

'''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


A_							    =				logging.get_logger(__name__)





class 				_snake_case			(							_snake_case    ):
   _A							:  List[str]      =       ['input_features', 'is_longer']
   def __init__(			self      :   Union[str, Any]  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]=64  ,SCREAMING_SNAKE_CASE__      :   Any=48_000  ,SCREAMING_SNAKE_CASE__      :   int=480  ,SCREAMING_SNAKE_CASE__      :   Any=10  ,SCREAMING_SNAKE_CASE__      :   Dict=1_024  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]=0.0  ,SCREAMING_SNAKE_CASE__      :   int=False  ,SCREAMING_SNAKE_CASE__      :   float = 0  ,SCREAMING_SNAKE_CASE__      :   float = 14_000  ,SCREAMING_SNAKE_CASE__      :   int = None  ,SCREAMING_SNAKE_CASE__      :   str = "fusion"  ,SCREAMING_SNAKE_CASE__      :   str = "repeatpad"  ,**SCREAMING_SNAKE_CASE__      :   Dict  ,):
     super().__init__(
         feature_size=SCREAMING_SNAKE_CASE__  ,sampling_rate=SCREAMING_SNAKE_CASE__  ,padding_value=SCREAMING_SNAKE_CASE__  ,return_attention_mask=SCREAMING_SNAKE_CASE__  ,**SCREAMING_SNAKE_CASE__  ,)
     SCREAMING_SNAKE_CASE:Tuple       =       top_db
     SCREAMING_SNAKE_CASE:Dict       =       truncation
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       padding
     SCREAMING_SNAKE_CASE:str       =       fft_window_size
     SCREAMING_SNAKE_CASE:str       =       (fft_window_size >> 1) + 1
     SCREAMING_SNAKE_CASE:Tuple       =       hop_length
     SCREAMING_SNAKE_CASE:Any       =       max_length_s
     SCREAMING_SNAKE_CASE:Optional[Any]       =       max_length_s * sampling_rate
     SCREAMING_SNAKE_CASE:Any       =       sampling_rate
     SCREAMING_SNAKE_CASE:Optional[int]       =       frequency_min
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       frequency_max
     SCREAMING_SNAKE_CASE:Tuple       =       mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins  ,num_mel_filters=SCREAMING_SNAKE_CASE__  ,min_frequency=SCREAMING_SNAKE_CASE__  ,max_frequency=SCREAMING_SNAKE_CASE__  ,sampling_rate=SCREAMING_SNAKE_CASE__  ,norm=SCREAMING_SNAKE_CASE__  ,mel_scale="htk"  ,)
     SCREAMING_SNAKE_CASE:int       =       mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins  ,num_mel_filters=SCREAMING_SNAKE_CASE__  ,min_frequency=SCREAMING_SNAKE_CASE__  ,max_frequency=SCREAMING_SNAKE_CASE__  ,sampling_rate=SCREAMING_SNAKE_CASE__  ,norm="slaney"  ,mel_scale="slaney"  ,)
   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:Optional[Any]       =       copy.deepcopy(self.__dict__				)
     SCREAMING_SNAKE_CASE: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      :   str  ,SCREAMING_SNAKE_CASE__      :   np.array  ,SCREAMING_SNAKE_CASE__      :   Optional[np.array] = None				):
     SCREAMING_SNAKE_CASE:Optional[int]       =       spectrogram(
         SCREAMING_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=SCREAMING_SNAKE_CASE__  ,log_mel="dB"  ,)
     return log_mel_spectrogram.T
   def      __UpperCamelCase    (			self      :   int  ,SCREAMING_SNAKE_CASE__      :   int  ,SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ,SCREAMING_SNAKE_CASE__      :   int				):
     SCREAMING_SNAKE_CASE:Optional[int]       =       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
       SCREAMING_SNAKE_CASE:Dict       =       [0]
     if len(ranges[2]				) == 0:
       # if the audio is too short, we just use the first chunk
       SCREAMING_SNAKE_CASE:List[str]       =       [0]
     # randomly choose index for each part
     SCREAMING_SNAKE_CASE:Optional[Any]       =       np.random.choice(ranges[0]				)
     SCREAMING_SNAKE_CASE:List[Any]       =       np.random.choice(ranges[1]				)
     SCREAMING_SNAKE_CASE:Dict       =       np.random.choice(ranges[2]				)

     SCREAMING_SNAKE_CASE:List[str]       =       mel[idx_front : idx_front + chunk_frames, :]
     SCREAMING_SNAKE_CASE:List[Any]       =       mel[idx_middle : idx_middle + chunk_frames, :]
     SCREAMING_SNAKE_CASE:Any       =       mel[idx_back : idx_back + chunk_frames, :]

     SCREAMING_SNAKE_CASE:int       =       torch.tensor(mel[None, None, :]				)
     SCREAMING_SNAKE_CASE:List[str]       =       torch.nn.functional.interpolate(
         SCREAMING_SNAKE_CASE__  ,size=[chunk_frames, 64]  ,mode="bilinear"  ,align_corners=SCREAMING_SNAKE_CASE__				)
     SCREAMING_SNAKE_CASE:Any       =       mel_shrink[0][0].numpy()
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]  ,axis=0				)
     return mel_fusion
   def      __UpperCamelCase    (			self      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   np.array  ,SCREAMING_SNAKE_CASE__      :   str  ,SCREAMING_SNAKE_CASE__      :   Tuple  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]				):

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

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

       if truncation == "fusion":
         SCREAMING_SNAKE_CASE:Optional[Any]       =       self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__  ,self.mel_filters				)
         SCREAMING_SNAKE_CASE:List[Any]       =       np.stack([input_mel, input_mel, input_mel, input_mel]  ,axis=0				)
       else:
         SCREAMING_SNAKE_CASE:Tuple       =       self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__  ,self.mel_filters_slaney				)[None, :]

     return input_mel, longer







   def __call__(			self      :   Optional[Any]  ,SCREAMING_SNAKE_CASE__      :   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,SCREAMING_SNAKE_CASE__      :   str = None  ,SCREAMING_SNAKE_CASE__      :   Optional[str] = None  ,SCREAMING_SNAKE_CASE__      :   Optional[int] = None  ,SCREAMING_SNAKE_CASE__      :   Optional[int] = None  ,SCREAMING_SNAKE_CASE__      :   Optional[Union[str, TensorType]] = None  ,**SCREAMING_SNAKE_CASE__      :   str  ,):
     SCREAMING_SNAKE_CASE:Tuple       =       truncation if truncation is not None else self.truncation
     SCREAMING_SNAKE_CASE: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."				)

     SCREAMING_SNAKE_CASE:Tuple       =       isinstance(SCREAMING_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}'''				)
     SCREAMING_SNAKE_CASE:Dict       =       is_batched_numpy or (
         isinstance(SCREAMING_SNAKE_CASE__  ,(list, tuple)				) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)				))
     )

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

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       [np.asarray(SCREAMING_SNAKE_CASE__				)]

     # convert to mel spectrogram, truncate and pad if needed.
     SCREAMING_SNAKE_CASE:List[Any]       =       [
         self._get_input_mel(SCREAMING_SNAKE_CASE__  ,max_length if max_length else self.nb_max_samples  ,SCREAMING_SNAKE_CASE__  ,SCREAMING_SNAKE_CASE__				)
         for waveform in raw_speech
     ]

     SCREAMING_SNAKE_CASE:Tuple       =       []
     SCREAMING_SNAKE_CASE:Optional[Any]       =       []
     for mel, longer in padded_inputs:
       input_mel.append(SCREAMING_SNAKE_CASE__				)
       is_longer.append(SCREAMING_SNAKE_CASE__				)

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

     if isinstance(input_mel[0]  ,SCREAMING_SNAKE_CASE__				):
       SCREAMING_SNAKE_CASE:Dict       =       [np.asarray(SCREAMING_SNAKE_CASE__  ,dtype=np.floataa				) for feature in input_mel]

     # is_longer is a list of bool
     SCREAMING_SNAKE_CASE:List[Any]       =       [[longer] for longer in is_longer]

     SCREAMING_SNAKE_CASE:str       =       {"input_features": input_mel, "is_longer": is_longer}
     SCREAMING_SNAKE_CASE:Optional[Any]       =       BatchFeature(SCREAMING_SNAKE_CASE__				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE:str       =       input_features.convert_to_tensors(SCREAMING_SNAKE_CASE__				)

     return input_features













'''simple docstring'''

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(boundary[1] - boundary[0]) / steps
   __SCREAMING_SNAKE_CASE   				=							boundary[0]
   __SCREAMING_SNAKE_CASE   				=							boundary[1]
   __SCREAMING_SNAKE_CASE   				=							make_points(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							0.0
   y += (h / 2.0) * f(__UpperCAmelCase   )
   for i in x_i:
      # print(i)
      y += h * f(__UpperCAmelCase   )
   y += (h / 2.0) * f(__UpperCAmelCase   )
   return y

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							a + h
   while x < (b - h):
      yield x
      __SCREAMING_SNAKE_CASE   				=							x + h

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:  # enter your function here

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(x - 0) * (x - 0)
   return y

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0.0  # Lower bound of integration
   __SCREAMING_SNAKE_CASE   				=							1.0  # Upper bound of integration
   __SCREAMING_SNAKE_CASE   				=							1_0.0  # define number of steps or resolution
   __SCREAMING_SNAKE_CASE   				=							[a, b]  # define boundary of integration
   __SCREAMING_SNAKE_CASE   				=							method_a(__UpperCAmelCase				,						__UpperCAmelCase   )
   print(f"""y = {y}"""   )


if __name__ == "__main__":
    main()





