




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__a          =			logging.get_logger(__name__)

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''audio_values''', '''audio_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=2_048       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=[16, 16]       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=44_100       ,_SCREAMING_SNAKE_CASE=86       ,_SCREAMING_SNAKE_CASE=2_048       ,_SCREAMING_SNAKE_CASE=0.0       ,**_SCREAMING_SNAKE_CASE       ,)			->		Tuple:
         super().__init__(
             feature_size=_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,padding_value=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							Optional[int]       =      spectrogram_length
         UpperCAmelCase_      :							List[Any]       =      num_channels
         UpperCAmelCase_      :							List[Any]       =      patch_size
         UpperCAmelCase_      :							Any       =      feature_size // self.patch_size[1]
         UpperCAmelCase_      :							Any       =      n_fft
         UpperCAmelCase_      :							Tuple       =      sampling_rate // hop_length_to_sampling_rate
         UpperCAmelCase_      :							Optional[Any]       =      sampling_rate
         UpperCAmelCase_      :							Union[str, Any]       =      padding_value
         UpperCAmelCase_      :							Tuple       =      mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,num_mel_filters=_SCREAMING_SNAKE_CASE       ,min_frequency=0.0       ,max_frequency=2_20_50.0       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,norm='''slaney'''       ,mel_scale='''slaney'''       ,).T





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		np.ndarray:
         UpperCAmelCase_      :							Union[str, Any]       =      spectrogram(
             _SCREAMING_SNAKE_CASE       ,window_function(self.n_fft       ,'''hann'''  )       ,frame_length=self.n_fft       ,hop_length=self.hop_length       ,power=2.0       ,mel_filters=self.mel_filters.T       ,log_mel='''dB'''       ,db_range=80.0       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      log_spec[:, :-1]
         UpperCAmelCase_      :							int       =      log_spec - 20.0
         UpperCAmelCase_      :							Tuple       =      np.clip(log_spec / 40.0       ,-2.0       ,0.0  ) + 1.0
         return log_spec





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = False       ,**_SCREAMING_SNAKE_CASE       ,)			->		BatchFeature:

         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       '''This feature extractor is set to support sampling rate'''
                       f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                       f''' 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.'''  )

         UpperCAmelCase_      :							str       =      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}'''  )
         UpperCAmelCase_      :							Optional[int]       =      is_batched_numpy or (
             isinstance(_SCREAMING_SNAKE_CASE       ,(list, tuple)  ) and (isinstance(raw_speech[0]       ,(np.ndarray, tuple, list)  ))
         )
         if is_batched:
              UpperCAmelCase_      :							str       =      [np.asarray([speech]       ,dtype=np.floataa  ).T for speech in raw_speech]
         elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  ):
              UpperCAmelCase_      :							str       =      np.asarray(_SCREAMING_SNAKE_CASE       ,dtype=np.floataa  )
         elif isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
              UpperCAmelCase_      :							str       =      raw_speech.astype(np.floataa  )
         # always return batch
         if not is_batched:
              UpperCAmelCase_      :							Any       =      [np.asarray([raw_speech]  ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         UpperCAmelCase_      :							Any       =      [
             self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							List[str]       =      [np.asarray(_SCREAMING_SNAKE_CASE       ,dtype=np.floataa  ) for feature in audio_features]

         # Create audio attention mask
         UpperCAmelCase_      :							List[Any]       =      max(
             [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
         if return_attention_mask:
              UpperCAmelCase_      :							Union[str, Any]       =      [
                  (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                  + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                  for feature in audio_features
              ]
              UpperCAmelCase_      :							Tuple       =      np.array(_SCREAMING_SNAKE_CASE  ).astype(np.floataa  )

         # convert into correct format for padding
         UpperCAmelCase_      :							int       =      max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         UpperCAmelCase_      :							List[Any]       =      np.ones([len(_SCREAMING_SNAKE_CASE  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
         UpperCAmelCase_      :							Any       =      padded_audio_features * self.padding_value
         for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
              UpperCAmelCase_      :							List[str]       =      audio_features[i]
              UpperCAmelCase_      :							int       =      feature

         # return as BatchFeature
         if return_attention_mask:
              UpperCAmelCase_      :							List[Any]       =      {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
              UpperCAmelCase_      :							List[Any]       =      {'''audio_values''': padded_audio_features}

         UpperCAmelCase_      :							Optional[Any]       =      BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )
         return encoded_inputs





from typing import TYPE_CHECKING

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


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)