






"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def _a			(		_snake_case							):







      """simple docstring"""






      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(_snake_case				,	_snake_case							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	emb.weight.shape
      UpperCAmelCase          =	nn.Linear(_snake_case				,	_snake_case				,	bias=_snake_case							)
      UpperCAmelCase          =	emb.weight.data
      return lin_layer

def _a			(		_snake_case							):







      """simple docstring"""






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

      UpperCAmelCase          =	MaMaaaConfig(
          vocab_size=_snake_case				,	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(_snake_case							)
      model.model.load_state_dict(_snake_case				,	strict=_snake_case							)
      UpperCAmelCase          =	make_linear_from_emb(model.model.shared							)

      return model


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











"""simple docstring"""







import numpy as np

def _a			(		_snake_case							):







      """simple docstring"""






      return 1 / (1 + np.exp(-vector							))


if __name__ == "__main__":
  import doctest

  doctest.testmod()



