






"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       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 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[Any]             =	emb.weight.shape
  _a  :       List[Any]             =	nn.Linear(__lowerCamelCase     ,   __lowerCamelCase     ,   bias=__lowerCamelCase				)
  _a  :       int             =	emb.weight.data
  return lin_layer

def 			lowerCAmelCase__     (     UpperCamelCase__				):



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

  _a  :       Union[str, Any]             =	MaMaaaConfig(
      vocab_size=__lowerCamelCase     ,   max_position_embeddings=1_0_2_4     ,   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"""     ,   )

  _a  :       str             =	state_dict["decoder.embed_tokens.weight"]
  _a  :       Any             =	MaMaaaForConditionalGeneration(__lowerCamelCase				)
  model.model.load_state_dict(__lowerCamelCase     ,   strict=__lowerCamelCase				)
  _a  :       Dict             =	make_linear_from_emb(model.model.shared				)

  return model


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


