


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__							:						Optional[Any]							     =   logging.get_logger(__name__)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       ['input_features', 'is_longer']

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

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

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

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

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

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

       return input_mel, longer







     def __call__(					self:Union[str, Any]     ,		_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:int     ,		):
       snake_case__						= truncation if truncation is not None else self.truncation
       snake_case__						= 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__						= 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__						= is_batched_numpy or (
           isinstance(_a     ,		(list, tuple)   ) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)   ))
       )

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

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

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

       snake_case__						= []
       snake_case__						= []
       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__						= np.random.randint(0     ,		len(_a   )   )
         snake_case__						= True

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

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

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

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

       return input_features





import os
from datetime import datetime as dt

from github import Github


lowerCamelCase__							:						int							     =   [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]







def SCREAMING_SNAKE_CASE	(			)    ->				Optional[Any]:
  snake_case__						= Github(os.environ['''GITHUB_TOKEN''']				)
  snake_case__						= g.get_repo('''huggingface/diffusers'''				)
  snake_case__						= repo.get_issues(state='''open'''				)

  for issue in open_issues:
    snake_case__						= sorted(issue.get_comments()							,   key=lambda __lowerCAmelCase				: i.created_at							,   reverse=__lowerCAmelCase				)
    snake_case__						= comments[0] if len(__lowerCAmelCase				) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''				)
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''				)
      issue.remove_from_labels('''stale'''				)
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''				)
      issue.add_to_labels('''stale'''				)


if __name__ == "__main__":
   main()

