



"""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

__snake_case							:   List[str]      =    logging.get_logger(__name__)



@add_end_docstrings(__SCREAMING_SNAKE_CASE							)
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(     self:		str       ,			*_SCREAMING_SNAKE_CASE:		List[str]       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       List[Any]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=None       ,			_SCREAMING_SNAKE_CASE:		Tuple=None       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       {}, {}
				if padding is not None:
							__lowerCAmelCase      :  List[Any]												=       padding
				if truncation is not None:
							__lowerCAmelCase      :  int												=       truncation
				if top_k is not None:
							__lowerCAmelCase      :  str												=       top_k
				return preprocess_params, {}, postprocess_params

	def __call__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		int = None       ,			**_SCREAMING_SNAKE_CASE:		int)				->       Dict:

				"""simple docstring"""

				if isinstance(__lowerCAmelCase       ,			(Image.Image, str)) and isinstance(__lowerCAmelCase       ,			__lowerCAmelCase):
							__lowerCAmelCase      :  List[Any]												=       {"image": image, "question": question}
				else:
							__lowerCAmelCase      :  int												=       image
				__lowerCAmelCase      :  str												=       super().__call__(__lowerCAmelCase       ,			**__lowerCAmelCase)
				return results

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		List[Any]=False       ,			_SCREAMING_SNAKE_CASE:		Any=False)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       load_image(inputs["image"])
				__lowerCAmelCase      :  List[Any]												=       self.tokenizer(
				    inputs["question"]       ,			return_tensors=self.framework       ,			padding=__lowerCAmelCase       ,			truncation=__lowerCAmelCase)
				__lowerCAmelCase      :  Optional[int]												=       self.image_processor(images=__lowerCAmelCase       ,			return_tensors=self.framework)
				model_inputs.update(__lowerCAmelCase)
				return model_inputs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.model(**__lowerCAmelCase)
				return model_outputs







	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=5)				->       Optional[int]:

				"""simple docstring"""

				if top_k > self.model.config.num_labels:
							__lowerCAmelCase      :  Optional[int]												=       self.model.config.num_labels

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

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






_a 						=      65_521





def lowerCAmelCase__(__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		1
	lowerCamelCase__								=		0
	for plain_chr in plain_text:
		lowerCamelCase__								=		(a + ord(__snake_case							)) % MOD_ADLER
		lowerCamelCase__								=		(b + a) % MOD_ADLER
	return (b << 16) | a



