

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 _UpperCamelCase				(				__snake_case ):
			'''simple docstring'''







			lowerCamelCase__							     =['input_features', 'is_longer']
			def __init__(      self				:			Union[str, Any]   ,   a				:			Any=64   ,   a				:			Optional[int]=4_8000   ,   a				:			Optional[int]=480   ,   a				:			Union[str, Any]=10   ,   a				:			Any=1024   ,   a				:			Tuple=0.0   ,   a				:			List[Any]=False   ,   a				:			List[Any] = 0   ,   a				:			Any = 1_4000   ,   a				:			List[str] = None   ,   a				:			Tuple = "fusion"   ,   a				:			List[Any] = "repeatpad"   ,   **a				:			Any   ,   )				->							Union[str, Any]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							copy.deepcopy(self.__dict__						)
				SCREAMING_SNAKE_CASE :   int					  =							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				:			List[str]   ,   a				:			List[Any]   ,   a				:			List[Any] = None						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							spectrogram(
				    snake_case_   ,   window_function(self.fft_window_size   ,   "hann"						)   ,   frame_length=self.fft_window_size   ,   hop_length=self.hop_length   ,   power=2.0   ,   mel_filters=snake_case_   ,   log_mel="dB"   ,   )
				return log_mel_spectrogram.T
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Dict   ,   a				:			str   ,   a				:			List[str]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   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
					SCREAMING_SNAKE_CASE :   int					  =							[0]
				if len(ranges[2]						) == 0:
					# if the audio is too short, we just use the first chunk
					SCREAMING_SNAKE_CASE :   Dict					  =							[0]
				# randomly choose index for each part
				SCREAMING_SNAKE_CASE :   Dict					  =							np.random.choice(ranges[0]						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							np.random.choice(ranges[1]						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							np.random.choice(ranges[2]						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							mel[idx_front : idx_front + chunk_frames, :]
				SCREAMING_SNAKE_CASE :   List[str]					  =							mel[idx_middle : idx_middle + chunk_frames, :]
				SCREAMING_SNAKE_CASE :   Any					  =							mel[idx_back : idx_back + chunk_frames, :]

				SCREAMING_SNAKE_CASE :   Tuple					  =							torch.tensor(mel[None, None, :]						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.nn.functional.interpolate(
				    snake_case_   ,   size=[chunk_frames, 64]   ,   mode="bilinear"   ,   align_corners=snake_case_						)
				SCREAMING_SNAKE_CASE :   Any					  =							mel_shrink[0][0].numpy()
				SCREAMING_SNAKE_CASE :   Dict					  =							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,   axis=0						)
				return mel_fusion
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Tuple   ,   a				:			List[str]   ,   a				:			Union[str, Any]   ,   a				:			Dict						)				->							Dict:

				"""simple docstring"""

				if waveform.shape[0] > max_length:
					if truncation == "rand_trunc":
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
						# random crop to max_length (for compatibility) -> this should be handled by self.pad
						SCREAMING_SNAKE_CASE :   List[str]					  =							len(snake_case_						) - max_length
						SCREAMING_SNAKE_CASE :   int					  =							np.random.randint(0   ,   overflow + 1						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							waveform[idx : idx + max_length]
						SCREAMING_SNAKE_CASE :   str					  =							self._np_extract_fbank_features(snake_case_   ,   self.mel_filters_slaney						)[None, :]
					elif truncation == "fusion":
						SCREAMING_SNAKE_CASE :   int					  =							self._np_extract_fbank_features(snake_case_   ,   self.mel_filters						)
						SCREAMING_SNAKE_CASE :   int					  =							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
						SCREAMING_SNAKE_CASE :   List[Any]					  =							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.
							SCREAMING_SNAKE_CASE :   Dict					  =							np.stack([mel, mel, mel, mel]   ,   axis=0						)
							SCREAMING_SNAKE_CASE :   Tuple					  =							False
						else:
							SCREAMING_SNAKE_CASE :   List[Any]					  =							self._random_mel_fusion(snake_case_   ,   snake_case_   ,   snake_case_						)
							SCREAMING_SNAKE_CASE :   int					  =							True
					else:
						raise NotImplementedError(F"data_truncating {truncation} not implemented"						)

				else:
					SCREAMING_SNAKE_CASE :   int					  =							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":
							SCREAMING_SNAKE_CASE :   Dict					  =							int(max_length / len(snake_case_						)						)
							SCREAMING_SNAKE_CASE :   Optional[int]					  =							np.stack(np.tile(snake_case_   ,   n_repeat + 1						)						)[:max_length]
						if padding == "repeatpad":
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							int(max_length / len(snake_case_						)						)
							SCREAMING_SNAKE_CASE :   List[Any]					  =							np.stack(np.tile(snake_case_   ,   snake_case_						)						)
						SCREAMING_SNAKE_CASE :   List[str]					  =							np.pad(snake_case_   ,   (0, max_length - waveform.shape[0])   ,   mode="constant"   ,   constant_values=0						)

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

				return input_mel, longer






			def __call__(      self				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			List[Any] = None   ,   a				:			List[Any] = None   ,   a				:			List[Any] = None   ,   a				:			Any = None   ,   a				:			Tuple = None   ,   **a				:			Union[str, Any]   ,   )				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							truncation if truncation is not None else self.truncation
				SCREAMING_SNAKE_CASE :   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."						)

				SCREAMING_SNAKE_CASE :   int					  =							isinstance(snake_case_   ,   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}"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							is_batched_numpy or (
				    isinstance(snake_case_   ,   (list, tuple)						) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)						))
				)

				if is_batched:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[np.asarray(snake_case_   ,   dtype=np.floataa						) for speech in raw_speech]
				elif not is_batched and not isinstance(snake_case_   ,   np.ndarray						):
					SCREAMING_SNAKE_CASE :   List[str]					  =							np.asarray(snake_case_   ,   dtype=np.floataa						)
				elif isinstance(snake_case_   ,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							raw_speech.astype(np.floataa						)

				# always return batch
				if not is_batched:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							[np.asarray(snake_case_						)]

				# convert to mel spectrogram, truncate and pad if needed.
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[
				    self._get_input_mel(snake_case_   ,   max_length if max_length else self.nb_max_samples   ,   snake_case_   ,   snake_case_						)
				    for waveform in raw_speech
				]

				SCREAMING_SNAKE_CASE :   List[str]					  =							[]
				SCREAMING_SNAKE_CASE :   Dict					  =							[]
				for mel, longer in padded_inputs:
					input_mel.append(snake_case_						)
					is_longer.append(snake_case_						)

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

				if isinstance(input_mel[0]   ,   snake_case_						):
					SCREAMING_SNAKE_CASE :   Tuple					  =							[np.asarray(snake_case_   ,   dtype=np.floataa						) for feature in input_mel]

				# is_longer is a list of bool
				SCREAMING_SNAKE_CASE :   Tuple					  =							[[longer] for longer in is_longer]

				SCREAMING_SNAKE_CASE :   Tuple					  =							{"input_features": input_mel, "is_longer": is_longer}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							BatchFeature(snake_case_						)

				if return_tensors is not None:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							input_features.convert_to_tensors(snake_case_						)

				return input_features






from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
 print(f"{solution() = }")





