import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					Union[str, 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(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      emb.weight.shape
  SCREAMING_SNAKE_CASE__										=      nn.Linear(__UpperCamelCase ,     __UpperCamelCase ,     bias=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      emb.weight.data
  return lin_layer



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any]	)							->					Any:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__										=      MaMaaaConfig(
      vocab_size=__UpperCamelCase ,     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(__UpperCamelCase	)
  model.model.load_state_dict(__UpperCamelCase ,     strict=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      make_linear_from_emb(model.model.shared	)

  return model


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

from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
