






'''simple docstring'''

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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """ctrl"""
  lowercase  	=   ["""past_key_values"""]
  lowercase  	=   {
      """max_position_embeddings""": """n_positions""",
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=246534    ,	_SCREAMING_SNAKE_CASE=256    ,	_SCREAMING_SNAKE_CASE=1280    ,	_SCREAMING_SNAKE_CASE=8192    ,	_SCREAMING_SNAKE_CASE=48    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=1e-6    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=True    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      n_positions
        UpperCamelCase       						=      n_embd
        UpperCamelCase       						=      n_layer
        UpperCamelCase       						=      n_head
        UpperCamelCase       						=      dff
        UpperCamelCase       						=      resid_pdrop
        UpperCamelCase       						=      embd_pdrop
        UpperCamelCase       						=      layer_norm_epsilon
        UpperCamelCase       						=      initializer_range

        UpperCamelCase       						=      use_cache

        super().__init__(**_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """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 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		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       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_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.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
