
'''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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[Any]				=							["input_features", "is_longer"]







 def __init__(					self		:  Any			,				snake_case__		:  int=64			,				snake_case__		:  Optional[Any]=4_80_00			,				snake_case__		:  Dict=4_80			,				snake_case__		:  str=10			,				snake_case__		:  Tuple=10_24			,				snake_case__		:  Tuple=0.0			,				snake_case__		:  Optional[Any]=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]			,				):



   '''simple docstring'''




   super().__init__(
       feature_size=snake_case__			,				sampling_rate=snake_case__			,				padding_value=snake_case__			,				return_attention_mask=snake_case__			,				**snake_case__			,				)
   UpperCAmelCase__      :					Any				       =							top_db
   UpperCAmelCase__      :					Optional[int]				       =							truncation
   UpperCAmelCase__      :					int				       =							padding
   UpperCAmelCase__      :					Optional[Any]				       =							fft_window_size
   UpperCAmelCase__      :					str				       =							(fft_window_size >> 1) + 1
   UpperCAmelCase__      :					List[Any]				       =							hop_length
   UpperCAmelCase__      :					str				       =							max_length_s
   UpperCAmelCase__      :					Any				       =							max_length_s * sampling_rate
   UpperCAmelCase__      :					Dict				       =							sampling_rate
   UpperCAmelCase__      :					Optional[int]				       =							frequency_min
   UpperCAmelCase__      :					Union[str, Any]				       =							frequency_max
   UpperCAmelCase__      :					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=snake_case__			,				mel_scale="htk"			,				)
   UpperCAmelCase__      :					Optional[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		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							copy.deepcopy(self.__dict__ )
   UpperCAmelCase__      :					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[Any]			,				snake_case__		:  np.array			,				snake_case__		:  Optional[np.array] = None ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							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		:  Optional[Any]			,				snake_case__		:  Dict			,				snake_case__		:  int			,				snake_case__		:  str ):



   '''simple docstring'''




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

   UpperCAmelCase__      :					Tuple				       =							mel[idx_front : idx_front + chunk_frames, :]
   UpperCAmelCase__      :					Union[str, Any]				       =							mel[idx_middle : idx_middle + chunk_frames, :]
   UpperCAmelCase__      :					Optional[int]				       =							mel[idx_back : idx_back + chunk_frames, :]

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







 def 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  np.array			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  Dict			,				snake_case__		:  List[str] ):



   '''simple docstring'''




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

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

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

   return input_mel, longer







 def __call__(					self		:  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			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							truncation if truncation is not None else self.truncation
   UpperCAmelCase__      :					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." )

   UpperCAmelCase__      :					Dict				       =							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}""" )
   UpperCAmelCase__      :					Union[str, Any]				       =							is_batched_numpy or (
       isinstance(snake_case__			,				(list, tuple) ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list) ))
   )

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

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

   # convert to mel spectrogram, truncate and pad if needed.
   UpperCAmelCase__      :					str				       =							[
       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
   ]

   UpperCAmelCase__      :					Optional[int]				       =							[]
   UpperCAmelCase__      :					List[Any]				       =							[]
   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
     UpperCAmelCase__      :					Dict				       =							np.random.randint(0			,				len(snake_case__ ) )
     UpperCAmelCase__      :					List[str]				       =							True

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

   # is_longer is a list of bool
   UpperCAmelCase__      :					int				       =							[[longer] for longer in is_longer]

   UpperCAmelCase__      :					Optional[int]				       =							{"input_features": input_mel, "is_longer": is_longer}
   UpperCAmelCase__      :					List[str]				       =							BatchFeature(snake_case__ )

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

   return input_features





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



