





from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)
class 			UpperCamelCase_		(  _lowerCamelCase					):
     lowerCAmelCase_							=	['input_features', 'attention_mask']




     def __init__(		self				,						lowerCAmelCase_=80				,						lowerCAmelCase_=1_6000				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=10				,						lowerCAmelCase_=25				,						lowerCAmelCase_="hamming_window"				,						lowerCAmelCase_=3_2768.0				,						lowerCAmelCase_=0.97				,						lowerCAmelCase_=1.0				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=False				,						**lowerCAmelCase_				,						)		->							Optional[int]:
          super().__init__(feature_size=UpperCAmelCase__				,						sampling_rate=UpperCAmelCase__				,						padding_value=UpperCAmelCase__				,						**UpperCAmelCase__  )

          _snake_case									=				feature_size
          _snake_case									=				sampling_rate
          _snake_case									=				padding_value
          _snake_case									=				hop_length
          _snake_case									=				win_length
          _snake_case									=				frame_signal_scale
          _snake_case									=				preemphasis_coeff
          _snake_case									=				mel_floor
          _snake_case									=				normalize_means
          _snake_case									=				normalize_vars
          _snake_case									=				win_function
          _snake_case									=				return_attention_mask

          _snake_case									=				win_length * sampling_rate // 1000
          _snake_case									=				hop_length * sampling_rate // 1000

          _snake_case									=				optimal_fft_length(self.sample_size  )
          _snake_case									=				(self.n_fft // 2) + 1




     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Any:
          if self.win_function == "hamming_window":
               _snake_case									=				window_function(window_length=self.sample_size				,						name=self.win_function				,						periodic=UpperCAmelCase__  )
          else:
               _snake_case									=				window_function(window_length=self.sample_size				,						name=self.win_function  )

          _snake_case									=				mel_filter_bank(
              num_frequency_bins=self.n_freqs				,						num_mel_filters=self.feature_size				,						min_frequency=0.0				,						max_frequency=self.sampling_rate / 2.0				,						sampling_rate=self.sampling_rate				,						)

          _snake_case									=				spectrogram(
              one_waveform * self.frame_signal_scale				,						window=UpperCAmelCase__				,						frame_length=self.sample_size				,						hop_length=self.sample_stride				,						fft_length=self.n_fft				,						center=UpperCAmelCase__				,						preemphasis=self.preemphasis_coeff				,						mel_filters=UpperCAmelCase__				,						mel_floor=self.mel_floor				,						log_mel='log'				,						)
          return msfc_features.T




     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							str:
          # make sure we normalize float32 arrays
          if self.normalize_means:
               _snake_case									=				x[:input_length].mean(axis=0  )
               _snake_case									=				np.subtract(UpperCAmelCase__				,						UpperCAmelCase__  )
          if self.normalize_vars:
               _snake_case									=				x[:input_length].std(axis=0  )
               _snake_case									=				np.divide(UpperCAmelCase__				,						UpperCAmelCase__  )

          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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None  )		->							List[Any]:
          _snake_case									=				attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(UpperCAmelCase__				,						UpperCAmelCase__				,						self.padding_value  ) for x, n in zip(UpperCAmelCase__				,						UpperCAmelCase__  )]






     def __call__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							Any:

          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(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}'''  )
          _snake_case									=				is_batched_numpy or (
              isinstance(UpperCAmelCase__				,						(list, tuple)  ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)  ))
          )

          if is_batched:
               _snake_case									=				[np.asarray(UpperCAmelCase__				,						dtype=np.floataa  ) for speech in raw_speech]
          elif not is_batched and not isinstance(UpperCAmelCase__				,						np.ndarray  ):
               _snake_case									=				np.asarray(UpperCAmelCase__				,						dtype=np.floataa  )
          elif isinstance(UpperCAmelCase__				,						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_mfsc_features(UpperCAmelCase__  ) for one_waveform in raw_speech]

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

          _snake_case									=				self.pad(
              UpperCAmelCase__				,						padding=UpperCAmelCase__				,						max_length=UpperCAmelCase__				,						truncation=UpperCAmelCase__				,						pad_to_multiple_of=UpperCAmelCase__				,						return_attention_mask=UpperCAmelCase__				,						**UpperCAmelCase__				,						)
          # make sure list is in array format
          _snake_case									=				padded_inputs.get('input_features'  )
          if isinstance(input_features[0]				,						UpperCAmelCase__  ):
               _snake_case									=				[np.asarray(UpperCAmelCase__				,						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(UpperCAmelCase__				,						dtype=np.intaa  ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
               _snake_case									=				(
                   np.array(UpperCAmelCase__				,						dtype=np.intaa  )
                   if self._get_padding_strategies(UpperCAmelCase__				,						max_length=UpperCAmelCase__  ) is not PaddingStrategy.DO_NOT_PAD
                   and padding
                   else None
               )
               _snake_case									=				self.normalize(
                   padded_inputs['input_features']				,						attention_mask=UpperCAmelCase__  )

          if return_tensors is not None:
               _snake_case									=				padded_inputs.convert_to_tensors(UpperCAmelCase__  )

          return padded_inputs










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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



