

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 TensorType, logging


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


class     lowerCAmelCase				(    __UpperCamelCase				):


       '''simple docstring'''







       snake_case    							=	['input_values', 'attention_mask']

       def __init__(	self					:  int						,	__snake_case					:  Optional[int]=1						,	__snake_case					:  Tuple=16000						,	__snake_case					:  int=128						,	__snake_case					:  int=1024						,	__snake_case					:  List[str]=0.0						,	__snake_case					:  Union[str, Any]=True						,	__snake_case					:  List[str]=-4.267_7393						,	__snake_case					:  Optional[int]=4.568_9974						,	__snake_case					:  Optional[int]=False						,	**__snake_case					:  List[str]						,	)   -> str:







        '''simple docstring'''

        super().__init__(feature_size=__snake_case						,	sampling_rate=__snake_case						,	padding_value=__snake_case						,	**__snake_case     )
        lowerCamelCase      						=    num_mel_bins
        lowerCamelCase      						=    max_length
        lowerCamelCase      						=    do_normalize
        lowerCamelCase      						=    mean
        lowerCamelCase      						=    std
        lowerCamelCase      						=    return_attention_mask

       def   lowerCamelCase__						(	self					:  Union[str, Any]						,	__snake_case					:  np.ndarray						,	__snake_case					:  int						,	)   -> np.ndarray:







        '''simple docstring'''

        lowerCamelCase      						=    torch.from_numpy(__snake_case     ).unsqueeze(0     )
        lowerCamelCase      						=    ta_kaldi.fbank(
            __snake_case						,	htk_compat=__snake_case						,	sample_frequency=self.sampling_rate						,	use_energy=__snake_case						,	window_type='hanning'						,	num_mel_bins=self.num_mel_bins						,	dither=0.0						,	frame_shift=10						,	)

        lowerCamelCase      						=    fbank.shape[0]
        lowerCamelCase      						=    max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
         lowerCamelCase      						=    torch.nn.ZeroPadad((0, 0, 0, difference)     )
         lowerCamelCase      						=    pad_module(__snake_case     )
        elif difference < 0:
         lowerCamelCase      						=    fbank[0:max_length, :]

        lowerCamelCase      						=    fbank.numpy()

        return fbank

       def   lowerCamelCase__						(	self					:  Dict						,	__snake_case					:  np.ndarray     )   -> np.ndarray:







        '''simple docstring'''

        return (input_values - (self.mean)) / (self.std * 2)





       def __call__(	self					:  Dict						,	__snake_case					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,	__snake_case					:  Optional[int] = None						,	__snake_case					:  Optional[Union[str, TensorType]] = None						,	**__snake_case					:  List[str]						,	)   -> BatchFeature:







        '''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.'     )

        lowerCamelCase      						=    isinstance(__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}'''     )
        lowerCamelCase      						=    is_batched_numpy or (
            isinstance(__snake_case						,	(list, tuple)     ) and (isinstance(raw_speech[0]						,	(np.ndarray, tuple, list)     ))
        )

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

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

        # extract fbank features and pad/truncate to max_length
        lowerCamelCase      						=    [self._extract_fbank_features(__snake_case						,	max_length=self.max_length     ) for waveform in raw_speech]

        # convert into BatchFeature
        lowerCamelCase      						=    BatchFeature({'input_values': features}     )

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

        # normalization
        if self.do_normalize:
         lowerCamelCase      						=    [self.normalize(__snake_case     ) for feature in input_values]

        if return_tensors is not None:
         lowerCamelCase      						=    padded_inputs.convert_to_tensors(__snake_case     )

        return padded_inputs







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase :	Optional[Any]     				=    {
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    ['XLMProphetNetTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	List[str]     				=    [
         'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMProphetNetDecoder',
         'XLMProphetNetEncoder',
         'XLMProphetNetForCausalLM',
         'XLMProphetNetForConditionalGeneration',
         'XLMProphetNetModel',
         'XLMProphetNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )

else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




