



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


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       ["input_features", "is_longer"]



	def __init__(						self							:    Union[str, Any]				,	snake_case_							:    str=64				,	snake_case_							:    Union[str, Any]=4_80_00				,	snake_case_							:    Dict=4_80				,	snake_case_							:    List[str]=10				,	snake_case_							:    Any=10_24				,	snake_case_							:    Tuple=0.0				,	snake_case_							:    List[str]=False				,	snake_case_							:    float = 0				,	snake_case_							:    float = 1_40_00				,	snake_case_							:    int = None				,	snake_case_							:    str = "fusion"				,	snake_case_							:    str = "repeatpad"				,	**snake_case_							:    List[Any]				,	)->							List[Any]:
						super().__init__(
						    feature_size=snake_case_				,	sampling_rate=snake_case_				,	padding_value=snake_case_				,	return_attention_mask=snake_case_				,	**snake_case_				,	)
						__lowerCAmelCase	   =top_db
						__lowerCAmelCase	   =truncation
						__lowerCAmelCase	   =padding
						__lowerCAmelCase	   =fft_window_size
						__lowerCAmelCase	   =(fft_window_size >> 1) + 1
						__lowerCAmelCase	   =hop_length
						__lowerCAmelCase	   =max_length_s
						__lowerCAmelCase	   =max_length_s * sampling_rate
						__lowerCAmelCase	   =sampling_rate
						__lowerCAmelCase	   =frequency_min
						__lowerCAmelCase	   =frequency_max
						__lowerCAmelCase	   =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"""				,	)
						__lowerCAmelCase	   =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])->							Dict[str, Any]:
						__lowerCAmelCase	   =copy.deepcopy(self.__dict__)
						__lowerCAmelCase	   =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							:    str				,	snake_case_							:    np.array				,	snake_case_							:    Optional[np.array] = None)->							np.ndarray:
						__lowerCAmelCase	   =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							:    int				,	snake_case_							:    Tuple				,	snake_case_							:    List[str]				,	snake_case_							:    Any)->							Optional[int]:
						__lowerCAmelCase	   =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
											__lowerCAmelCase	   =[0]
						if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											__lowerCAmelCase	   =[0]
						# randomly choose index for each part
						__lowerCAmelCase	   =np.random.choice(ranges[0])
						__lowerCAmelCase	   =np.random.choice(ranges[1])
						__lowerCAmelCase	   =np.random.choice(ranges[2])

						__lowerCAmelCase	   =mel[idx_front : idx_front + chunk_frames, :]
						__lowerCAmelCase	   =mel[idx_middle : idx_middle + chunk_frames, :]
						__lowerCAmelCase	   =mel[idx_back : idx_back + chunk_frames, :]

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



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

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

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

						return input_mel, longer



	def __call__(						self							:    Union[str, Any]				,	snake_case_							:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,	snake_case_							:    str = None				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[int] = None				,	snake_case_							:    Optional[int] = None				,	snake_case_							:    Optional[Union[str, TensorType]] = None				,	**snake_case_							:    Any				,	)->							BatchFeature:
						__lowerCAmelCase	   =truncation if truncation is not None else self.truncation
						__lowerCAmelCase	   =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.""")

						__lowerCAmelCase	   =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}""")
						__lowerCAmelCase	   =is_batched_numpy or (
						    isinstance(snake_case_				,	(list, tuple)) and (isinstance(raw_speech[0]				,	(np.ndarray, tuple, list)))
						)

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

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

						# convert to mel spectrogram, truncate and pad if needed.
						__lowerCAmelCase	   =[
						    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
						]

						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =[]
						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
											__lowerCAmelCase	   =np.random.randint(0				,	len(snake_case_))
											__lowerCAmelCase	   =True

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

						# is_longer is a list of bool
						__lowerCAmelCase	   =[[longer] for longer in is_longer]

						__lowerCAmelCase	   ={"""input_features""": input_mel, """is_longer""": is_longer}
						__lowerCAmelCase	   =BatchFeature(snake_case_)

						if return_tensors is not None:
											__lowerCAmelCase	   =input_features.convert_to_tensors(snake_case_)

						return input_features









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




