






'''simple docstring'''







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

class _lowercase			(       snake_case_	):
       a          = """ClapFeatureExtractor"""
       a          = ("""RobertaTokenizer""", """RobertaTokenizerFast""")




       def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
              super().__init__(UpperCamelCase__    ,							UpperCamelCase__  )




       def __call__(							self:       str    ,							UpperCamelCase__:       List[str]=None    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Union[str, Any]=None    ,							**UpperCamelCase__:       Any  ):
              lowerCamelCase__      :			int 						= kwargs.pop("""sampling_rate"""    ,							UpperCamelCase__  )

              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:
                     lowerCamelCase__      :			Optional[int] 						= self.tokenizer(UpperCamelCase__    ,							return_tensors=UpperCamelCase__    ,							**UpperCamelCase__  )

              if audios is not None:
                     lowerCamelCase__      :			List[Any] 						= self.feature_extractor(
                         UpperCamelCase__    ,							sampling_rate=UpperCamelCase__    ,							return_tensors=UpperCamelCase__    ,							**UpperCamelCase__  )

              if text is not None and audios is not None:
                     lowerCamelCase__      :			Optional[int] 						= audio_features.input_features
                     return encoding
              elif text is not None:
                     return encoding
              else:
                     return BatchEncoding(data=dict(**UpperCamelCase__  )    ,							tensor_type=UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       Dict    ,							*UpperCamelCase__:       int    ,							**UpperCamelCase__:       str  ):
              return self.tokenizer.batch_decode(*UpperCamelCase__    ,							**UpperCamelCase__  )




       def 	lowerCamelCase_       (							self:       Dict    ,							*UpperCamelCase__:       List[str]    ,							**UpperCamelCase__:       List[str]  ):
              return self.tokenizer.decode(*UpperCamelCase__    ,							**UpperCamelCase__  )




       @property
       def 	lowerCamelCase_       (							self:       List[str]  ):
              lowerCamelCase__      :			Dict 						= self.tokenizer.model_input_names
              lowerCamelCase__      :			Dict 						= 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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

