import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(A ,							A							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      emb.weight.shape
     UpperCamelCase__										=      nn.Linear(A ,							A ,							bias=A							)
     UpperCamelCase__										=      emb.weight.data
     return lin_layer







def  __UpperCamelCase    (						A ,							A="facebook/mbart-large-en-ro" ,							A=False ,							A=False							):
     UpperCamelCase__										=      torch.load(A ,							map_location='''cpu'''							)['''model''']
     remove_ignore_keys_(A							)
     UpperCamelCase__										=      state_dict['''encoder.embed_tokens.weight'''].shape[0]

     UpperCamelCase__										=      MBartConfig.from_pretrained(A ,							vocab_size=A							)
     if mbart_aa and finetuned:
          UpperCamelCase__										=      '''relu'''

     UpperCamelCase__										=      state_dict['''decoder.embed_tokens.weight''']
     UpperCamelCase__										=      MBartForConditionalGeneration(A							)
     model.model.load_state_dict(A							)

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

     return model


if __name__ == "__main__":
     __magic_name__								=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''')
     __magic_name__								=parser.parse_args()
     __magic_name__								=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)




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



