
"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					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(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    emb.weight.shape
   UpperCAmelCase_					:					List[Any]			     =    nn.Linear(__lowerCamelCase,		__lowerCamelCase,		bias=__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    emb.weight.data
   return lin_layer







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    torch.load(__lowerCamelCase,		map_location="cpu"  )
   UpperCAmelCase_					:					Optional[int]			     =    mam_aaa["args"] or mam_aaa["cfg"]["model"]
   UpperCAmelCase_					:					Union[str, Any]			     =    mam_aaa["model"]
   remove_ignore_keys_(__lowerCamelCase  )
   UpperCAmelCase_					:					Dict			     =    state_dict["encoder.embed_tokens.weight"].shape[0]

   UpperCAmelCase_					:					Union[str, Any]			     =    MaMaaaConfig(
       vocab_size=__lowerCamelCase,		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",		)

   UpperCAmelCase_					:					str			     =    state_dict["decoder.embed_tokens.weight"]
   UpperCAmelCase_					:					Any			     =    MaMaaaForConditionalGeneration(__lowerCamelCase  )
   model.model.load_state_dict(__lowerCamelCase,		strict=__lowerCamelCase  )
   UpperCAmelCase_					:					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='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.')
    _a		       =							parser.parse_args()
    _a		       =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


