



'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase: List[str]							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A    ):
  a						: Tuple					       =					torch.load(UpperCamelCase__			,      map_location='cpu'    )
  if "model" in sd.keys():
    a						: Optional[Any]					       =					torch.load(UpperCamelCase__			,      map_location='cpu'    )['model']

  # pop unnecessary weights
  a						: Any					       =					[
      'decoder.version',
      'decoder.output_projection.weight',
  ]
  for key in keys_to_delete:
    if key in sd:
      sd.pop(UpperCamelCase__    )

  a						: List[str]					       =					{
      'decoder.project_in_dim.weight': 'decoder.project_in.weight',
      'decoder.project_out_dim.weight': 'decoder.project_out.weight',
      'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
      'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
  }
  for old_key, new_key in keys_to_rename.items():
    if old_key in sd:
      a						: int					       =					sd.pop(UpperCamelCase__    )

  a						: List[Any]					       =					list(sd.keys()    )
  for key in keys:
    if ".qkv_proj." in key:
      a						: List[Any]					       =					sd[key]
      # We split QKV in separate Q,K,V

      a						: int					       =					key.replace('.qkv_proj.'			,      '.q_proj.'    )
      a						: Dict					       =					key.replace('.qkv_proj.'			,      '.k_proj.'    )
      a						: str					       =					key.replace('.qkv_proj.'			,      '.v_proj.'    )

      a						: str					       =					value.shape[0]
      assert depth % 3 == 0
      # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
      # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
      a       ,     a       ,     a						: List[Any]					       =					torch.split(UpperCamelCase__			,      depth // 3			,      dim=0    )

      a						: Union[str, Any]					       =					q
      a						: Optional[Any]					       =					k
      a						: List[Any]					       =					v
      del sd[key]

  return sd
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A=None    ):
  a						: List[str]					       =					load_checkpoint(UpperCamelCase__    )

  if config is not None:
    a						: List[Any]					       =					OPTConfig.from_pretrained(UpperCamelCase__    )
  else:
    a						: Tuple					       =					OPTConfig()

  a						: int					       =					OPTModel(UpperCamelCase__    ).half().eval()
  model.load_state_dict(UpperCamelCase__    )

  # Check results
  Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
  model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
     lowerCAmelCase: int							=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--fairseq_path',
         type=str,
         help=(
             'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
             ' https://huggingface.co/models?other=opt_metasq'
         ),
     )
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
     lowerCAmelCase: List[str]							=						parser.parse_args()
     convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




'''simple docstring'''






lowerCAmelCase: Union[str, Any]							=						{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
lowerCAmelCase: Optional[Any]							=						{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 1_2,
    'Pm': 1_5,
    'Em': 1_8,
    'Zm': 2_1,
    'Ym': 2_4,
}
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: Optional[int]					       =					from_type.lower().strip('s'    )
  a						: Optional[Any]					       =					to_type.lower().strip('s'    )

  a						: Dict					       =					UNIT_SYMBOL.get(_A			,      _A    )
  a						: Optional[Any]					       =					UNIT_SYMBOL.get(_A			,      _A    )

  if from_sanitized not in METRIC_CONVERSION:
    a						: Optional[Any]					       =					(
        f"""Invalid 'from_type' value: {from_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  if to_sanitized not in METRIC_CONVERSION:
    a						: Union[str, Any]					       =					(
        f"""Invalid 'to_type' value: {to_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  a						: List[Any]					       =					METRIC_CONVERSION[from_sanitized]
  a						: int					       =					METRIC_CONVERSION[to_sanitized]
  a						: Tuple					       =					1

  if from_exponent > to_exponent:
    a						: Optional[int]					       =					from_exponent - to_exponent
  else:
    a						: Optional[Any]					       =					-(to_exponent - from_exponent)

  return value * pow(10			,      _A    )


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

     testmod()