
import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	["input_values", "attention_mask"]





     def __init__(self   :					Optional[Any]       ,					_A   :					int = 1       ,					_A   :					int = 1_6_0_0_0       ,					_A   :					float = 0.0       ,					_A   :					bool = False       ,					_A   :					int = 8_0       ,					_A   :					int = 1_6       ,					_A   :					int = 6_4       ,					_A   :					str = "hann_window"       ,					_A   :					float = 1.0       ,					_A   :					float = 8_0       ,					_A   :					float = 7_6_0_0       ,					_A   :					float = 1E-10       ,					_A   :					int = 2       ,					_A   :					bool = True       ,					**_A   :					Optional[Any]       ,					)		->		int:
         super().__init__(feature_size=_A       ,					sampling_rate=_A       ,					padding_value=_A       ,					**_A     )
         snake_case				  =      do_normalize
         snake_case				  =      return_attention_mask

         snake_case				  =      num_mel_bins
         snake_case				  =      hop_length
         snake_case				  =      win_length
         snake_case				  =      win_function
         snake_case				  =      frame_signal_scale
         snake_case				  =      fmin
         snake_case				  =      fmax
         snake_case				  =      mel_floor
         snake_case				  =      reduction_factor

         snake_case				  =      win_length * sampling_rate // 1_0_0_0
         snake_case				  =      hop_length * sampling_rate // 1_0_0_0
         snake_case				  =      optimal_fft_length(self.sample_size     )
         snake_case				  =      (self.n_fft // 2) + 1

         snake_case				  =      window_function(window_length=self.sample_size       ,					name=self.win_function       ,					periodic=_A     )

         snake_case				  =      mel_filter_bank(
             num_frequency_bins=self.n_freqs       ,					num_mel_filters=self.num_mel_bins       ,					min_frequency=self.fmin       ,					max_frequency=self.fmax       ,					sampling_rate=self.sampling_rate       ,					norm="slaney"       ,					mel_scale="slaney"       ,					)

         if frame_signal_scale != 1.0:
             warnings.warn(
                 "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,					_A       ,					)
         if reduction_factor != 2.0:
             warnings.warn(
                 "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,					_A       ,					)





     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 	UpperCAmelCase(_A   :					List[np.ndarray]       ,					_A   :					List[np.ndarray]       ,					_A   :					float = 0.0     )		->		List[np.ndarray]:
         if attention_mask is not None:
             snake_case				  =      np.array(_A       ,					np.intaa     )
             snake_case				  =      []

             for vector, length in zip(_A       ,					attention_mask.sum(-1     )     ):
                 snake_case				  =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7     )
                 if length < normed_slice.shape[0]:
                     snake_case				  =      padding_value

                 normed_input_values.append(_A     )
         else:
             snake_case				  =      [(x - x.mean()) / np.sqrt(x.var() + 1E-7     ) for x in input_values]

         return normed_input_values





     def 	UpperCAmelCase(self   :					str       ,					_A   :					np.ndarray       ,					)		->		np.ndarray:
         snake_case				  =      spectrogram(
             _A       ,					window=self.window       ,					frame_length=self.sample_size       ,					hop_length=self.sample_stride       ,					fft_length=self.n_fft       ,					mel_filters=self.mel_filters       ,					mel_floor=self.mel_floor       ,					log_mel="log10"       ,					)
         return log_mel_spec.T





     def __call__(self   :					List[Any]       ,					_A   :					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None       ,					_A   :					Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None       ,					_A   :					Union[bool, str, PaddingStrategy] = False       ,					_A   :					Optional[int] = None       ,					_A   :					bool = False       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					_A   :					Optional[int] = None       ,					**_A   :					int       ,					)		->		BatchFeature:
         if audio is None and audio_target is None:
             raise ValueError("You must provide either `audio` or `audio_target` values."     )

         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 audio 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."     )

         if audio is not None:
             snake_case				  =      self._process_audio(
                 _A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					**_A       ,					)
         else:
             snake_case				  =      None

         if audio_target is not None:
             snake_case				  =      self._process_audio(
                 _A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					_A       ,					**_A       ,					)

             if inputs is None:
                 return inputs_target
             else:
                 snake_case				  =      inputs_target["input_values"]
                 snake_case				  =      inputs_target.get("attention_mask"     )
                 if decoder_attention_mask is not None:
                     snake_case				  =      decoder_attention_mask

         return inputs





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,					_A   :					bool = False       ,					_A   :					Union[bool, str, PaddingStrategy] = False       ,					_A   :					Optional[int] = None       ,					_A   :					bool = False       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[bool] = None       ,					_A   :					Optional[Union[str, TensorType]] = None       ,					**_A   :					Optional[int]       ,					)		->		BatchFeature:
         snake_case				  =      isinstance(_A       ,					np.ndarray     ) and len(speech.shape     ) > 1
         if is_batched_numpy and len(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(speech[0]       ,					(np.ndarray, tuple, list)     ))
         )

         if is_batched:
             snake_case				  =      [np.asarray(_A       ,					dtype=np.floataa     ) for speech in 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 speech.dtype is np.dtype(np.floataa     ):
             snake_case				  =      speech.astype(np.floataa     )

         # always return batch
         if not is_batched:
             snake_case				  =      [speech]

         # needed to make pad() work on spectrogram inputs
         snake_case				  =      self.feature_size

         # convert into correct format for padding
         if is_target:
             snake_case				  =      [self._extract_mel_features(_A     ) for waveform in speech]
             snake_case				  =      BatchFeature({"input_values": features}     )
             snake_case				  =      self.num_mel_bins
         else:
             snake_case				  =      BatchFeature({"input_values": speech}     )

         snake_case				  =      self.pad(
             _A       ,					padding=_A       ,					max_length=_A       ,					truncation=_A       ,					pad_to_multiple_of=_A       ,					return_attention_mask=_A       ,					**_A       ,					)

         snake_case				  =      feature_size_hack

         # convert input values to correct format
         snake_case				  =      padded_inputs["input_values"]
         if not isinstance(input_values[0]       ,					np.ndarray     ):
             snake_case				  =      [np.asarray(_A       ,					dtype=np.floataa     ) for array in input_values]
         elif (
             not isinstance(_A       ,					np.ndarray     )
             and isinstance(input_values[0]       ,					np.ndarray     )
             and input_values[0].dtype is np.dtype(np.floataa     )
         ):
             snake_case				  =      [array.astype(np.floataa     ) for array in input_values]
         elif isinstance(_A       ,					np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
             snake_case				  =      input_values.astype(np.floataa     )

         # convert attention_mask to correct format
         snake_case				  =      padded_inputs.get("attention_mask"     )
         if attention_mask is not None:
             snake_case				  =      [np.asarray(_A       ,					dtype=np.intaa     ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
             snake_case				  =      (
                 attention_mask
                 if self._get_padding_strategies(_A       ,					max_length=_A     ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case				  =      self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]       ,					attention_mask=_A       ,					padding_value=self.padding_value     )

         if return_tensors is not None:
             snake_case				  =      padded_inputs.convert_to_tensors(_A     )

         return padded_inputs





     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict[str, Any]:
         snake_case				  =      super().to_dict()

         # Don't serialize these as they are derived from the other properties.
         snake_case				  =      ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
         for name in names:
             if name in output:
                 del output[name]

         return output



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A						      =				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Wav2Vec2ForAudioFrameClassification",
       "Wav2Vec2ForCTC",
       "Wav2Vec2ForMaskedLM",
       "Wav2Vec2ForPreTraining",
       "Wav2Vec2ForSequenceClassification",
       "Wav2Vec2ForXVector",
       "Wav2Vec2Model",
       "Wav2Vec2PreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFWav2Vec2ForCTC",
       "TFWav2Vec2Model",
       "TFWav2Vec2PreTrainedModel",
       "TFWav2Vec2ForSequenceClassification",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "FlaxWav2Vec2ForCTC",
       "FlaxWav2Vec2ForPreTraining",
       "FlaxWav2Vec2Model",
       "FlaxWav2Vec2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   _A						      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

