


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_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(lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       emb.weight.shape
					__SCREAMING_SNAKE_CASE		     =       nn.Linear(lowerCAmelCase_						,		lowerCAmelCase_						,		bias=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       emb.weight.data
					return lin_layer




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_="facebook/mbart-large-en-ro"						,		lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


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

					__SCREAMING_SNAKE_CASE		     =       MBartConfig.from_pretrained(lowerCAmelCase_						,		vocab_size=lowerCAmelCase_     )
					if mbart_aa and finetuned:
										__SCREAMING_SNAKE_CASE		     =       "relu"

					__SCREAMING_SNAKE_CASE		     =       state_dict["decoder.embed_tokens.weight"]
					__SCREAMING_SNAKE_CASE		     =       MBartForConditionalGeneration(lowerCAmelCase_     )
					model.model.load_state_dict(lowerCAmelCase_     )

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

					return model


if __name__ == "__main__":
		a__       :							Any        =  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__       :							Optional[Any]        =  parser.parse_args()
		a__       :							Optional[int]        =  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)




"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						raise NotImplementedError()




	@abstractmethod
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						raise NotImplementedError()
