




'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def 						snake_case_							(_a	:							List[Any]     ):
    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 						snake_case_							(_a	:							Tuple     ):
    UpperCAmelCase       ,     UpperCAmelCase			       =		emb.weight.shape
    UpperCAmelCase			       =		nn.Linear(_a    ,   _a    ,   bias=_a     )
    UpperCAmelCase			       =		emb.weight.data
    return lin_layer





def 						snake_case_							(_a	:							Optional[int]    ,   _a	:							Optional[int]="facebook/mbart-large-en-ro"    ,   _a	:							Optional[Any]=False    ,   _a	:							Any=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__":
   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


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Union[str, Any]  						=	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							"""megatron-bert"""



      def __init__(self    :    Tuple   ,						UpperCamelCase    :    Optional[int]=29056   ,						UpperCamelCase    :    Optional[Any]=1024   ,						UpperCamelCase    :    Any=24   ,						UpperCamelCase    :    int=16   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    int="gelu"   ,						UpperCamelCase    :    int=0.1   ,						UpperCamelCase    :    Tuple=0.1   ,						UpperCamelCase    :    Any=512   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Dict=0.02   ,						UpperCamelCase    :    Dict=1E-12   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    Optional[int]="absolute"   ,						UpperCamelCase    :    List[Any]=True   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             super().__init__(pad_token_id=UpperCamelCase   ,						**UpperCamelCase )

             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= hidden_act
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= max_position_embeddings
             lowercase__      			= type_vocab_size
             lowercase__      			= initializer_range
             lowercase__      			= layer_norm_eps
             lowercase__      			= position_embedding_type
             lowercase__      			= use_cache




