
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""ClapFeatureExtractor"""
     A__      =			("""RobertaTokenizer""", """RobertaTokenizerFast""")
     def __init__(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       super().__init__(snake_case__  ,     snake_case__				)
     def __call__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = kwargs.pop("sampling_rate"  ,     snake_case__				)

       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:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.tokenizer(snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)

       if audios is not None:
         _SCREAMING_SNAKE_CASE   :				str							    = self.feature_extractor(
             snake_case__  ,     sampling_rate=snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)

       if text is not None and audios is not None:
         _SCREAMING_SNAKE_CASE   :				str							    = audio_features.input_features
         return encoding
       elif text is not None:
         return encoding
       else:
         return BatchEncoding(data=dict(**snake_case__				)  ,     tensor_type=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


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




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.tokenizer.model_input_names
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names				)				)





"""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__				)



