




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





class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Tuple	    =     ['''image_processor''', '''tokenizer''']
       __lowercase					: Union[str, Any]	    =     '''EfficientNetImageProcessor'''
       __lowercase					: Tuple	    =     ('''BertTokenizer''', '''BertTokenizerFast''')


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[Any]:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def __call__(				self      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE="max_length"      ,					__SCREAMING_SNAKE_CASE=64      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




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

            if text is not None:
                 __snake_case         =					self.tokenizer(
                     __SCREAMING_SNAKE_CASE      ,					padding=__SCREAMING_SNAKE_CASE      ,					max_length=__SCREAMING_SNAKE_CASE      ,					return_tensors=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            if images is not None:
                 __snake_case         =					self.image_processor(__SCREAMING_SNAKE_CASE      ,					return_tensors=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            if text is not None and images is not None:
                 __snake_case         =					image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE					)      ,					tensor_type=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




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




       @property
       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.tokenizer.model_input_names
            __snake_case         =					self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)












'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






