"""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   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    List[str]  )							->      str:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			torch.load(lowercase_      ,							map_location='cpu'  )

	__snake_case      :							Optional[int]						=			chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	__snake_case      :							List[Any]						=			{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			__snake_case      :							List[str]						=			v
		else:
			__snake_case      :							int						=			v

	__snake_case      :							List[str]						=			chkpt['params']
	__snake_case      :							Tuple						=			{n: v for n, v in config.items() if not isinstance(lowercase_      ,							(torch.FloatTensor, numpy.ndarray)  )}

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

	# Save pytorch-model
	__snake_case      :							Any						=			pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	__snake_case      :							Tuple						=			pytorch_dump_folder_path + '/' + CONFIG_NAME
	__snake_case      :							Optional[int]						=			pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(F"Save PyTorch model to {pytorch_weights_dump_path}"  )
	torch.save(lowercase_      ,							lowercase_  )

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

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


if __name__ == "__main__":
	_a					:					str=			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					:					str=			parser.parse_args()
	convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""







from __future__ import annotations

import math
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      bool:

	'''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(UpperCAmelCase_  ) + 1  )      ,							6  ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True


_a					:					Any=			[num for num in range(3, 100_001, 2) if not is_prime(num)]
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      list[int]:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ):
		raise ValueError('n must be an integer'  )
	if n <= 0:
		raise ValueError('n must be >= 0'  )

	__snake_case      :							int						=			[]
	for num in range(len(UpperCAmelCase_  )  ):
		__snake_case      :							List[str]						=			0
		while 2 * i * i <= odd_composites[num]:
			__snake_case      :							List[Any]						=			odd_composites[num] - 2 * i * i
			if is_prime(UpperCAmelCase_  ):
				break
			i += 1
		else:
			list_nums.append(odd_composites[num]  )
			if len(UpperCAmelCase_  ) == n:
				return list_nums

	return []
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	return compute_nums(1  )[0]


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





