
'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join([hex(SCREAMING_SNAKE_CASE_						)[2:].zfill(2						).upper() for byte in list(SCREAMING_SNAKE_CASE_						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bytes:




   """simple docstring"""
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(SCREAMING_SNAKE_CASE_						) % 2) != 0:
      raise ValueError(
          """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""						)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(SCREAMING_SNAKE_CASE_						) <= set("""0123456789ABCDEF"""						):
      raise ValueError(
          """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""						)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]    ,      16						) for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      2						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""model"""]
   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				state_dict["""encoder.embed_tokens.weight"""].shape[0]

   _SCREAMING_SNAKE_CASE      =				MaMaaaConfig(
       vocab_size=SCREAMING_SNAKE_CASE_    ,      max_position_embeddings=10_24    ,      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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				state_dict["""decoder.embed_tokens.weight"""]
   _SCREAMING_SNAKE_CASE      =				MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  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.")
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






