import math
from collections.abc import Iterator
from itertools import takewhile







def      _a					(   lowercase__	:      int			):







     '''simple docstring'''


     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5  ,	int(math.sqrt(lowercase__			) + 1			)  ,	6			):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Dict      =		2
     while True:
          if is_prime(lowercase__			):
               yield num
          num += 1







def      _a					(   lowercase__	:      int = 2_00_00_00			):







     '''simple docstring'''


     return sum(takewhile(lambda lowercase__			: x < n  ,	prime_generator()			)			)


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






"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
							import onnxruntime as ort


__UpperCamelCase    : int				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[Any]				 =						{
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Optional[Any]  ,lowercase_     :							Union[str, Any]=None  ,**lowercase_     :							List[str]						):
						logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''						)
						lowerCAmelCase__      :		List[str]    							=      model
						lowerCAmelCase__      :		Optional[Any]    							=      kwargs.get('''model_save_dir'''  ,lowercase_						)
						lowerCAmelCase__      :		str    							=      kwargs.get('''latest_model_name'''  ,lowercase_						)







					def __call__(			self     :							Any  ,**lowercase_     :							Dict						):
						lowerCAmelCase__      :		List[Any]    							=      {k: np.array(lowercase_						) for k, v in kwargs.items()}
						return self.model.run(lowercase_  ,lowercase_						)







					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							Union[str, Path]  ,lowercase_     :							str=None  ,lowercase_     :							str=None						):
						if provider is None:
							logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''						)
							lowerCAmelCase__      :		Optional[Any]    							=      '''CPUExecutionProvider'''

						return ort.InferenceSession(lowercase_  ,providers=[provider]  ,sess_options=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							str						):
						lowerCAmelCase__      :		Any    							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

						lowerCAmelCase__      :		Tuple    							=      self.model_save_dir.joinpath(self.latest_model_name						)
						lowerCAmelCase__      :		List[Any]    							=      Path(lowercase_						).joinpath(lowercase_						)
						try:
							shutil.copyfile(lowercase_  ,lowercase_						)
						except shutil.SameFileError:
							pass

						# copy external weights (for models >2GB)
						lowerCAmelCase__      :		Dict    							=      self.model_save_dir.joinpath(lowercase_						)
						if src_path.exists():
							lowerCAmelCase__      :		Union[str, Any]    							=      Path(lowercase_						).joinpath(lowercase_						)
							try:
								shutil.copyfile(lowercase_  ,lowercase_						)
							except shutil.SameFileError:
								pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							Union[str, os.PathLike]  ,**lowercase_     :							Union[str, Any]  ,):
						if os.path.isfile(lowercase_						):
							logger.error(F'Provided path ({save_directory}) should be a directory, not a file'						)
							return

						os.makedirs(lowercase_  ,exist_ok=lowercase_						)

						# saving model weights/files
						self._save_pretrained(lowercase_  ,**lowercase_						)







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							Dict  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							Optional[Union[bool, str, None]] = None  ,lowercase_     :							Optional[Union[str, None]] = None  ,lowercase_     :							bool = False  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional["ort.SessionOptions"] = None  ,**lowercase_     :							Optional[int]  ,):
						lowerCAmelCase__      :		Optional[Any]    							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase_						):
							lowerCAmelCase__      :		str    							=      OnnxRuntimeModel.load_model(
							    os.path.join(lowercase_  ,lowercase_						)  ,provider=lowercase_  ,sess_options=lowercase_						)
							lowerCAmelCase__      :		Any    							=      Path(lowercase_						)
						# load model from hub
						else:
							# download model
							lowerCAmelCase__      :		Optional[Any]    							=      hf_hub_download(
							    repo_id=lowercase_  ,filename=lowercase_  ,use_auth_token=lowercase_  ,revision=lowercase_  ,cache_dir=lowercase_  ,force_download=lowercase_  ,)
							lowerCAmelCase__      :		str    							=      Path(lowercase_						).parent
							lowerCAmelCase__      :		Union[str, Any]    							=      Path(lowercase_						).name
							lowerCAmelCase__      :		Optional[Any]    							=      OnnxRuntimeModel.load_model(lowercase_  ,provider=lowercase_  ,sess_options=lowercase_						)
						return cls(model=lowercase_  ,**lowercase_						)







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							Any  ,lowercase_     :							Union[str, Path]  ,lowercase_     :							bool = True  ,lowercase_     :							Optional[str] = None  ,lowercase_     :							Optional[str] = None  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Union[str, Any]    							=      None
						if len(str(lowercase_						).split('''@'''						)						) == 2:
							lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      model_id.split('''@'''						)

						return cls._from_pretrained(
						    model_id=lowercase_  ,revision=lowercase_  ,cache_dir=lowercase_  ,force_download=lowercase_  ,use_auth_token=lowercase_  ,**lowercase_  ,)




