"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase__					):



    """simple docstring"""
    _lowerCAmelCase     :    Dict			  =						["input_features", "attention_mask"]

    def __init__(				self		,    lowerCAmelCase=80		,    lowerCAmelCase=1_60_00		,    lowerCAmelCase=80		,    lowerCAmelCase=0.0		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    **lowerCAmelCase		,    ):






          """simple docstring"""

          super().__init__(feature_size=_SCREAMING_SNAKE_CASE		,    sampling_rate=_SCREAMING_SNAKE_CASE		,    padding_value=_SCREAMING_SNAKE_CASE		,    **_SCREAMING_SNAKE_CASE    )
          snake_case								=			num_mel_bins
          snake_case								=			do_ceptral_normalize
          snake_case								=			normalize_means
          snake_case								=			normalize_vars
          snake_case								=			True

    def 		snake_case (				self		,    lowerCAmelCase		,    ):






          """simple docstring"""

          snake_case								=			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
          snake_case								=			torch.from_numpy(_SCREAMING_SNAKE_CASE    ).unsqueeze(0    )
          snake_case								=			ta_kaldi.fbank(_SCREAMING_SNAKE_CASE		,    num_mel_bins=self.num_mel_bins		,    sample_frequency=self.sampling_rate    )
          return features.numpy()

    @staticmethod
    def 		snake_case (				lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = True		,    lowerCAmelCase = True		,    lowerCAmelCase = 0.0		,    ):






          """simple docstring"""

          if normalize_means:
                snake_case								=			x[:input_length].mean(axis=0    )
                snake_case								=			np.subtract(_SCREAMING_SNAKE_CASE		,    _SCREAMING_SNAKE_CASE    )
          if normalize_vars:
                snake_case								=			x[:input_length].std(axis=0    )
                snake_case								=			np.divide(_SCREAMING_SNAKE_CASE		,    _SCREAMING_SNAKE_CASE    )

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

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

          return x

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






          """simple docstring"""

          snake_case								=			attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [
              self.utterance_cmvn(_SCREAMING_SNAKE_CASE		,    _SCREAMING_SNAKE_CASE		,    self.normalize_means		,    self.normalize_vars		,    self.padding_value    )
              for x, n in zip(_SCREAMING_SNAKE_CASE		,    _SCREAMING_SNAKE_CASE    )
          ]

    def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






          """simple docstring"""

          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.'    )

          snake_case								=			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}"""    )
          snake_case								=			is_batched_numpy or (
              isinstance(_SCREAMING_SNAKE_CASE		,    (list, tuple)    ) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)    ))
          )

          if is_batched:
                snake_case								=			[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    ):
                snake_case								=			np.asarray(_SCREAMING_SNAKE_CASE		,    dtype=np.floataa    )
          elif isinstance(_SCREAMING_SNAKE_CASE		,    np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
                snake_case								=			raw_speech.astype(np.floataa    )

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

          # extract fbank features
          snake_case								=			[self._extract_fbank_features(_SCREAMING_SNAKE_CASE    ) for waveform in raw_speech]

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

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

          # make sure list is in array format
          snake_case								=			padded_inputs.get('input_features'    )
          if isinstance(input_features[0]		,    _SCREAMING_SNAKE_CASE    ):
                snake_case								=			[np.asarray(_SCREAMING_SNAKE_CASE		,    dtype=np.floataa    ) for feature in input_features]

          snake_case								=			padded_inputs.get('attention_mask'    )
          if attention_mask is not None:
                snake_case								=			[np.asarray(_SCREAMING_SNAKE_CASE		,    dtype=np.intaa    ) for array in attention_mask]

          # Utterance-level cepstral mean and variance normalization
          if self.do_ceptral_normalize:
                snake_case								=			(
                    np.array(_SCREAMING_SNAKE_CASE		,    dtype=np.intaa    )
                    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE		,    max_length=_SCREAMING_SNAKE_CASE    ) is not PaddingStrategy.DO_NOT_PAD
                    else None
                )
                snake_case								=			self.normalize(
                    padded_inputs['input_features']		,    attention_mask=_SCREAMING_SNAKE_CASE    )

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

          return padded_inputs



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      						=  {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SEWForCTC",
					    "SEWForSequenceClassification",
					    "SEWModel",
					    "SEWPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew import (
										    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWForCTC,
										    SEWForSequenceClassification,
										    SEWModel,
										    SEWPreTrainedModel,
										)


else:
					import sys

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


