





from typing import TYPE_CHECKING

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


snake_case_				=			{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			['ReformerTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			['ReformerTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_				=			[
        'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ReformerAttention',
        'ReformerForMaskedLM',
        'ReformerForQuestionAnswering',
        'ReformerForSequenceClassification',
        'ReformerLayer',
        'ReformerModel',
        'ReformerModelWithLMHead',
        'ReformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

    snake_case_				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








import warnings

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

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """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(a__				,			return_tensors=a__				,			**a__      )

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

         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(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



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


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



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


      @property
      def    a (self       :      int      ):





         """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      )      )

