





"""simple docstring"""
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     :						List[str]						       =  logging.get_logger(__name__)







class       a							(   a__  ):
 snake_case__             =       ['''input_features''']



 def __init__(     self				,       _snake_case=80				,       _snake_case=1_60_00				,       _snake_case=1_60				,       _snake_case=30				,       _snake_case=4_00				,       _snake_case=0.0				,       _snake_case=False				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(
      feature_size=_snake_case				,       sampling_rate=_snake_case				,       padding_value=_snake_case				,       return_attention_mask=_snake_case				,       **_snake_case				,       )
  lowerCAmelCase    				= n_fft
  lowerCAmelCase    				= hop_length
  lowerCAmelCase    				= chunk_length
  lowerCAmelCase    				= chunk_length * sampling_rate
  lowerCAmelCase    				= self.n_samples // hop_length
  lowerCAmelCase    				= sampling_rate
  lowerCAmelCase    				= mel_filter_bank(
      num_frequency_bins=1 + n_fft // 2				,       num_mel_filters=_snake_case				,       min_frequency=0.0				,       max_frequency=8_000.0				,       sampling_rate=_snake_case				,       norm='slaney'				,       mel_scale='slaney'				,       )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= 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				,       log_mel='log10'				,       )
  lowerCAmelCase    				= log_spec[:, :-1]
  lowerCAmelCase    				= np.maximum(_snake_case				,       log_spec.max() - 8.0     )
  lowerCAmelCase    				= (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    UpperCamelCase__  (     _snake_case				,       _snake_case				,       _snake_case = 0.0     ):







  """simple docstring"""






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

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

    normed_input_values.append(_snake_case     )
  else:
   lowerCAmelCase    				= [(x - x.mean()) / np.sqrt(x.var() + 1E-7     ) for x in input_values]

  return normed_input_values



 def __call__(     self				,       _snake_case				,       _snake_case = True				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = "max_length"				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       **_snake_case				,       ):







  """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.'     )

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

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

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

  # convert into correct format for padding

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

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

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

  lowerCAmelCase    				= [self._np_extract_fbank_features(_snake_case     ) for waveform in input_features[0]]

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

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

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

  return padded_inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						List[str]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





