




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


__magic_name__  :  Optional[Any]					=       logging.get_logger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Optional[int]				= ["input_features", "attention_mask"]
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[Any]=80       ,						__lowerCamelCase	:		List[str]=1_60_00       ,						__lowerCamelCase	:		List[str]=0.0       ,						__lowerCamelCase	:		Dict=10       ,						__lowerCamelCase	:		Optional[int]=25       ,						__lowerCamelCase	:		List[str]="hamming_window"       ,						__lowerCamelCase	:		List[Any]=3_2768.0       ,						__lowerCamelCase	:		Optional[Any]=0.97       ,						__lowerCamelCase	:		Optional[Any]=1.0       ,						__lowerCamelCase	:		Any=True       ,						__lowerCamelCase	:		Optional[int]=True       ,						__lowerCamelCase	:		str=False       ,						**__lowerCamelCase	:		Union[str, Any]       ,						):



       """simple docstring"""
       super().__init__(feature_size=__lowerCamelCase       ,						sampling_rate=__lowerCamelCase       ,						padding_value=__lowerCamelCase       ,						**__lowerCamelCase						)

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

       lowerCAmelCase__										=      win_length * sampling_rate // 10_00
       lowerCAmelCase__										=      hop_length * sampling_rate // 10_00

       lowerCAmelCase__										=      optimal_fft_length(self.sample_size						)
       lowerCAmelCase__										=      (self.n_fft // 2) + 1
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		np.array						):



       """simple docstring"""
       if self.win_function == "hamming_window":
         lowerCAmelCase__										=      window_function(window_length=self.sample_size       ,						name=self.win_function       ,						periodic=__lowerCamelCase						)
       else:
         lowerCAmelCase__										=      window_function(window_length=self.sample_size       ,						name=self.win_function						)

       lowerCAmelCase__										=      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       ,						)

       lowerCAmelCase__										=      spectrogram(
           one_waveform * self.frame_signal_scale       ,						window=__lowerCamelCase       ,						frame_length=self.sample_size       ,						hop_length=self.sample_stride       ,						fft_length=self.n_fft       ,						center=__lowerCamelCase       ,						preemphasis=self.preemphasis_coeff       ,						mel_filters=__lowerCamelCase       ,						mel_floor=self.mel_floor       ,						log_mel='''log'''       ,						)
       return msfc_features.T
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       # make sure we normalize float32 arrays
       if self.normalize_means:
         lowerCAmelCase__										=      x[:input_length].mean(axis=0						)
         lowerCAmelCase__										=      np.subtract(__lowerCamelCase       ,						__lowerCamelCase						)
       if self.normalize_vars:
         lowerCAmelCase__										=      x[:input_length].std(axis=0						)
         lowerCAmelCase__										=      np.divide(__lowerCamelCase       ,						__lowerCamelCase						)

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

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

       return x
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		List[np.ndarray]       ,						__lowerCamelCase	:		Optional[np.ndarray] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(__lowerCamelCase       ,						__lowerCamelCase       ,						self.padding_value						) for x, n in zip(__lowerCamelCase       ,						__lowerCamelCase						)]



     def __call__(						self	:		List[str]       ,						__lowerCamelCase	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,						__lowerCamelCase	:		Union[bool, str, PaddingStrategy] = False       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		bool = False       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		Optional[bool] = None       ,						__lowerCamelCase	:		Optional[Union[str, TensorType]] = None       ,						__lowerCamelCase	:		Optional[int] = None       ,						**__lowerCamelCase	:		List[Any]       ,						):



       """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(__lowerCamelCase       ,						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(__lowerCamelCase       ,						(list, tuple)						) and (isinstance(raw_speech[0]       ,						(np.ndarray, tuple, list)						))
       )

       if is_batched:
         lowerCAmelCase__										=      [np.asarray(__lowerCamelCase       ,						dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(__lowerCamelCase       ,						np.ndarray						):
         lowerCAmelCase__										=      np.asarray(__lowerCamelCase       ,						dtype=np.floataa						)
       elif isinstance(__lowerCamelCase       ,						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
       lowerCAmelCase__										=      [self._extract_mfsc_features(__lowerCamelCase						) for one_waveform in raw_speech]

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

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

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

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

       if return_tensors is not None:
         lowerCAmelCase__										=      padded_inputs.convert_to_tensors(__lowerCamelCase						)

       return padded_inputs







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

