



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_    :  List[str]						  =		{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Optional[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_    :  Union[str, Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


"""simple docstring"""


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCAmelCase													=logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      str:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          self.check_model_type(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_    )    ->      List[Any]:
          A  ,			A				  =		{}, {}
          if padding is not None:
                A				  =		padding
          if truncation is not None:
                A				  =		truncation
          if top_k is not None:
                A				  =		top_k
          return preprocess_params, {}, postprocess_params




    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_    )    ->      Dict:
          if isinstance(lowerCamelCase_ ,(Image.Image, str)    ) and isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		{"""image""": image, """question""": question}
          else:
                A				  =		image
          A				  =		super().__call__(lowerCamelCase_ ,**lowerCamelCase_    )
          return results




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=False    )    ->      List[Any]:
          A				  =		load_image(inputs["""image"""]    )
          A				  =		self.tokenizer(
              inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_    )
          A				  =		self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework    )
          model_inputs.update(lowerCamelCase_    )
          return model_inputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		self.model(**lowerCamelCase_    )
          return model_outputs




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=5    )    ->      int:
          if top_k > self.model.config.num_labels:
                A				  =		self.model.config.num_labels

          if self.framework == "pt":
                A				  =		model_outputs.logits.sigmoid()[0]
                A  ,			A				  =		probs.topk(lowerCamelCase_    )
          else:
                raise ValueError(f'Unsupported framework: {self.framework}'    )

          A				  =		scores.tolist()
          A				  =		ids.tolist()
          return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase_ ,lowerCamelCase_    )]




