





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       ["input_features", "is_longer"]






						def __init__(      self      :      Optional[Any]		,   _A      :      Dict=64		,   _A      :      Any=48000		,   _A      :      Any=480		,   _A      :      Optional[Any]=10		,   _A      :      Optional[int]=1024		,   _A      :      int=0.0		,   _A      :      Dict=False		,   _A      :      float = 0		,   _A      :      float = 14000		,   _A      :      int = None		,   _A      :      str = "fusion"		,   _A      :      str = "repeatpad"		,   **_A      :      Union[str, Any]		,   )							->    str:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Dict[str, Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	copy.deepcopy(self.__dict__	)
							snake_case_		:			Any											=	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      :      np.array		,   _A      :      Optional[np.array] = None	)							->    np.ndarray:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	spectrogram(
							    _A		,   window_function(self.fft_window_size		,   'hann'	)		,   frame_length=self.fft_window_size		,   hop_length=self.hop_length		,   power=2.0		,   mel_filters=_A		,   log_mel='dB'		,   )
							return log_mel_spectrogram.T






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[int]		,   _A      :      Optional[int]		,   _A      :      Tuple	)							->    int:







							"""simple docstring"""


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

							snake_case_		:			Dict											=	mel[idx_front : idx_front + chunk_frames, :]
							snake_case_		:			int											=	mel[idx_middle : idx_middle + chunk_frames, :]
							snake_case_		:			Optional[Any]											=	mel[idx_back : idx_back + chunk_frames, :]

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






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      np.array		,   _A      :      Union[str, Any]		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    np.array:







							"""simple docstring"""


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

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

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

							return input_mel, longer






						def __call__(      self      :      str		,   _A      :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,   _A      :      str = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[int] = None		,   _A      :      Optional[int] = None		,   _A      :      Optional[Union[str, TensorType]] = None		,   **_A      :      Dict		,   )							->    BatchFeature:







							"""simple docstring"""


							snake_case_		:			str											=	truncation if truncation is not None else self.truncation
							snake_case_		:			Optional[int]											=	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.'	)

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

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

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

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

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

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

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

							# is_longer is a list of bool
							snake_case_		:			Union[str, Any]											=	[[longer] for longer in is_longer]

							snake_case_		:			Any											=	{'input_features': input_mel, 'is_longer': is_longer}
							snake_case_		:			Optional[int]											=	BatchFeature(_A	)

							if return_tensors is not None:
								snake_case_		:			Union[str, Any]											=	input_features.convert_to_tensors(_A	)

							return input_features








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		"""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))

						print("""Googling.....""")

						_SCREAMING_SNAKE_CASE      	=		F'''https://www.google.com/search?q={query}&num=100'''

						_SCREAMING_SNAKE_CASE      	=		requests.get(
						    url,
						    headers={"""User-Agent""": str(UserAgent().random)},
						)

						try:
												_SCREAMING_SNAKE_CASE      	=		(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """yuRUbf"""})
												    .find("""a""")
												    .get("""href""")
												)

						except AttributeError:
												_SCREAMING_SNAKE_CASE      	=		parse_qs(
												    BeautifulSoup(res.text, """html.parser""")
												    .find("""div""", attrs={"""class""": """kCrYT"""})
												    .find("""a""")
												    .get("""href""")
												)["""url"""][0]

						webbrowser.open(link)

