import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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     :  Union[str, Any]					     =       logging.get_logger(__name__)


class   lowerCamelCase					(_UpperCAmelCase					):
  """simple docstring"""







  lowerCamelCase__     =						['''input_features''']







  def __init__(							self :       Dict  ,     __magic_name__ :       str=80  ,     __magic_name__ :       List[Any]=16_000  ,     __magic_name__ :       Optional[int]=160  ,     __magic_name__ :       List[Any]=30  ,     __magic_name__ :       Dict=400  ,     __magic_name__ :       Optional[Any]=0.0  ,     __magic_name__ :       Optional[Any]=False  ,     **__magic_name__ :       Tuple  ,     )  ->	int:
        super().__init__(
            feature_size=lowercase__  ,     sampling_rate=lowercase__  ,     padding_value=lowercase__  ,     return_attention_mask=lowercase__  ,     **lowercase__  ,     )
        SCREAMING_SNAKE_CASE_          =  n_fft
        SCREAMING_SNAKE_CASE_          =  hop_length
        SCREAMING_SNAKE_CASE_          =  chunk_length
        SCREAMING_SNAKE_CASE_          =  chunk_length * sampling_rate
        SCREAMING_SNAKE_CASE_          =  self.n_samples // hop_length
        SCREAMING_SNAKE_CASE_          =  sampling_rate
        SCREAMING_SNAKE_CASE_          =  mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2  ,     num_mel_filters=lowercase__  ,     min_frequency=0.0  ,     max_frequency=8000.0  ,     sampling_rate=lowercase__  ,     norm="slaney"  ,     mel_scale="slaney"  ,     )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  spectrogram(
            lowercase__  ,     window_function(self.n_fft  ,     "hann"   )  ,     frame_length=self.n_fft  ,     hop_length=self.hop_length  ,     power=2.0  ,     mel_filters=self.mel_filters  ,     log_mel="log10"  ,     )
        SCREAMING_SNAKE_CASE_          =  log_spec[:, :-1]
        SCREAMING_SNAKE_CASE_          =  np.maximum(lowercase__  ,     log_spec.max() - 8.0   )
        SCREAMING_SNAKE_CASE_          =  (log_spec + 4.0) / 4.0
        return log_spec







  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def    __A     (							__magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int] = 0.0   )  ->	List[Any]:






        if attention_mask is not None:
              SCREAMING_SNAKE_CASE_          =  np.array(lowercase__  ,     np.intaa   )
              SCREAMING_SNAKE_CASE_          =  []

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

                    normed_input_values.append(lowercase__   )
        else:
              SCREAMING_SNAKE_CASE_          =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

        return normed_input_values







  def __call__(							self :       List[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int] = True  ,     __magic_name__ :       Any = None  ,     __magic_name__ :       List[str] = None  ,     __magic_name__ :       Optional[Any] = None  ,     __magic_name__ :       Any = "max_length"  ,     __magic_name__ :       List[str] = None  ,     __magic_name__ :       Optional[Any] = None  ,     __magic_name__ :       Optional[int] = None  ,     **__magic_name__ :       Tuple  ,     )  ->	Tuple:






        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_          =  isinstance(lowercase__  ,     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(lowercase__  ,     (list, tuple)   ) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)   ))
        )

        if is_batched:
              SCREAMING_SNAKE_CASE_          =  [np.asarray([speech]  ,     dtype=np.floataa   ).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase__  ,     np.ndarray   ):
              SCREAMING_SNAKE_CASE_          =  np.asarray(lowercase__  ,     dtype=np.floataa   )
        elif isinstance(lowercase__  ,     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_          =  [np.asarray([raw_speech]   ).T]

        SCREAMING_SNAKE_CASE_          =  BatchFeature({"input_features": raw_speech}   )

        # convert into correct format for padding

        SCREAMING_SNAKE_CASE_          =  self.pad(
            lowercase__  ,     padding=lowercase__  ,     max_length=max_length if max_length else self.n_samples  ,     truncation=lowercase__  ,     pad_to_multiple_of=lowercase__  ,     return_attention_mask=return_attention_mask or do_normalize  ,     )

        # zero-mean and unit-variance normalization
        if do_normalize:
              SCREAMING_SNAKE_CASE_          =  self.zero_mean_unit_var_norm(
                  padded_inputs["input_features"]  ,     attention_mask=padded_inputs["attention_mask"]  ,     padding_value=self.padding_value  ,     )
              SCREAMING_SNAKE_CASE_          =  np.stack(padded_inputs["input_features"]  ,     axis=0   )

        # make sure list is in array format
        SCREAMING_SNAKE_CASE_          =  padded_inputs.get("input_features"   ).transpose(2  ,     0  ,     1   )

        SCREAMING_SNAKE_CASE_          =  [self._np_extract_fbank_features(lowercase__   ) for waveform in input_features[0]]

        if isinstance(input_features[0]  ,     lowercase__   ):
              SCREAMING_SNAKE_CASE_          =  [np.asarray(lowercase__  ,     dtype=np.floataa   ) for feature in input_features]
        else:
              SCREAMING_SNAKE_CASE_          =  input_features

        if return_attention_mask:
              # rescale from sample (48000) to feature (3000)
              SCREAMING_SNAKE_CASE_          =  padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE_          =  padded_inputs.convert_to_tensors(lowercase__   )

        return padded_inputs







  def    __A     (							self :       List[str]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        return output



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


