


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  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(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  snake_case__				,				snake_case__						= emb.weight.shape
  snake_case__						= nn.Linear(__lowerCAmelCase							,   __lowerCAmelCase							,   bias=__lowerCAmelCase				)
  snake_case__						= emb.weight.data
  return lin_layer







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Dict:
  snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  snake_case__						= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
  snake_case__						= mam_aaa['''model''']
  remove_ignore_keys_(__lowerCAmelCase				)
  snake_case__						= state_dict['''encoder.embed_tokens.weight'''].shape[0]

  snake_case__						= 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'''							,   )

  snake_case__						= state_dict['''decoder.embed_tokens.weight''']
  snake_case__						= MaMaaaForConditionalGeneration(__lowerCAmelCase				)
  model.model.load_state_dict(__lowerCAmelCase							,   strict=__lowerCAmelCase				)
  snake_case__						= make_linear_from_emb(model.model.shared				)

  return model


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[int]							     =   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__							:						Optional[int]							     =   parser.parse_args()
   lowerCamelCase__							:						List[str]							     =   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''



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


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






