
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    a_ (			_a    ):






			def __init__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									super().__init__(*__UpperCamelCase				,			**__UpperCamelCase  )
									self.check_model_type(__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase       ,						_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				,			__UpperCamelCase				,			__UpperCamelCase = None				,			**__UpperCamelCase  ):
									if isinstance(__UpperCamelCase				,			(Image.Image, str)  ) and isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					{"""image""": image, """question""": question}
									else:
															_lowercase					       =					image
									_lowercase					       =					super().__call__(__UpperCamelCase				,			**__UpperCamelCase  )
									return results






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase=False				,			__UpperCamelCase=False  ):
									_lowercase					       =					load_image(inputs["""image"""]  )
									_lowercase					       =					self.tokenizer(
									    inputs["""question"""]				,			return_tensors=self.framework				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase  )
									_lowercase					       =					self.image_processor(images=__UpperCamelCase				,			return_tensors=self.framework  )
									model_inputs.update(__UpperCamelCase  )
									return model_inputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.model(**__UpperCamelCase  )
									return model_outputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase=5  ):
									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       ,						_lowercase					       =					probs.topk(__UpperCamelCase  )
									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(__UpperCamelCase				,			__UpperCamelCase  )]

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()