



"""simple docstring"""






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def     _lowercase       (						)		->     int:
			__lowerCAmelCase   :						Union[str, Any]				= ArgumentParser(
			    description=(
			        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
			    )			)

			# Optional arguments for the launch helper
			parser.add_argument("--num_cores"      ,type=__snake_case      ,default=1      ,help="Number of TPU cores to use (1 or 8)."			)

			# positional
			parser.add_argument(
			    "training_script"      ,type=__snake_case      ,help=(
			        "The full path to the single TPU training "
			        "program/script to be launched in parallel, "
			        "followed by all the arguments for the "
			        "training script"
			    )      ,)

			# rest from the training program
			parser.add_argument("training_script_args"      ,nargs=__snake_case			)

			return parser.parse_args()


def     _lowercase       (						)		->     str:
			__lowerCAmelCase   :						int				= parse_args()

			# Import training_script as a module.
			__lowerCAmelCase   :						Union[str, Any]				= Path(args.training_script			)
			sys.path.append(str(script_fpath.parent.resolve()			)			)
			__lowerCAmelCase   :						Any				= script_fpath.stem
			__lowerCAmelCase   :						List[Any]				= importlib.import_module(__snake_case			)

			# Patch sys.argv
			__lowerCAmelCase   :						str				= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores			)]

			xmp.spawn(mod._mp_fn      ,args=()      ,nprocs=args.num_cores			)


if __name__ == "__main__":
	main()




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,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      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     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.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)