






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def   UpperCamelCase  (       snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						[
         """encoder.version""",
         """decoder.version""",
         """model.encoder.version""",
         """model.decoder.version""",
         """decoder.output_projection.weight""",
         """_float_tensor""",
         """encoder.embed_positions._float_tensor""",
         """decoder.embed_positions._float_tensor""",
     ]
     for k in ignore_keys:
          state_dict.pop(snake_case__	,snake_case__  )






def   UpperCamelCase  (       snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case       , __snake_case			:Tuple          =						emb.weight.shape
     __snake_case			:Optional[Any]          =						nn.Linear(snake_case__	,snake_case__	,bias=snake_case__  )
     __snake_case			:str          =						emb.weight.data
     return lin_layer






def   UpperCamelCase  (       snake_case__     :   Optional[int]  ):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						torch.load(snake_case__	,map_location="""cpu"""  )
     __snake_case			:str          =						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
     __snake_case			:str          =						mam_aaa["""model"""]
     remove_ignore_keys_(snake_case__  )
     __snake_case			:int          =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

     __snake_case			:Optional[Any]          =						MaMaaaConfig(
         vocab_size=snake_case__	,max_position_embeddings=1024	,encoder_layers=args.encoder_layers	,decoder_layers=args.decoder_layers	,encoder_attention_heads=args.encoder_attention_heads	,decoder_attention_heads=args.decoder_attention_heads	,encoder_ffn_dim=args.encoder_ffn_embed_dim	,decoder_ffn_dim=args.decoder_ffn_embed_dim	,d_model=args.encoder_embed_dim	,encoder_layerdrop=args.encoder_layerdrop	,decoder_layerdrop=args.decoder_layerdrop	,dropout=args.dropout	,attention_dropout=args.attention_dropout	,activation_dropout=args.activation_dropout	,activation_function="""relu"""	,)

     __snake_case			:Optional[Any]          =						state_dict["""decoder.embed_tokens.weight"""]
     __snake_case			:Optional[Any]          =						MaMaaaForConditionalGeneration(snake_case__  )
     model.model.load_state_dict(snake_case__	,strict=snake_case__  )
     __snake_case			:List[str]          =						make_linear_from_emb(model.model.shared  )

     return model


if __name__ == "__main__":
     lowerCamelCase__							=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""fairseq_path""", type=str, help="""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.""")
     lowerCamelCase__							=     parser.parse_args()
     lowerCamelCase__							=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= "visual_bert"



       def __init__(							self			,						a__=3_05_22			,						a__=7_68			,						a__=5_12			,						a__=12			,						a__=12			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_12			,						a__=2			,						a__=0.02			,						a__=1e-12			,						a__=False			,						a__=True			,						a__=1			,						a__=0			,						a__=2			,						**a__			,						) ->      Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						**a__    )

            __snake_case			:Tuple          =						vocab_size
            __snake_case			:Any          =						max_position_embeddings
            __snake_case			:int          =						hidden_size
            __snake_case			:List[str]          =						visual_embedding_dim
            __snake_case			:List[str]          =						num_hidden_layers
            __snake_case			:Tuple          =						num_attention_heads
            __snake_case			:str          =						intermediate_size
            __snake_case			:Union[str, Any]          =						hidden_act
            __snake_case			:Union[str, Any]          =						hidden_dropout_prob
            __snake_case			:str          =						attention_probs_dropout_prob
            __snake_case			:Tuple          =						initializer_range
            __snake_case			:str          =						type_vocab_size
            __snake_case			:Optional[Any]          =						layer_norm_eps
            __snake_case			:Union[str, Any]          =						bypass_transformer
            __snake_case			:Tuple          =						special_visual_initialize


