


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)

	A								=				chkpt["""model"""]

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

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

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

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

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

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

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


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







import math





def   _lowerCAmelCase				(   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





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



