
"""simple docstring"""
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


lowercase_       :						Dict     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							UpperCamelCase_  ):
     A__      =			["""audio_values""", """audio_mask"""]
     def __init__(     self  ,     snake_case__=2048  ,     snake_case__=1  ,     snake_case__=[16, 16]  ,     snake_case__=128  ,     snake_case__=44100  ,     snake_case__=86  ,     snake_case__=2048  ,     snake_case__=0.0  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(
           feature_size=snake_case__  ,     sampling_rate=snake_case__  ,     padding_value=snake_case__  ,     **snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = spectrogram_length
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = num_channels
       _SCREAMING_SNAKE_CASE   :				Any							    = patch_size
       _SCREAMING_SNAKE_CASE   :				Any							    = feature_size // self.patch_size[1]
       _SCREAMING_SNAKE_CASE   :				List[str]							    = n_fft
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = sampling_rate // hop_length_to_sampling_rate
       _SCREAMING_SNAKE_CASE   :				List[str]							    = sampling_rate
       _SCREAMING_SNAKE_CASE   :				Any							    = padding_value
       _SCREAMING_SNAKE_CASE   :				Dict							    = mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2  ,     num_mel_filters=snake_case__  ,     min_frequency=0.0  ,     max_frequency=22_050.0  ,     sampling_rate=snake_case__  ,     norm="slaney"  ,     mel_scale="slaney"  ,     ).T
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = spectrogram(
           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  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = log_spec[:, :-1]
       _SCREAMING_SNAKE_CASE   :				Any							    = log_spec - 20.0
       _SCREAMING_SNAKE_CASE   :				Any							    = np.clip(log_spec / 40.0  ,     -2.0  ,     0.0				) + 1.0
       return log_spec




     def __call__(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = False  ,     **snake_case__  ,     ):




       """simple docstring"""


       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."				)

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

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

       # Create audio attention mask
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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:
         _SCREAMING_SNAKE_CASE   :				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
         ]
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = np.array(snake_case__				).astype(np.floataa				)

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

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

       _SCREAMING_SNAKE_CASE   :				Dict							    = BatchFeature(data=snake_case__  ,     tensor_type=snake_case__				)
       return encoded_inputs





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_       :						Union[str, Any]     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __init__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       warnings.warn(
           "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use LayoutLMv2ImageProcessor instead."  ,     snake_case__  ,     )
       super().__init__(*snake_case__  ,     **snake_case__				)



