


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




@add_end_docstrings(UpperCamelCase)
class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Optional[int]      ,      *UpperCAmelCase__      :    Any      ,      **UpperCAmelCase__      :    Any       )	->     Dict:
						super().__init__(*UpperCAmelCase__      ,      **UpperCAmelCase__       )
						self.check_model_type(UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    Optional[int]=None      ,      UpperCAmelCase__      :    List[str]=None      ,      UpperCAmelCase__      :    Union[str, Any]=None      ,      **UpperCAmelCase__      :    Any       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    {}, {}
						if padding is not None:
											__SCREAMING_SNAKE_CASE						       =    padding
						if truncation is not None:
											__SCREAMING_SNAKE_CASE						       =    truncation
						if top_k is not None:
											__SCREAMING_SNAKE_CASE						       =    top_k
						return preprocess_params, {}, postprocess_params




	def __call__(			self      :    int      ,      UpperCAmelCase__      :    Union["Image.Image", str]      ,      UpperCAmelCase__      :    str = None      ,      **UpperCAmelCase__      :    List[Any]       )	->     str:
						if isinstance(UpperCAmelCase__      ,      (Image.Image, str)       ) and isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    {"image": image, "question": question}
						else:
											__SCREAMING_SNAKE_CASE						       =    image
						__SCREAMING_SNAKE_CASE						       =    super().__call__(UpperCAmelCase__      ,      **UpperCAmelCase__       )
						return results




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Dict=False      ,      UpperCAmelCase__      :    Tuple=False       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    load_image(inputs["image"]       )
						__SCREAMING_SNAKE_CASE						       =    self.tokenizer(
						    inputs["question"]      ,      return_tensors=self.framework      ,      padding=UpperCAmelCase__      ,      truncation=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    self.image_processor(images=UpperCAmelCase__      ,      return_tensors=self.framework       )
						model_inputs.update(UpperCAmelCase__       )
						return model_inputs




	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.model(**UpperCAmelCase__       )
						return model_outputs




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    int=5       )	->     List[Any]:
						if top_k > self.model.config.num_labels:
											__SCREAMING_SNAKE_CASE						       =    self.model.config.num_labels

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

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




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if not nums:
										return 0
					__SCREAMING_SNAKE_CASE						       =    nums[0]
					__SCREAMING_SNAKE_CASE						       =    0
					for num in nums[1:]:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (
										    max_excluding + num,
										    max(lowerCAmelCase_  ,						lowerCAmelCase_       ),
										)
					return max(lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
