

'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					"""ClapFeatureExtractor"""
 __SCREAMING_SNAKE_CASE					:List[str]      	=					("""RobertaTokenizer""", """RobertaTokenizerFast""")



 def __init__(   self		:   int			,				a__		:   str			,				a__		:   List[Any]      ):
      super().__init__(a__			,				a__      )



 def __call__(   self		:   List[str]			,				a__		:   List[Any]=None			,				a__		:   Any=None			,				a__		:   str=None			,				**a__		:   int      ):
      __magic_name__						       =    kwargs.pop('''sampling_rate'''			,				a__      )

      if text is None and audios is None:
           raise ValueError('''You have to specify either text or audios. Both cannot be none.'''      )

      if text is not None:
           __magic_name__						       =    self.tokenizer(a__			,				return_tensors=a__			,				**a__      )

      if audios is not None:
           __magic_name__						       =    self.feature_extractor(
               a__			,				sampling_rate=a__			,				return_tensors=a__			,				**a__      )

      if text is not None and audios is not None:
           __magic_name__						       =    audio_features.input_features
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**a__      )			,				tensor_type=a__      )



 def 			snake_case__  (   self		:   List[Any]			,				*a__		:   Tuple			,				**a__		:   List[Any]      ):
      return self.tokenizer.batch_decode(*a__			,				**a__      )



 def 			snake_case__  (   self		:   List[str]			,				*a__		:   int			,				**a__		:   Union[str, Any]      ):
      return self.tokenizer.decode(*a__			,				**a__      )



 @property
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.tokenizer.model_input_names
      __magic_name__						       =    self.feature_extractor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names      )      )









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






