



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





    '''simple docstring'''

    lowerCAmelCase__  :       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(snake_case__ ,snake_case__)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any):





    '''simple docstring'''

    lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  emb.weight.shape
    lowerCAmelCase__  :       List[Any]				=  nn.Linear(snake_case__ ,snake_case__ ,bias=snake_case__)
    lowerCAmelCase__  :       Dict				=  emb.weight.data
    return lin_layer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	int="facebook/mbart-large-en-ro" ,lowerCamelCase_	:	Any=False ,lowerCamelCase_	:	int=False):





    '''simple docstring'''

    lowerCAmelCase__  :       Dict				=  torch.load(snake_case__ ,map_location='''cpu''')['''model''']
    remove_ignore_keys_(snake_case__)
    lowerCAmelCase__  :       List[str]				=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

    lowerCAmelCase__  :       Any				=  MBartConfig.from_pretrained(snake_case__ ,vocab_size=snake_case__)
    if mbart_aa and finetuned:
        lowerCAmelCase__  :       str				=  '''relu'''

    lowerCAmelCase__  :       int				=  state_dict['''decoder.embed_tokens.weight''']
    lowerCAmelCase__  :       Tuple				=  MBartForConditionalGeneration(snake_case__)
    model.model.load_state_dict(snake_case__)

    if finetuned:
        lowerCAmelCase__  :       Tuple				=  make_linear_from_emb(model.model.shared)

    return model


if __name__ == "__main__":
       __snake_case       :	str            =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')
       __snake_case       :	Tuple            =parser.parse_args()
       __snake_case       :	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)






from __future__ import annotations






def 			lowerCAmelCase__	(					lowerCamelCase_	:	list[int]):





				'''simple docstring'''

				return len(set(lowerCamelCase_)) == len(lowerCamelCase_)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

