

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    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



			def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
					super().__init__(*__A,		**__A						)
					self.check_model_type(__A						)



			def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
					UpperCAmelCase	:							List[Any]						=			{}, {}
					if padding is not None:
							UpperCAmelCase	:							Optional[int]						=			padding
					if truncation is not None:
							UpperCAmelCase	:							Optional[int]						=			truncation
					if top_k is not None:
							UpperCAmelCase	:							Tuple						=			top_k
					return preprocess_params, {}, postprocess_params



			def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
					if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
							UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
					else:
							UpperCAmelCase	:							str						=			image
					UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
					return results



			def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
					UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
					UpperCAmelCase	:							List[str]						=			self.tokenizer(
					    inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
					UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
					model_inputs.update(__A						)
					return model_inputs



			def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
					UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
					return model_outputs





			def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
					if top_k > self.model.config.num_labels:
							UpperCAmelCase	:							Any						=			self.model.config.num_labels

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

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








def       a__					(		UpperCAmelCase							:      int      )  ->						bool:
  if not isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
    UpperCAmelCase	:							List[str]						=			f'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCAmelCase      )
  if number < 0:
    return False
  UpperCAmelCase	:							List[str]						=			number * number
  while number > 0:
    if number % 10 != number_square % 10:
      return False
    number //= 10
    number_square //= 10
  return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()





