import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration



def        lowerCamelCase_       (    _lowercase							)				->			List[str]:
		__A				:       List[str]			    = [
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "_float_tensor",
		    "decoder.output_projection.weight",
		]
		for k in ignore_keys:
				state_dict.pop(_lowercase   ,      _lowercase							)



def        lowerCamelCase_       (    _lowercase							)				->			str:
		__A    ,  __A				:       str			    = emb.weight.shape
		__A				:       Union[str, Any]			    = nn.Linear(_lowercase   ,      _lowercase   ,      bias=_lowercase							)
		__A				:       int			    = emb.weight.data
		return lin_layer



def        lowerCamelCase_       (    _lowercase   ,      _lowercase="facebook/mbart-large-en-ro"   ,      _lowercase=False   ,      _lowercase=False							)				->			Optional[int]:
		__A				:       Any			    = torch.load(_lowercase   ,      map_location="cpu"							)["model"]
		remove_ignore_keys_(_lowercase							)
		__A				:       Any			    = state_dict["encoder.embed_tokens.weight"].shape[0]

		__A				:       int			    = MBartConfig.from_pretrained(_lowercase   ,      vocab_size=_lowercase							)
		if mbart_aa and finetuned:
				__A				:       str			    = "relu"

		__A				:       List[Any]			    = state_dict["decoder.embed_tokens.weight"]
		__A				:       Optional[int]			    = MBartForConditionalGeneration(_lowercase							)
		model.model.load_state_dict(_lowercase							)

		if finetuned:
				__A				:       Optional[Any]			    = make_linear_from_emb(model.model.shared							)

		return model


if __name__ == "__main__":
					UpperCamelCase     					=      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')
					UpperCamelCase     					=      parser.parse_args()
					UpperCamelCase     					=      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 argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def _SCREAMING_SNAKE_CASE ()			->			Dict:






       """simple docstring"""







       lowercase__      =					ArgumentParser('''Transformers CLI tool'''    ,					usage='''transformers-cli <command> [<args>]'''			)
       lowercase__      =					parser.add_subparsers(help='''transformers-cli command helpers'''			)

       # Register commands
       ConvertCommand.register_subcommand(A			)
       DownloadCommand.register_subcommand(A			)
       EnvironmentCommand.register_subcommand(A			)
       RunCommand.register_subcommand(A			)
       ServeCommand.register_subcommand(A			)
       UserCommands.register_subcommand(A			)
       AddNewModelCommand.register_subcommand(A			)
       AddNewModelLikeCommand.register_subcommand(A			)
       LfsCommands.register_subcommand(A			)
       PTtoTFCommand.register_subcommand(A			)

       # Let's go
       lowercase__      =					parser.parse_args()

       if not hasattr(A    ,					'''func'''			):
              parser.print_help()
              exit(1			)

       # Run
       lowercase__      =					args.func(A			)
       service.run()


if __name__ == "__main__":
 main()




