






'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Any:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= [
       '''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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







   """simple docstring"""
   __snake_case				:      Dict     							= emb.weight.shape
   __snake_case				:      Tuple     							= nn.Linear(lowerCamelCase_  ,		lowerCamelCase_  ,		bias=lowerCamelCase_  )
   __snake_case				:      Tuple     							= emb.weight.data
   return lin_layer




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[Any]="facebook/mbart-large-en-ro"  ,		A  :       str=False  ,		A  :       int=False  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      int     							= torch.load(lowerCamelCase_  ,		map_location='cpu'  )['''model''']
   remove_ignore_keys_(lowerCamelCase_  )
   __snake_case				:      int     							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

   __snake_case				:      str     							= MBartConfig.from_pretrained(lowerCamelCase_  ,		vocab_size=lowerCamelCase_  )
   if mbart_aa and finetuned:
      __snake_case				:      List[Any]     							= '''relu'''

   __snake_case				:      Dict     							= state_dict['''decoder.embed_tokens.weight''']
   __snake_case				:      Any     							= MBartForConditionalGeneration(lowerCamelCase_  )
   model.model.load_state_dict(lowerCamelCase_  )

   if finetuned:
      __snake_case				:      Dict     							= make_linear_from_emb(model.model.shared  )

   return model


if __name__ == "__main__":
     __A    	=   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    	=   parser.parse_args()
     __A    	=   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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias