


def 		lowerCAmelCase_   (	lowerCamelCase = 2000000   ):
				__magic_name__	:	Optional[int]								=[0 for i in range(n + 1   )]
				__magic_name__	:	Optional[int]								=1
				__magic_name__	:	Optional[Any]								=1

				for i in range(2	,     int(n**0.5   ) + 1   ):
								if primality_list[i] == 0:
												for j in range(i * i	,     n + 1	,     lowerCamelCase   ):
																__magic_name__	:	str								=1
				__magic_name__	:	List[str]								=0
				for i in range(lowerCamelCase   ):
								if primality_list[i] == 0:
												sum_of_primes += i
				return sum_of_primes


if __name__ == "__main__":
							print(F"""{solution() = }""")










from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__snake_case )
										requires_backends(self    , """vision""" )
										self.check_model_type(
										    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
										    if self.framework == """tf"""
										    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )

						def     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return super().__call__(__snake_case    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




