





"""simple docstring"""
from ...processing_utils import ProcessorMixin







class       a							(   a__  ):
 snake_case__             =       '''SpeechT5FeatureExtractor'''
 snake_case__             =       '''SpeechT5Tokenizer'''



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  super().__init__(_snake_case				,       _snake_case     )



 def __call__(     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.pop('audio'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('text'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('text_target'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('audio_target'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('sampling_rate'				,       _snake_case     )

  if audio is not None and text is not None:
   raise ValueError(
       'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'     )
  if audio_target is not None and text_target is not None:
   raise ValueError(
       'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'     )
  if audio is None and audio_target is None and text is None and text_target is None:
   raise ValueError(
       'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'     )

  if audio is not None:
   lowerCAmelCase    				= self.feature_extractor(_snake_case				,       *_snake_case				,       sampling_rate=_snake_case				,       **_snake_case     )
  elif text is not None:
   lowerCAmelCase    				= self.tokenizer(_snake_case				,       **_snake_case     )
  else:
   lowerCAmelCase    				= None

  if audio_target is not None:
   lowerCAmelCase    				= self.feature_extractor(audio_target=_snake_case				,       *_snake_case				,       sampling_rate=_snake_case				,       **_snake_case     )
   lowerCAmelCase    				= targets['input_values']
  elif text_target is not None:
   lowerCAmelCase    				= self.tokenizer(_snake_case				,       **_snake_case     )
   lowerCAmelCase    				= targets['input_ids']
  else:
   lowerCAmelCase    				= None

  if inputs is None:
   return targets

  if targets is not None:
   lowerCAmelCase    				= labels

   lowerCAmelCase    				= targets.get('attention_mask'     )
   if decoder_attention_mask is not None:
    lowerCAmelCase    				= decoder_attention_mask

  return inputs



 def    UpperCamelCase__  (     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.pop('input_values'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('input_ids'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('labels'				,       _snake_case     )

  if input_values is not None and input_ids is not None:
   raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'     )
  if input_values is None and input_ids is None and labels is None:
   raise ValueError(
       'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'     )

  if input_values is not None:
   lowerCAmelCase    				= self.feature_extractor.pad(_snake_case				,       *_snake_case				,       **_snake_case     )
  elif input_ids is not None:
   lowerCAmelCase    				= self.tokenizer.pad(_snake_case				,       **_snake_case     )
  else:
   lowerCAmelCase    				= None

  if labels is not None:
   if "input_ids" in labels or (isinstance(_snake_case				,       _snake_case     ) and "input_ids" in labels[0]):
    lowerCAmelCase    				= self.tokenizer.pad(_snake_case				,       **_snake_case     )
    lowerCAmelCase    				= targets['input_ids']
   else:
    lowerCAmelCase    				= self.feature_extractor.feature_size
    lowerCAmelCase    				= self.feature_extractor.num_mel_bins
    lowerCAmelCase    				= self.feature_extractor.pad(_snake_case				,       *_snake_case				,       **_snake_case     )
    lowerCAmelCase    				= feature_size_hack
    lowerCAmelCase    				= targets['input_values']
  else:
   lowerCAmelCase    				= None

  if inputs is None:
   return targets

  if targets is not None:
   lowerCAmelCase    				= labels

   lowerCAmelCase    				= targets.get('attention_mask'     )
   if decoder_attention_mask is not None:
    lowerCAmelCase    				= decoder_attention_mask

  return inputs



 def    UpperCamelCase__  (     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  return self.tokenizer.batch_decode(*_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  return self.tokenizer.decode(*_snake_case				,       **_snake_case     )












"""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





