


'''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   :    Tuple											= logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(  self       :    Union[str, Any]       ,		*lowerCamelCase       :    List[Any]       ,		**lowerCamelCase       :    str						)  ->    Dict:
      super().__init__(*lowerCamelCase       ,		**lowerCamelCase						)
      self.check_model_type(lowerCamelCase						)

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Optional[int]=None       ,		lowerCamelCase       :    List[str]=None       ,		lowerCamelCase       :    Dict=None       ,		**lowerCamelCase       :    List[Any]						)  ->    Tuple:
      lowerCAmelCase_,				lowerCAmelCase_     : Tuple     =  {}, {}
      if padding is not None:
        lowerCAmelCase_     : Optional[Any]     =  padding
      if truncation is not None:
        lowerCAmelCase_     : Union[str, Any]     =  truncation
      if top_k is not None:
        lowerCAmelCase_     : Any     =  top_k
      return preprocess_params, {}, postprocess_params

    def __call__(  self       :    Dict       ,		lowerCamelCase       :    Union["Image.Image", str]       ,		lowerCamelCase       :    str = None       ,		**lowerCamelCase       :    Optional[Any]						)  ->    Any:
      if isinstance(lowerCamelCase       ,		(Image.Image, str)						) and isinstance(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : str     =  {"""image""": image, """question""": question}
      else:
        lowerCAmelCase_     : str     =  image
      lowerCAmelCase_     : str     =  super().__call__(lowerCamelCase       ,		**lowerCamelCase						)
      return results

    def __lowercase  (  self       :    str       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    Tuple=False       ,		lowerCamelCase       :    str=False						)  ->    List[Any]:
      lowerCAmelCase_     : int     =  load_image(inputs["""image"""]						)
      lowerCAmelCase_     : Union[str, Any]     =  self.tokenizer(
          inputs["""question"""]       ,		return_tensors=self.framework       ,		padding=lowerCamelCase       ,		truncation=lowerCamelCase						)
      lowerCAmelCase_     : Any     =  self.image_processor(images=lowerCamelCase       ,		return_tensors=self.framework						)
      model_inputs.update(lowerCamelCase						)
      return model_inputs

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Any						)  ->    List[Any]:
      lowerCAmelCase_     : str     =  self.model(**lowerCamelCase						)
      return model_outputs





    def __lowercase  (  self       :    str       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    int=5						)  ->    Tuple:
      if top_k > self.model.config.num_labels:
        lowerCAmelCase_     : Tuple     =  self.model.config.num_labels

      if self.framework == "pt":
        lowerCAmelCase_     : str     =  model_outputs.logits.sigmoid()[0]
        lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  probs.topk(lowerCamelCase						)
      else:
        raise ValueError(F'Unsupported framework: {self.framework}'						)

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







'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  float   ):
  '''simple docstring'''





  if initial_intensity < 0:
    raise ValueError("""The value of intensity cannot be negative"""   )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_60:
    raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees"""   )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



