





'''simple docstring'''


import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      torch.load(SCREAMING_SNAKE_CASE_		,       map_location="cpu"    )
    if "model" in sd.keys():
        _lowerCAmelCase				=      torch.load(SCREAMING_SNAKE_CASE_		,       map_location="cpu"    )["model"]

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

    _lowerCAmelCase				=      {
        "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				=      sd.pop(SCREAMING_SNAKE_CASE_    )

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

            _lowerCAmelCase				=      key.replace(".qkv_proj."		,       ".q_proj."    )
            _lowerCAmelCase				=      key.replace(".qkv_proj."		,       ".k_proj."    )
            _lowerCAmelCase				=      key.replace(".qkv_proj."		,       ".v_proj."    )

            _lowerCAmelCase				=      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			,			_lowerCAmelCase			,			_lowerCAmelCase				=      torch.split(SCREAMING_SNAKE_CASE_		,       depth // 3		,       dim=0    )

            _lowerCAmelCase				=      q
            _lowerCAmelCase				=      k
            _lowerCAmelCase				=      v
            del sd[key]

    return sd


@torch.no_grad()
def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					List[Any]=None    ):



    '''simple docstring'''





    _lowerCAmelCase				=      load_checkpoint(SCREAMING_SNAKE_CASE_    )

    if config is not None:
        _lowerCAmelCase				=      OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
    else:
        _lowerCAmelCase				=      OPTConfig()

    _lowerCAmelCase				=      OPTModel(SCREAMING_SNAKE_CASE_    ).half().eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_    )

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


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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


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

       testmod()


