



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Dict  	=    ["image_processor", "tokenizer"]
       UpperCAmelCase__							:   Optional[Any]  	=    "BlipImageProcessor"
       UpperCAmelCase__							:   Optional[Any]  	=    "AutoTokenizer"
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    List[Any]		      =      False
          super().__init__(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[str]		      =      self.image_processor
       def __call__(     self,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = 0,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = True,     SCREAMING_SNAKE_CASE_ = None,     **SCREAMING_SNAKE_CASE_,     )	->						BatchEncoding:
          if images is None and text is None:
             raise ValueError('You have to specify either images or text.'			)

          # Get only text
          if images is None:
             UpperCamelCase       :    Union[str, Any]		      =      self.tokenizer
             UpperCamelCase       :    Optional[Any]		      =      self.tokenizer(
                 text=SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=SCREAMING_SNAKE_CASE_,     stride=SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_,     return_attention_mask=SCREAMING_SNAKE_CASE_,     return_overflowing_tokens=SCREAMING_SNAKE_CASE_,     return_special_tokens_mask=SCREAMING_SNAKE_CASE_,     return_offsets_mapping=SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_,     return_length=SCREAMING_SNAKE_CASE_,     verbose=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
             return text_encoding

          # add pixel_values
          UpperCamelCase       :    Dict		      =      self.image_processor(SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_			)

          if text is not None:
             UpperCamelCase       :    Any		      =      self.tokenizer(
                 text=SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=SCREAMING_SNAKE_CASE_,     stride=SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_,     return_attention_mask=SCREAMING_SNAKE_CASE_,     return_overflowing_tokens=SCREAMING_SNAKE_CASE_,     return_special_tokens_mask=SCREAMING_SNAKE_CASE_,     return_offsets_mapping=SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_,     return_length=SCREAMING_SNAKE_CASE_,     verbose=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
          else:
             UpperCamelCase       :    Dict		      =      None

          if text_encoding is not None:
             encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

          return encoding_image_processor
       def    snake_case_			(     self,     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						List[str]:
          return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     *SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)	->						Any:
          return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    Optional[Any]		      =      self.tokenizer.model_input_names
          UpperCamelCase       :    List[str]		      =      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_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



