

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]      )			->      List[Any]:




				'''simple docstring'''





				A__        =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__snake_case	,       __snake_case      )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			emb.weight.shape
				A__        =			nn.Linear(__snake_case	,       __snake_case	,       bias=__snake_case      )
				A__        =			emb.weight.data
				return lin_layer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str="facebook/mbart-large-en-ro"	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]=False	,       SCREAMING_SNAKE_CASE_:							Any=False      )			->      List[Any]:




				'''simple docstring'''





				A__        =			torch.load(__snake_case	,       map_location="cpu"      )["model"]
				remove_ignore_keys_(__snake_case      )
				A__        =			state_dict["encoder.embed_tokens.weight"].shape[0]

				A__        =			MBartConfig.from_pretrained(__snake_case	,       vocab_size=__snake_case      )
				if mbart_aa and finetuned:
								A__        =			"relu"

				A__        =			state_dict["decoder.embed_tokens.weight"]
				A__        =			MBartForConditionalGeneration(__snake_case      )
				model.model.load_state_dict(__snake_case      )

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

				return model


if __name__ == "__main__":
	lowerCAmelCase__	   = 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""")
	lowerCAmelCase__	   = parser.parse_args()
	lowerCAmelCase__	   = 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)




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

