




"""simple docstring"""







import math





def 							lowercase					(snake_case__   : int )     ->  list[int]:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				lowerCAmelCase  =				2
				lowerCAmelCase  =				int(math.sqrt(snake_case__ ) )  # Size of every segment
				lowerCAmelCase  =				[True] * (end + 1)
				lowerCAmelCase  =				[]

				while start <= end:
								if temp[start] is True:
												in_prime.append(snake_case__ )
												for i in range(start * start      ,       end + 1      ,       snake_case__ ):
																lowerCAmelCase  =				False
								start += 1
				prime += in_prime

				lowerCAmelCase  =				end + 1
				lowerCAmelCase  =				min(2 * end      ,       snake_case__ )

				while low <= n:
								lowerCAmelCase  =				[True] * (high - low + 1)
								for each in in_prime:
												lowerCAmelCase  =				math.floor(low / each ) * each
												if t < low:
																t += each

												for j in range(snake_case__      ,       high + 1      ,       snake_case__ ):
																lowerCAmelCase  =				False

								for j in range(len(snake_case__ ) ):
												if temp[j] is True:
																prime.append(j + low )

								lowerCAmelCase  =				high + 1
								lowerCAmelCase  =				min(high + end      ,       snake_case__ )

				return prime






print(sieve(1_0**6))











"""simple docstring"""







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Union[str, Any] )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )

				lowerCAmelCase  =				chkpt["""model"""]

				# We have the base model one level deeper than the original XLM repository
				lowerCAmelCase  =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												lowerCAmelCase  =				v
								else:
												lowerCAmelCase  =				v

				lowerCAmelCase  =				chkpt["""params"""]
				lowerCAmelCase  =				{n: v for n, v in config.items() if not isinstance(snake_case__      ,       (torch.FloatTensor, numpy.ndarray) )}

				lowerCAmelCase  =				chkpt["""dico_word2id"""]
				lowerCAmelCase  =				{s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@"""      ,       """""" ): i for s, i in vocab.items()}

				# Save pytorch-model
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

				print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
				torch.save(snake_case__      ,       snake_case__ )

				print(f'''Save configuration file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )

				print(f'''Save vocab file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		a      =							parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





