
"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __A						(   a_ :							List[Any]     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= [
		    '''decoder.version''',
		    '''decoder.output_projection.weight''',
		    '''_float_tensor''',
		    '''decoder.embed_positions._float_tensor''',
		]
		for k in ignore_keys:
				state_dict.pop(a_		,		a_     )





def __A						(   a_ :							Optional[Any]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= emb.weight.shape
		SCREAMING_SNAKE_CASE       :     int				= nn.Linear(a_		,		a_		,		bias=a_     )
		SCREAMING_SNAKE_CASE       :     Any				= emb.weight.data
		return lin_layer





def __A						(   a_ :							List[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.load(a_		,		map_location='''cpu'''     )
		SCREAMING_SNAKE_CASE       :     Any				= Namespace(**checkpoint['''cfg''']['''model''']     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= checkpoint['''model''']
		remove_ignore_keys_(a_     )
		SCREAMING_SNAKE_CASE       :     Tuple				= state_dict['''decoder.embed_tokens.weight'''].shape[0]

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {key.replace('''decoder'''		,		'''model'''     ): val for key, val in state_dict.items()}

		SCREAMING_SNAKE_CASE       :     Tuple				= XGLMConfig(
		    vocab_size=a_		,		max_position_embeddings=args.max_target_positions		,		num_layers=args.decoder_layers		,		attention_heads=args.decoder_attention_heads		,		ffn_dim=args.decoder_ffn_embed_dim		,		d_model=args.decoder_embed_dim		,		layerdrop=args.decoder_layerdrop		,		dropout=args.dropout		,		attention_dropout=args.attention_dropout		,		activation_dropout=args.activation_dropout		,		activation_function='''gelu'''		,		scale_embedding=not args.no_scale_embedding		,		tie_word_embeddings=args.share_decoder_input_output_embed		,		)

		SCREAMING_SNAKE_CASE       :     Optional[int]				= XGLMForCausalLM(a_     )
		SCREAMING_SNAKE_CASE       :     str				= model.load_state_dict(a_		,		strict=a_     )
		print(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= make_linear_from_emb(model.model.embed_tokens     )

		return model


if __name__ == "__main__":
					lowerCamelCase__ :       str						     =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("fairseq_path", type=str, help="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.")
					lowerCamelCase__ :       Optional[int]						     =			parser.parse_args()
					lowerCamelCase__ :       int						     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""






def __A						(   a_ :							int     )->							list[int]:




		'''simple docstring'''






		if num <= 0:
				raise ValueError('''Input must be a positive integer'''     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= [True] * (num + 1)

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2
		while p * p <= num:
				if primes[p]:
						for i in range(p * p		,		num + 1		,		a_     ):
								SCREAMING_SNAKE_CASE       :     Any				= False
				p += 1

		return [prime for prime in range(2		,		num + 1     ) if primes[prime]]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCamelCase__ :       str						     =			int(input("Enter a positive integer: ").strip())
					print(prime_sieve_eratosthenes(user_num))





