






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _UpperCAmelCase   (	a						:	List[str] ):
							snake_case__	  =       [
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """_float_tensor""",
							    """decoder.output_projection.weight""",
							]
							for k in ignore_keys:
														state_dict.pop(a  ,					a )

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__  , snake_case__	  =       emb.weight.shape
							snake_case__	  =       nn.Linear(a  ,					a  ,					bias=a )
							snake_case__	  =       emb.weight.data
							return lin_layer

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[Any]="facebook/mbart-large-en-ro"  ,					a						:	int=False  ,					a						:	Tuple=False ):
							snake_case__	  =       torch.load(a  ,					map_location="""cpu""" )["""model"""]
							remove_ignore_keys_(a )
							snake_case__	  =       state_dict["""encoder.embed_tokens.weight"""].shape[0]

							snake_case__	  =       MBartConfig.from_pretrained(a  ,					vocab_size=a )
							if mbart_aa and finetuned:
														snake_case__	  =       """relu"""

							snake_case__	  =       state_dict["""decoder.embed_tokens.weight"""]
							snake_case__	  =       MBartForConditionalGeneration(a )
							model.model.load_state_dict(a )

							if finetuned:
														snake_case__	  =       make_linear_from_emb(model.model.shared )

							return model


if __name__ == "__main__":
	a__       =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
	)
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--hf_config""",
	    default="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	a__       =     parser.parse_args()
	a__       =     convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)








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

def _UpperCAmelCase   (	a						:	int ):

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

def _UpperCAmelCase   (	):
							snake_case__	  =       2
							while True:
														if is_prime(a ):
																					yield num
														num += 1

def _UpperCAmelCase   (	a						:	int = 200_0000 ):
							return sum(takewhile(lambda a : x < n  ,					prime_generator() ) )


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