





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Any:





  if gpta_config_file == "":
    lowercase__           =  GPTaConfig()
  else:
    lowercase__           =  GPTaConfig.from_json_file(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  GPTaModel(_SCREAMING_SNAKE_CASE		)

  # Load weights from numpy
  load_tf_weights_in_gpta(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  # Save pytorch-model
  lowercase__           =  pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  lowercase__           =  pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""		)
  torch.save(model.state_dict()		,						_SCREAMING_SNAKE_CASE		)
  print(F"""Save configuration file to {pytorch_config_dump_path}"""		)
  with open(_SCREAMING_SNAKE_CASE		,						'w'		,						encoding='utf-8'		) as f:
    f.write(config.to_json_string()		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--gpt2_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
              """This specifies the model architecture."""
          ),
      )
      lowercase_          =		parser.parse_args()
      convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)












import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  if finetuned:
    lowercase__           =  make_linear_from_emb(model.model.shared		)

  return model


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a 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.""")
      parser.add_argument(
          """--hf_config""",
          default="""facebook/mbart-large-cc25""",
          type=str,
          help="""Which huggingface architecture to use: mbart-large""",
      )
      parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
      parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
      lowercase_          =		parser.parse_args()
      lowercase_          =		convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)


