"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """_float_tensor""",
      """decoder.output_projection.weight""",
  ]
  for k in ignore_keys:
    state_dict.pop(lowercase      ,lowercase						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase       , _UpperCAmelCase         =					emb.weight.shape
  _UpperCAmelCase         =					nn.Linear(lowercase      ,lowercase      ,bias=lowercase						)
  _UpperCAmelCase         =					emb.weight.data
  return lin_layer






def 			__UpperCAmelCase    (	lowercase      ,lowercase="facebook/mbart-large-en-ro"      ,lowercase=False      ,lowercase=False						):







  """simple docstring"""
  _UpperCAmelCase         =					torch.load(lowercase      ,map_location="""cpu"""						)["""model"""]
  remove_ignore_keys_(lowercase						)
  _UpperCAmelCase         =					state_dict["""encoder.embed_tokens.weight"""].shape[0]

  _UpperCAmelCase         =					MBartConfig.from_pretrained(lowercase      ,vocab_size=lowercase						)
  if mbart_aa and finetuned:
    _UpperCAmelCase         =					"""relu"""

  _UpperCAmelCase         =					state_dict["""decoder.embed_tokens.weight"""]
  _UpperCAmelCase         =					MBartForConditionalGeneration(lowercase						)
  model.model.load_state_dict(lowercase						)

  if finetuned:
    _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="""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""")
    UpperCAmelCase__     						=  parser.parse_args()
    UpperCAmelCase__     						=  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)

"""simple docstring"""


UpperCAmelCase__     						=  {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
UpperCAmelCase__     						=  {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 1_2,
    """Pm""": 1_5,
    """Em""": 1_8,
    """Zm""": 2_1,
    """Ym""": 2_4,
}






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					from_type.lower().strip("""s"""						)
  _UpperCAmelCase         =					to_type.lower().strip("""s"""						)

  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)
  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)

  if from_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'from_type\' value: {from_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  if to_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'to_type\' value: {to_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  _UpperCAmelCase         =					METRIC_CONVERSION[from_sanitized]
  _UpperCAmelCase         =					METRIC_CONVERSION[to_sanitized]
  _UpperCAmelCase         =					1

  if from_exponent > to_exponent:
    _UpperCAmelCase         =					from_exponent - to_exponent
  else:
    _UpperCAmelCase         =					-(to_exponent - from_exponent)

  return value * pow(10      ,lowercase						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
