

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



class       __snake_case  (    lowerCAmelCase__       ):
		__lowerCAmelCase     :	str						 =  'ClapFeatureExtractor'
		__lowerCAmelCase     :	Union[str, Any]						 =  ('RobertaTokenizer', 'RobertaTokenizerFast')

		def __init__(					self						,		_A						,		_A):
						super().__init__(__lowerCAmelCase						,		__lowerCAmelCase)

		def __call__(					self						,		_A=None						,		_A=None						,		_A=None						,		**_A):
						SCREAMING_SNAKE_CASE_									=						kwargs.pop('sampling_rate'						,		__lowerCAmelCase)

						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_									=						self.tokenizer(__lowerCAmelCase						,		return_tensors=__lowerCAmelCase						,		**__lowerCAmelCase)

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

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

		def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
						return self.tokenizer.batch_decode(*__lowerCAmelCase						,		**__lowerCAmelCase)

		def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
						return self.tokenizer.decode(*__lowerCAmelCase						,		**__lowerCAmelCase)


		@property
		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE_									=						self.feature_extractor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "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

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


