



"""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  :						int	=       logging.get_logger(__name__)


@add_end_docstrings(lowercase__      )
class       SCREAMING_SNAKE_CASE__       (							lowercase__      ):

    def __init__( self	: str       ,     *lowerCAmelCase_	: Tuple       ,     **lowerCAmelCase_	: Union[str, Any]):







      """simple docstring"""







      super().__init__(*lowercase_       ,     **lowercase_)
      self.check_model_type(lowercase_)

    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: int=None       ,     lowerCAmelCase_	: Any=None       ,     lowerCAmelCase_	: int=None       ,     **lowerCAmelCase_	: Optional[Any]):







      """simple docstring"""







      lowercase_	    =       {}, {}
      if padding is not None:
        lowercase_	    =       padding
      if truncation is not None:
        lowercase_	    =       truncation
      if top_k is not None:
        lowercase_	    =       top_k
      return preprocess_params, {}, postprocess_params

    def __call__( self	: int       ,     lowerCAmelCase_	: Optional[int]       ,     lowerCAmelCase_	: Dict = None       ,     **lowerCAmelCase_	: List[Any]):







      """simple docstring"""







      if isinstance(lowercase_       ,     (Image.Image, str)) and isinstance(lowercase_       ,     lowercase_):
        lowercase_	    =       {"image": image, "question": question}
      else:
        lowercase_	    =       image
      lowercase_	    =       super().__call__(lowercase_       ,     **lowercase_)
      return results

    def     _UpperCAmelCase		( self	: Union[str, Any]       ,     lowerCAmelCase_	: Optional[Any]       ,     lowerCAmelCase_	: Dict=False       ,     lowerCAmelCase_	: int=False):







      """simple docstring"""







      lowercase_	    =       load_image(inputs["""image"""])
      lowercase_	    =       self.tokenizer(
          inputs["""question"""]       ,     return_tensors=self.framework       ,     padding=lowercase_       ,     truncation=lowercase_)
      lowercase_	    =       self.image_processor(images=lowercase_       ,     return_tensors=self.framework)
      model_inputs.update(lowercase_)
      return model_inputs

    def     _UpperCAmelCase		( self	: Any       ,     lowerCAmelCase_	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       self.model(**lowercase_)
      return model_outputs




    def     _UpperCAmelCase		( self	: Optional[int]       ,     lowerCAmelCase_	: Optional[int]       ,     lowerCAmelCase_	: Tuple=5):







      """simple docstring"""







      if top_k > self.model.config.num_labels:
        lowercase_	    =       self.model.config.num_labels

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

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



"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


