, j in zip(range(UpperCAmelCase__,						-1,						-1      ),						range(UpperCAmelCase__,						-1,						-1      )      ):
												if board[i][j] == 1:
																		return False
						for i, j in zip(range(UpperCAmelCase__,						-1,						-1      ),						range(UpperCAmelCase__,						len(UpperCAmelCase__      )      )      ):
												if board[i][j] == 1:
																		return False
						return True




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int      )		->bool:
						if row >= len(UpperCAmelCase__      ):
												solution.append(UpperCAmelCase__      )
												printboard(UpperCAmelCase__      )
												print()
												return True
						for i in range(len(UpperCAmelCase__      )      ):
												if is_safe(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      ):
																		A__						:  Tuple		  =  1
																		solve(UpperCAmelCase__,						row + 1      )
																		A__						:  Optional[int]		  =  0
						return False




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]]      )		->None:
						for i in range(len(UpperCAmelCase__      )      ):
												for j in range(len(UpperCAmelCase__      )      ):
																		if board[i][j] == 1:
																								print("""Q""",						end=""" """      )
																		else:
																								print(""".""",						end=""" """      )
												print()


# n=int(input("The no. of queens"))
A_    =							8
A_    =							[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))






