



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    _snake_case (       __snake_case    )     ->     Tuple:






      '''simple docstring'''







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

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

      UpperCAmelCase_  :	Union[str, 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:
                  UpperCAmelCase_  :	Optional[Any]				      =		sd.pop(_lowerCAmelCase    )

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

                  UpperCAmelCase_  :	int				      =		key.replace(".qkv_proj."					,  ".q_proj."    )
                  UpperCAmelCase_  :	List[Any]				      =		key.replace(".qkv_proj."					,  ".k_proj."    )
                  UpperCAmelCase_  :	Dict				      =		key.replace(".qkv_proj."					,  ".v_proj."    )

                  UpperCAmelCase_  :	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
                  UpperCAmelCase_  :	List[str]				      =		torch.split(_lowerCAmelCase					,  depth // 3					,  dim=0    )

                  UpperCAmelCase_  :	str				      =		q
                  UpperCAmelCase_  :	Dict				      =		k
                  UpperCAmelCase_  :	int				      =		v
                  del sd[key]

      return sd







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case=None    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		load_checkpoint(_lowerCAmelCase    )

      if config is not None:
            UpperCAmelCase_  :	str				      =		OPTConfig.from_pretrained(_lowerCAmelCase    )
      else:
            UpperCAmelCase_  :	Any				      =		OPTConfig()

      UpperCAmelCase_  :	Optional[int]				      =		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)





from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase = None     ,lowercase = None     ,lowercase = None     ,lowercase = None     ,lowercase = False     ,lowercase = False     ,lowercase = None     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		path_or_paths
             UpperCAmelCase_  :	Optional[int]				      =		split if split or isinstance(lowercase     ,lowercase) else "train"
             UpperCAmelCase_  :	Optional[int]				      =		features
             UpperCAmelCase_  :	Dict				      =		cache_dir
             UpperCAmelCase_  :	int				      =		keep_in_memory
             UpperCAmelCase_  :	Tuple				      =		streaming
             UpperCAmelCase_  :	Union[str, Any]				      =		num_proc
             UpperCAmelCase_  :	Union[str, Any]				      =		kwargs



       @abstractmethod
       def  A_   (     self):

             """simple docstring"""





             pass


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __init__(     self     ,lowercase = None     ,lowercase = None     ,lowercase = False     ,lowercase = False     ,lowercase = None     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		features
             UpperCAmelCase_  :	List[Any]				      =		cache_dir
             UpperCAmelCase_  :	List[str]				      =		keep_in_memory
             UpperCAmelCase_  :	Optional[int]				      =		streaming
             UpperCAmelCase_  :	Dict				      =		num_proc
             UpperCAmelCase_  :	str				      =		kwargs



       @abstractmethod
       def  A_   (     self):

             """simple docstring"""





             pass
