




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

_lowerCamelCase	:		Union[str, Any]							       =   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Dict)						->Dict:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Optional[int]=None					, UpperCAmelCase__      :	int=None					, **UpperCAmelCase__      :	Any)						->str:





           '''simple docstring'''

           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      :	Dict					, UpperCAmelCase__      :	Union["Image.Image", str]					, UpperCAmelCase__      :	str = None					, **UpperCAmelCase__      :	List[Any])						->List[Any]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Union[str, Any]=False					, UpperCAmelCase__      :	Tuple=False)						->Optional[Any]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	List[Any])						->str:





           '''simple docstring'''

           A__       =    self.model(**UpperCAmelCase__)
           return model_outputs





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[int]=5)						->List[Any]:





           '''simple docstring'''

           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(UpperCAmelCase__)
           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(UpperCAmelCase__					, UpperCAmelCase__)]







'''simple docstring'''





_UpperCamelCase											=						tuple[float, float, float]
_UpperCamelCase											=						tuple[float, float, float]

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       Any           = end_pointa[0] - end_pointa[0]
  __lowerCamelCase       :       str           = end_pointa[1] - end_pointa[1]
  __lowerCamelCase       :       Tuple           = end_pointa[2] - end_pointa[2]
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       List[str]           = ab[1] * ac[2] - ab[2] * ac[1]  # *i
  __lowerCamelCase       :       Dict           = (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
  __lowerCamelCase       :       List[Any]           = ab[0] * ac[1] - ab[1] * ac[0]  # *k
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      bool:
  return tuple(round(_lowerCAmelCase       ,_lowerCAmelCase       ) for x in vector       ) == (0, 0, 0)


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 10       )      ->      bool:
  __lowerCamelCase       :       str           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  return is_zero_vector(get_ad_vectors_cross(_lowerCAmelCase       ,_lowerCAmelCase       )       ,_lowerCAmelCase       )






