

"""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						=		logging.get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )
    if "model" in sd.keys():
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )['model']

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

    _lowerCAmelCase       :   Any  =    {
        '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:
            _lowerCAmelCase       :   Tuple  =    sd.pop(_lowerCamelCase   )

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

            _lowerCAmelCase       :   List[Any]  =    key.replace('.qkv_proj.' ,   '.q_proj.'   )
            _lowerCAmelCase       :   Optional[int]  =    key.replace('.qkv_proj.' ,   '.k_proj.'   )
            _lowerCAmelCase       :   Any  =    key.replace('.qkv_proj.' ,   '.v_proj.'   )

            _lowerCAmelCase       :   Optional[Any]  =    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
            _lowerCAmelCase       :   int  =    torch.split(_lowerCamelCase ,   depth // 3 ,   dim=0   )

            _lowerCAmelCase       :   int  =    q
            _lowerCAmelCase       :   List[Any]  =    k
            _lowerCAmelCase       :   Optional[int]  =    v
            del sd[key]

    return sd

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    load_checkpoint(_lowerCamelCase   )

    if config is not None:
        _lowerCAmelCase       :   str  =    OPTConfig.from_pretrained(_lowerCamelCase   )
    else:
        _lowerCAmelCase       :   Tuple  =    OPTConfig()

    _lowerCAmelCase       :   Dict  =    OPTModel(_lowerCamelCase   ).half().eval()
    model.load_state_dict(_lowerCamelCase   )

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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						=		parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""






import logging
import os

from .state import PartialState







class 	__UpperCamelCase    (    logging.LoggerAdapter						):

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,*_A    ,**_A      ):
      '''simple docstring'''

      if PartialState._shared_state == {}:
          raise RuntimeError(
              'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'      )
      _lowerCAmelCase       :   Tuple  =    kwargs.pop('main_process_only'    ,_A      )
      _lowerCAmelCase       :   Any  =    kwargs.pop('in_order'    ,_A      )

      if self.isEnabledFor(_A      ):
          if self._should_log(_A      ):
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
              self.logger.log(_A    ,_A    ,*_A    ,**_A      )

          elif in_order:
              _lowerCAmelCase       :   str  =    PartialState()
              for i in range(state.num_processes      ):
                  if i == state.process_index:
                      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
                      self.logger.log(_A    ,_A    ,*_A    ,**_A      )
                  state.wait_for_everyone()





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = None   ):

    '''simple docstring'''



    if log_level is None:
        _lowerCAmelCase       :   Union[str, Any]  =    os.environ.get('ACCELERATE_LOG_LEVEL' ,   _lowerCamelCase   )
    _lowerCAmelCase       :   int  =    logging.getLogger(_lowerCamelCase   )
    if log_level is not None:
        logger.setLevel(log_level.upper()   )
        logger.root.setLevel(log_level.upper()   )
    return MultiProcessAdapter(_lowerCamelCase ,   {}   )





