


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						=		logging.get_logger(__name__)





@add_end_docstrings(_lowercase)
class  _a							(    _lowercase):




	def __init__( self						:		Union[str, Any]		, *_SCREAMING_SNAKE_CASE						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	List[Any]:
			super().__init__(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			self.check_model_type(_SCREAMING_SNAKE_CASE		)




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




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




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int=False		, _SCREAMING_SNAKE_CASE						:		List[str]=False		)->	Any:
			lowerCAmelCase__       :      Tuple   					= load_image(inputs['''image''']		)
			lowerCAmelCase__       :      Dict   					= self.tokenizer(
			    inputs['''question''']		, return_tensors=self.framework		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= self.image_processor(images=_SCREAMING_SNAKE_CASE		, return_tensors=self.framework		)
			model_inputs.update(_SCREAMING_SNAKE_CASE		)
			return model_inputs




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Any:
			lowerCAmelCase__       :      List[str]   					= self.model(**_SCREAMING_SNAKE_CASE		)
			return model_outputs




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Dict=5		)->	Optional[int]:
			if top_k > self.model.config.num_labels:
					lowerCAmelCase__       :      Any   					= self.model.config.num_labels

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

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








import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

		if n % 2 == 0:
				return n == 2

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

		# n - 1=d*(2**exp)
		lowerCAmelCase__       :      Optional[Any]   					= 0
		while count < prec:
				lowerCAmelCase__       :      Optional[Any]   					= random.randint(2     ,       n - 1	)
				lowerCAmelCase__       :      List[Any]   					= bin_exp_mod(_a     ,       _a     ,       _a	)
				if b != 1:
						lowerCAmelCase__       :      Dict   					= True
						for _ in range(_a	):
								if b == n - 1:
										lowerCAmelCase__       :      Union[str, Any]   					= False
										break
								lowerCAmelCase__       :      Tuple   					= b * b
								b %= n
						if flag:
								return False
						count += 1
		return True


if __name__ == "__main__":
		lowerCamelCase						=		abs(int(input('''Enter bound : ''').strip()))
		print('''Here\'s the list of primes:''')
		print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))




