



'''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


__lowerCAmelCase						      =       logging.get_logger(__name__)

class      lowerCAmelCase__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Tuple           = ['input_features', 'is_longer']







							def __init__(							self :	Optional[Any]     ,_UpperCAmelCase :	Optional[int]=64     ,_UpperCAmelCase :	Dict=48000     ,_UpperCAmelCase :	int=480     ,_UpperCAmelCase :	Any=10     ,_UpperCAmelCase :	List[str]=1024     ,_UpperCAmelCase :	List[Any]=0.0     ,_UpperCAmelCase :	Optional[int]=False     ,_UpperCAmelCase :	float = 0     ,_UpperCAmelCase :	float = 14000     ,_UpperCAmelCase :	int = None     ,_UpperCAmelCase :	str = "fusion"     ,_UpperCAmelCase :	str = "repeatpad"     ,**_UpperCAmelCase :	Optional[Any]     ,):
									super().__init__(
									    feature_size=_UpperCAmelCase     ,sampling_rate=_UpperCAmelCase     ,padding_value=_UpperCAmelCase     ,return_attention_mask=_UpperCAmelCase     ,**_UpperCAmelCase     ,)
									_a					:				Optional[int]				=							top_db
									_a					:				List[Any]				=							truncation
									_a					:				List[Any]				=							padding
									_a					:				Union[str, Any]				=							fft_window_size
									_a					:				Tuple				=							(fft_window_size >> 1) + 1
									_a					:				Tuple				=							hop_length
									_a					:				Optional[Any]				=							max_length_s
									_a					:				List[str]				=							max_length_s * sampling_rate
									_a					:				str				=							sampling_rate
									_a					:				Optional[int]				=							frequency_min
									_a					:				List[Any]				=							frequency_max
									_a					:				Any				=							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					:				List[Any]				=							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 							__lowercase      (							self :	Optional[int]		):
									_a					:				Tuple				=							copy.deepcopy(self.__dict__		)
									_a					:				Dict				=							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 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	np.array     ,_UpperCAmelCase :	Optional[np.array] = None		):
									_a					:				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 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Dict		):
									_a					:				str				=							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					:				str				=							[0]
									if len(ranges[2]		) == 0:
											# if the audio is too short, we just use the first chunk
											_a					:				Optional[int]				=							[0]
									# randomly choose index for each part
									_a					:				Dict				=							np.random.choice(ranges[0]		)
									_a					:				int				=							np.random.choice(ranges[1]		)
									_a					:				Optional[Any]				=							np.random.choice(ranges[2]		)

									_a					:				Dict				=							mel[idx_front : idx_front + chunk_frames, :]
									_a					:				Dict				=							mel[idx_middle : idx_middle + chunk_frames, :]
									_a					:				Dict				=							mel[idx_back : idx_back + chunk_frames, :]

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







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

									else:
											_a					:				List[str]				=							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					:				str				=							int(max_length / len(_UpperCAmelCase		)		)
															_a					:				str				=							np.stack(np.tile(_UpperCAmelCase     ,n_repeat + 1		)		)[:max_length]
													if padding == "repeatpad":
															_a					:				int				=							int(max_length / len(_UpperCAmelCase		)		)
															_a					:				Optional[Any]				=							np.stack(np.tile(_UpperCAmelCase     ,_UpperCAmelCase		)		)
													_a					:				Union[str, Any]				=							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					:				Union[str, Any]				=							np.stack([input_mel, input_mel, input_mel, input_mel]     ,axis=0		)
											else:
													_a					:				Union[str, Any]				=							self._np_extract_fbank_features(_UpperCAmelCase     ,self.mel_filters_slaney		)[None, :]

									return input_mel, longer







							def __call__(							self :	str     ,_UpperCAmelCase :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,_UpperCAmelCase :	str = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[int] = None     ,_UpperCAmelCase :	Optional[int] = None     ,_UpperCAmelCase :	Optional[Union[str, TensorType]] = None     ,**_UpperCAmelCase :	Tuple     ,):
									_a					:				Dict				=							truncation if truncation is not None else self.truncation
									_a					:				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.'		)

									_a					:				Tuple				=							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					:				Any				=							is_batched_numpy or (
									    isinstance(_UpperCAmelCase     ,(list, tuple)		) and (isinstance(raw_speech[0]     ,(np.ndarray, tuple, list)		))
									)

									if is_batched:
											_a					:				Dict				=							[np.asarray(_UpperCAmelCase     ,dtype=np.floataa		) for speech in raw_speech]
									elif not is_batched and not isinstance(_UpperCAmelCase     ,np.ndarray		):
											_a					:				List[Any]				=							np.asarray(_UpperCAmelCase     ,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase     ,np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
											_a					:				Tuple				=							raw_speech.astype(np.floataa		)

									# always return batch
									if not is_batched:
											_a					:				int				=							[np.asarray(_UpperCAmelCase		)]

									# convert to mel spectrogram, truncate and pad if needed.
									_a					:				Optional[int]				=							[
									    self._get_input_mel(_UpperCAmelCase     ,max_length if max_length else self.nb_max_samples     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									    for waveform in raw_speech
									]

									_a					:				Dict				=							[]
									_a					:				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					:				Optional[int]				=							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					:				Union[str, Any]				=							[[longer] for longer in is_longer]

									_a					:				str				=							{'input_features': input_mel, 'is_longer': is_longer}
									_a					:				str				=							BatchFeature(_UpperCAmelCase		)

									if return_tensors is not None:
											_a					:				str				=							input_features.convert_to_tensors(_UpperCAmelCase		)

									return input_features









'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

__lowerCAmelCase						      =       '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase						      =       BASE_URL + '''/user'''

# https://github.com/settings/tokens
__lowerCAmelCase						      =       os.environ.get('''USER_TOKEN''', '''''')



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				dict[Any, Any]:
		_a					:				Union[str, Any]				=							{
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(lowerCAmelCase_						,      headers=lowerCAmelCase_  ).json()


if __name__ == "__main__":  # pragma: no cover
							if USER_TOKEN:
														for key, value in fetch_github_info(USER_TOKEN).items():
																					print(f"""{key}: {value}""")
							else:
														raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')




