


'''simple docstring'''
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'''MCTCTFeatureExtractor'''
      lowerCAmelCase__							=	'''AutoTokenizer'''






      def __init__(   self							:       List[Any]		,		_A							:       Union[str, Any]		,		_A							:       int					):


          '''simple docstring'''


          super().__init__(__lowerCAmelCase		,		__lowerCAmelCase					)
          UpperCAmelCase__     :						int						=							self.feature_extractor
          UpperCAmelCase__     :						Tuple						=							False






      def __call__(   self							:       Optional[Any]		,		*_A							:       Optional[Any]		,		**_A							:       Optional[int]					):


          '''simple docstring'''


          if self._in_target_context_manager:
              return self.current_processor(*__lowerCAmelCase		,		**__lowerCAmelCase					)

          if "raw_speech" in kwargs:
              warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''					)
              UpperCAmelCase__     :						Optional[int]						=							kwargs.pop('''raw_speech'''					)
          else:
              UpperCAmelCase__     :						List[str]						=							kwargs.pop('''audio'''		,		__lowerCAmelCase					)
          UpperCAmelCase__     :						Optional[Any]						=							kwargs.pop('''sampling_rate'''		,		__lowerCAmelCase					)
          UpperCAmelCase__     :						Optional[Any]						=							kwargs.pop('''text'''		,		__lowerCAmelCase					)
          if len(__lowerCAmelCase					) > 0:
              UpperCAmelCase__     :						Dict						=							args[0]
              UpperCAmelCase__     :						List[str]						=							args[1:]

          if audio is None and text is None:
              raise ValueError('''You need to specify either an `audio` or `text` input to process.'''					)

          if audio is not None:
              UpperCAmelCase__     :						Optional[int]						=							self.feature_extractor(__lowerCAmelCase		,		*__lowerCAmelCase		,		sampling_rate=__lowerCAmelCase		,		**__lowerCAmelCase					)
          if text is not None:
              UpperCAmelCase__     :						Tuple						=							self.tokenizer(__lowerCAmelCase		,		**__lowerCAmelCase					)

          if text is None:
              return inputs
          elif audio is None:
              return encodings
          else:
              UpperCAmelCase__     :						Optional[Any]						=							encodings['''input_ids''']
              return inputs






      def lowercase_				(   self							:       Union[str, Any]		,		*_A							:       Dict		,		**_A							:       Any					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       str		,		*_A							:       Any		,		**_A							:       List[str]					):


          '''simple docstring'''


          if self._in_target_context_manager:
              return self.current_processor.pad(*__lowerCAmelCase		,		**__lowerCAmelCase					)

          UpperCAmelCase__     :						Union[str, Any]						=							kwargs.pop('''input_features'''		,		__lowerCAmelCase					)
          UpperCAmelCase__     :						Union[str, Any]						=							kwargs.pop('''labels'''		,		__lowerCAmelCase					)
          if len(__lowerCAmelCase					) > 0:
              UpperCAmelCase__     :						List[Any]						=							args[0]
              UpperCAmelCase__     :						Union[str, Any]						=							args[1:]

          if input_features is not None:
              UpperCAmelCase__     :						Union[str, Any]						=							self.feature_extractor.pad(__lowerCAmelCase		,		*__lowerCAmelCase		,		**__lowerCAmelCase					)
          if labels is not None:
              UpperCAmelCase__     :						Union[str, Any]						=							self.tokenizer.pad(__lowerCAmelCase		,		**__lowerCAmelCase					)

          if labels is None:
              return input_features
          elif input_features is None:
              return labels
          else:
              UpperCAmelCase__     :						List[str]						=							labels['''input_ids''']
              return input_features






      def lowercase_				(   self							:       Any		,		*_A							:       str		,		**_A							:       Optional[int]					):


          '''simple docstring'''


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






      @contextmanager
      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          warnings.warn(
              '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
              '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
              '''your audio inputs, or in a separate call.'''					)
          UpperCAmelCase__     :						Union[str, Any]						=							True
          UpperCAmelCase__     :						List[str]						=							self.tokenizer
          yield
          UpperCAmelCase__     :						str						=							self.feature_extractor
          UpperCAmelCase__     :						int						=							False







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A       :   Optional[int]					= logging.get_logger(__name__)

A       :   Dict					= {
    '''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     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

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



