






"""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

A__					: Optional[int]=	logging.get_logger(__name__)
@add_end_docstrings(_a  )
class    __lowerCamelCase    (  _a  ):

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=None ,				snake_case_=None ,				snake_case_=None ,				**snake_case_      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    {}, {}
       if padding is not None:
           UpperCamelCase__         =    padding
       if truncation is not None:
           UpperCamelCase__         =    truncation
       if top_k is not None:
           UpperCamelCase__         =    top_k
       return preprocess_params, {}, postprocess_params

   def __call__(					self ,				snake_case_ ,				snake_case_ = None ,				**snake_case_      )		->       Any:
       if isinstance(snake_case_ ,				(Image.Image, str)      ) and isinstance(snake_case_ ,				snake_case_      ):
           UpperCamelCase__         =    {'image': image, 'question': question}
       else:
           UpperCamelCase__         =    image
       UpperCamelCase__         =    super().__call__(snake_case_ ,				**snake_case_      )
       return results

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=False ,				snake_case_=False      )		->       Tuple:
       UpperCamelCase__         =    load_image(inputs['image']      )
       UpperCamelCase__         =    self.tokenizer(
           inputs['question'] ,				return_tensors=self.framework ,				padding=snake_case_ ,				truncation=snake_case_      )
       UpperCamelCase__         =    self.image_processor(images=snake_case_ ,				return_tensors=self.framework      )
       model_inputs.update(snake_case_      )
       return model_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.model(**snake_case_      )
       return model_outputs







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

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

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












"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




