'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  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(_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    emb.weight.shape
       _UpperCAmelCase     :  Optional[int]							      =    nn.Linear(_UpperCAmelCase			,	_UpperCAmelCase			,	bias=_UpperCAmelCase    )
       _UpperCAmelCase     :  List[str]							      =    emb.weight.data
       return lin_layer

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any    )    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )
       _UpperCAmelCase     :  Any							      =    mam_aaa["args"] or mam_aaa["cfg"]["model"]
       _UpperCAmelCase     :  List[Any]							      =    mam_aaa["model"]
       remove_ignore_keys_(_UpperCAmelCase    )
       _UpperCAmelCase     :  int							      =    state_dict["encoder.embed_tokens.weight"].shape[0]

       _UpperCAmelCase     :  Tuple							      =    MaMaaaConfig(
           vocab_size=_UpperCAmelCase			,	max_position_embeddings=1_024			,	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     :  Union[str, Any]							      =    state_dict["decoder.embed_tokens.weight"]
       _UpperCAmelCase     :  Union[str, Any]							      =    MaMaaaForConditionalGeneration(_UpperCAmelCase    )
       model.model.load_state_dict(_UpperCAmelCase			,	strict=_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[Any]							      =    make_linear_from_emb(model.model.shared    )

       return model


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  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.""")
  __SCREAMING_SNAKE_CASE       :     int     =  parser.parse_args()
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      list    )    ->    list:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    len(_UpperCAmelCase    )
       for _ in range(_UpperCAmelCase    ):
              for i in range(_ % 2			,	arr_size - 1			,	2    ):
                     if arr[i + 1] < arr[i]:
                            _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  list(range(10, 0, -1))
  print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')

