






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


UpperCAmelCase__							  =						logging.get_logger(__name__)






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    ['''input_features''']







      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		List[str]=80					,   __UpperCAmelCase :		Dict=16_000					,   __UpperCAmelCase :		List[str]=160					,   __UpperCAmelCase :		int=30					,   __UpperCAmelCase :		int=400					,   __UpperCAmelCase :		List[str]=0.0					,   __UpperCAmelCase :		Optional[Any]=False					,   **__UpperCAmelCase :		List[str]					,   )       ->Union[str, Any]:




       """simple docstring"""


       super().__init__(
           feature_size=__UpperCAmelCase					,   sampling_rate=__UpperCAmelCase					,   padding_value=__UpperCAmelCase					,   return_attention_mask=__UpperCAmelCase					,   **__UpperCAmelCase					,   )
       a						    = n_fft
       a						    = hop_length
       a						    = chunk_length
       a						    = chunk_length * sampling_rate
       a						    = self.n_samples // hop_length
       a						    = sampling_rate
       a						    = mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2					,   num_mel_filters=__UpperCAmelCase					,   min_frequency=0.0					,   max_frequency=8000.0					,   sampling_rate=__UpperCAmelCase					,   norm='''slaney'''					,   mel_scale='''slaney'''					,   )







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		np.array    )       ->np.ndarray:




       """simple docstring"""


       a						    = spectrogram(
           __UpperCAmelCase					,   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'''					,   )
       a						    = log_spec[:, :-1]
       a						    = np.maximum(__UpperCAmelCase					,   log_spec.max() - 8.0    )
       a						    = (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  __lowerCAmelCase   ( __UpperCAmelCase :		List[np.ndarray]					,   __UpperCAmelCase :		List[np.ndarray]					,   __UpperCAmelCase :		float = 0.0    )       ->List[np.ndarray]:




       """simple docstring"""


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

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

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

       return normed_input_values







      def __call__( self :		str					,   __UpperCAmelCase :		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Optional[Union[str, TensorType]] = None					,   __UpperCAmelCase :		Optional[bool] = None					,   __UpperCAmelCase :		Optional[str] = "max_length"					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Optional[bool] = None					,   **__UpperCAmelCase :		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.__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.'''    )

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

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

       # always return batch
       if not is_batched:
        a						    = [np.asarray([raw_speech]    ).T]

       a						    = BatchFeature({'''input_features''': raw_speech}    )

       # convert into correct format for padding

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

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

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

       a						    = [self._np_extract_fbank_features(__UpperCAmelCase    ) for waveform in input_features[0]]

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

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

       if return_tensors is not None:
        a						    = padded_inputs.convert_to_tensors(__UpperCAmelCase    )

       return padded_inputs







      def  __lowerCAmelCase   ( self :		int    )       ->Dict[str, Any]:




       """simple docstring"""


       a						    = copy.deepcopy(self.__dict__    )
       a						    = self.__class__.__name__
       if "mel_filters" in output:
        del output["mel_filters"]
       return output










from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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


