

from math import ceil
from typing import List, Optional, Union

import numpy as np

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


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					__lowerCAmelCase			):
			"""simple docstring"""







			__lowerCamelCase        =       ['''audio_values''', '''audio_mask''']


			def __init__(    self      ,      lowercase=2048      ,      lowercase=1      ,      lowercase=[16, 16]      ,      lowercase=128      ,      lowercase=44100      ,      lowercase=86      ,      lowercase=2048      ,      lowercase=0.0      ,      **lowercase      ,      )  ->						Tuple:
							'''simple docstring'''






							super().__init__(
							    feature_size=lowerCAmelCase_      ,      sampling_rate=lowerCAmelCase_      ,      padding_value=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

							A__        =			spectrogram_length
							A__        =			num_channels
							A__        =			patch_size
							A__        =			feature_size // self.patch_size[1]
							A__        =			n_fft
							A__        =			sampling_rate // hop_length_to_sampling_rate
							A__        =			sampling_rate
							A__        =			padding_value
							A__        =			mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2      ,      num_mel_filters=lowerCAmelCase_      ,      min_frequency=0.0      ,      max_frequency=22050.0      ,      sampling_rate=lowerCAmelCase_      ,      norm="slaney"      ,      mel_scale="slaney"      ,      ).T


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			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.T      ,      log_mel="dB"      ,      db_range=80.0      ,      )
							A__        =			log_spec[:, :-1]
							A__        =			log_spec - 20.0
							A__        =			np.clip(log_spec / 40.0      ,      -2.0      ,      0.0       ) + 1.0
							return log_spec


			def __call__(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = True      ,      lowercase = None      ,      lowercase = False      ,      lowercase = False      ,      **lowercase      ,      )  ->						BatchFeature:
							'''simple docstring'''






							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    "This feature extractor is set to support sampling rate"
															    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
															    F' 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__        =			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}'       )
							A__        =			is_batched_numpy or (
							    isinstance(lowerCAmelCase_      ,      (list, tuple)       ) and (isinstance(raw_speech[0]      ,      (np.ndarray, tuple, list)       ))
							)
							if is_batched:
											A__        =			[np.asarray([speech]      ,      dtype=np.floataa       ).T for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCAmelCase_      ,      np.ndarray       ):
											A__        =			np.asarray(lowerCAmelCase_      ,      dtype=np.floataa       )
							elif isinstance(lowerCAmelCase_      ,      np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
											A__        =			raw_speech.astype(np.floataa       )
							# always return batch
							if not is_batched:
											A__        =			[np.asarray([raw_speech]       ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							A__        =			[
							    self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]      ,      lowerCAmelCase_       ):
											A__        =			[np.asarray(lowerCAmelCase_      ,      dtype=np.floataa       ) for feature in audio_features]

							# Create audio attention mask
							A__        =			max(
							    [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
							if return_attention_mask:
											A__        =			[
											    (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
											    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
											    for feature in audio_features
											]
											A__        =			np.array(lowerCAmelCase_       ).astype(np.floataa       )

							# convert into correct format for padding
							A__        =			max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							A__        =			np.ones([len(lowerCAmelCase_       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
							A__        =			padded_audio_features * self.padding_value
							for i in range(len(lowerCAmelCase_       )       ):
											A__        =			audio_features[i]
											A__        =			feature

							# return as BatchFeature
							if return_attention_mask:
											A__        =			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
							else:
											A__        =			{"audio_values": padded_audio_features}

							A__        =			BatchFeature(data=lowerCAmelCase_      ,      tensor_type=lowerCAmelCase_       )
							return encoded_inputs









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# If row is equal to the size of the board it means there are a queen in each row in
				# the current board (possible_board)
				if row == n:
								# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
								# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
								boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board]      )
								return

				# We iterate each column in the row to find all possible results in each row
				for col in range(SCREAMING_SNAKE_CASE_      ):
								# We apply that we learned previously. First we check that in the current board
								# (possible_board) there are not other same value because if there is it means
								# that there are a collision in vertical. Then we apply the two formulas we
								# learned before:
								#
								# 45º: y - x = b or 45: row - col = b
								# 135º: y + x = b or row + col = b.
								#
								# And we verify if the results of this two formulas not exist in their variables
								# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
								#
								# If any or these are True it means there is a collision so we continue to the
								# next value in the for loop.
								if (
								    col in possible_board
								    or row - col in diagonal_right_collisions
								    or row + col in diagonal_left_collisions
								):
												continue

								# If it is False we call dfs function again and we update the inputs
								depth_first_search(
								    [*possible_board, col]	,       [*diagonal_right_collisions, row - col]	,       [*diagonal_left_collisions, row + col]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

