

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__A			:					str						=			logging.get_logger(__name__)





class 			_SCREAMING_SNAKE_CASE (   _lowerCamelCase		):







   '''simple docstring'''




   lowerCamelCase__				=      ['input_features', 'attention_mask']





   def __init__(       self					:			Tuple     , __lowerCamelCase					:			List[Any]=80     , __lowerCamelCase					:			int=16000     , __lowerCamelCase					:			Tuple=0.0     , __lowerCamelCase					:			Optional[Any]=10     , __lowerCamelCase					:			int=25     , __lowerCamelCase					:			str="hamming_window"     , __lowerCamelCase					:			List[str]=32768.0     , __lowerCamelCase					:			Optional[int]=0.97     , __lowerCamelCase					:			List[Any]=1.0     , __lowerCamelCase					:			int=True     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Optional[Any]=False     , **__lowerCamelCase					:			Union[str, Any]     , ):
       super().__init__(feature_size=__UpperCamelCase     , sampling_rate=__UpperCamelCase     , padding_value=__UpperCamelCase     , **__UpperCamelCase			)

       SCREAMING_SNAKE_CASE										=   feature_size
       SCREAMING_SNAKE_CASE										=   sampling_rate
       SCREAMING_SNAKE_CASE										=   padding_value
       SCREAMING_SNAKE_CASE										=   hop_length
       SCREAMING_SNAKE_CASE										=   win_length
       SCREAMING_SNAKE_CASE										=   frame_signal_scale
       SCREAMING_SNAKE_CASE										=   preemphasis_coeff
       SCREAMING_SNAKE_CASE										=   mel_floor
       SCREAMING_SNAKE_CASE										=   normalize_means
       SCREAMING_SNAKE_CASE										=   normalize_vars
       SCREAMING_SNAKE_CASE										=   win_function
       SCREAMING_SNAKE_CASE										=   return_attention_mask

       SCREAMING_SNAKE_CASE										=   win_length * sampling_rate // 1000
       SCREAMING_SNAKE_CASE										=   hop_length * sampling_rate // 1000

       SCREAMING_SNAKE_CASE										=   optimal_fft_length(self.sample_size			)
       SCREAMING_SNAKE_CASE										=   (self.n_fft // 2) + 1





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			np.array			):
       if self.win_function == "hamming_window":
           SCREAMING_SNAKE_CASE										=   window_function(window_length=self.sample_size     , name=self.win_function     , periodic=__UpperCamelCase			)
       else:
           SCREAMING_SNAKE_CASE										=   window_function(window_length=self.sample_size     , name=self.win_function			)

       SCREAMING_SNAKE_CASE										=   mel_filter_bank(
           num_frequency_bins=self.n_freqs     , num_mel_filters=self.feature_size     , min_frequency=0.0     , max_frequency=self.sampling_rate / 2.0     , sampling_rate=self.sampling_rate     , )

       SCREAMING_SNAKE_CASE										=   spectrogram(
           one_waveform * self.frame_signal_scale     , window=__UpperCamelCase     , frame_length=self.sample_size     , hop_length=self.sample_stride     , fft_length=self.n_fft     , center=__UpperCamelCase     , preemphasis=self.preemphasis_coeff     , mel_filters=__UpperCamelCase     , mel_floor=self.mel_floor     , log_mel="log"     , )
       return msfc_features.T





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple			):
       # make sure we normalize float32 arrays
       if self.normalize_means:
           SCREAMING_SNAKE_CASE										=   x[:input_length].mean(axis=0			)
           SCREAMING_SNAKE_CASE										=   np.subtract(__UpperCamelCase     , __UpperCamelCase			)
       if self.normalize_vars:
           SCREAMING_SNAKE_CASE										=   x[:input_length].std(axis=0			)
           SCREAMING_SNAKE_CASE										=   np.divide(__UpperCamelCase     , __UpperCamelCase			)

       if input_length < x.shape[0]:
           SCREAMING_SNAKE_CASE										=   padding_value

       # make sure array is in float32
       SCREAMING_SNAKE_CASE										=   x.astype(np.floataa			)

       return x





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[np.ndarray]     , __lowerCamelCase					:			Optional[np.ndarray] = None			):
       SCREAMING_SNAKE_CASE										=   attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(__UpperCamelCase     , __UpperCamelCase     , self.padding_value			) for x, n in zip(__UpperCamelCase     , __UpperCamelCase			)]





   def __call__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     , __lowerCamelCase					:			Union[bool, str, PaddingStrategy] = False     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[bool] = None     , __lowerCamelCase					:			Optional[Union[str, TensorType]] = None     , __lowerCamelCase					:			Optional[int] = None     , **__lowerCamelCase					:			List[str]     , ):

       if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
               raise ValueError(
                   f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                   f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                   f" {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										=   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}"			)
       SCREAMING_SNAKE_CASE										=   is_batched_numpy or (
           isinstance(__UpperCamelCase     , (list, tuple)			) and (isinstance(raw_speech[0]     , (np.ndarray, tuple, list)			))
       )

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

       # always return batch
       if not is_batched:
           SCREAMING_SNAKE_CASE										=   [raw_speech]

       # extract fbank features
       SCREAMING_SNAKE_CASE										=   [self._extract_mfsc_features(__UpperCamelCase			) for one_waveform in raw_speech]

       # convert into correct format for padding
       SCREAMING_SNAKE_CASE										=   BatchFeature({"input_features": features}			)

       SCREAMING_SNAKE_CASE										=   self.pad(
           __UpperCamelCase     , padding=__UpperCamelCase     , max_length=__UpperCamelCase     , truncation=__UpperCamelCase     , pad_to_multiple_of=__UpperCamelCase     , return_attention_mask=__UpperCamelCase     , **__UpperCamelCase     , )
       # make sure list is in array format
       SCREAMING_SNAKE_CASE										=   padded_inputs.get("input_features"			)
       if isinstance(input_features[0]     , __UpperCamelCase			):
           SCREAMING_SNAKE_CASE										=   [np.asarray(__UpperCamelCase     , dtype=np.floataa			) for feature in input_features]

       SCREAMING_SNAKE_CASE										=   padded_inputs.get("attention_mask"			)
       if attention_mask is not None:
           SCREAMING_SNAKE_CASE										=   [np.asarray(__UpperCamelCase     , dtype=np.intaa			) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
           SCREAMING_SNAKE_CASE										=   (
               np.array(__UpperCamelCase     , dtype=np.intaa			)
               if self._get_padding_strategies(__UpperCamelCase     , max_length=__UpperCamelCase			) is not PaddingStrategy.DO_NOT_PAD
               and padding
               else None
           )
           SCREAMING_SNAKE_CASE										=   self.normalize(
               padded_inputs["input_features"]     , attention_mask=__UpperCamelCase			)

       if return_tensors is not None:
           SCREAMING_SNAKE_CASE										=   padded_inputs.convert_to_tensors(__UpperCamelCase			)

       return padded_inputs




from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  Callable						, __UpperCamelCase      :  Optional[Features] = None						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[dict] = None						, __UpperCamelCase      :  Optional[int] = None						, **__UpperCamelCase      :  str						, )		->     List[str]:
											super().__init__(
											    features=__UpperCamelCase						, cache_dir=__UpperCamelCase						, keep_in_memory=__UpperCamelCase						, streaming=__UpperCamelCase						, num_proc=__UpperCamelCase						, **__UpperCamelCase						, )
											A      						=    Generator(
											    cache_dir=__UpperCamelCase						, features=__UpperCamelCase						, generator=__UpperCamelCase						, gen_kwargs=__UpperCamelCase						, **__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											# Build iterable dataset
											if self.streaming:
																		A      						=    self.builder.as_streaming_dataset(split='train'       )
											# Build regular (map-style) dataset
											else:
																		A      						=    None
																		A      						=    None
																		A      						=    None
																		A      						=    None

																		self.builder.download_and_prepare(
																		    download_config=__UpperCamelCase						, download_mode=__UpperCamelCase						, verification_mode=__UpperCamelCase						, base_path=__UpperCamelCase						, num_proc=self.num_proc						, )
																		A      						=    self.builder.as_dataset(
																		    split='train'						, verification_mode=__UpperCamelCase						, in_memory=self.keep_in_memory       )
											return dataset