





'''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'''


_SCREAMING_SNAKE_CASE	 =							range(2, 20 + 1)
_SCREAMING_SNAKE_CASE	 =							[10**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE	 =							{}


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					List[str]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(a_i[j] for j in range(SCREAMING_SNAKE_CASE_		,       len(SCREAMING_SNAKE_CASE_    )    )    )
    _lowerCAmelCase				=      sum(a_i[j] * base[j] for j in range(min(len(SCREAMING_SNAKE_CASE_    )		,       SCREAMING_SNAKE_CASE_    )    )    )

    _lowerCAmelCase			,			_lowerCAmelCase				=      0, 0
    _lowerCAmelCase				=      n - i

    _lowerCAmelCase				=      memo.get(SCREAMING_SNAKE_CASE_    )

    if sub_memo is not None:
        _lowerCAmelCase				=      sub_memo.get(SCREAMING_SNAKE_CASE_    )

        if jumps is not None and len(SCREAMING_SNAKE_CASE_    ) > 0:
            # find and make the largest jump without going over
            _lowerCAmelCase				=      -1
            for _k in range(len(SCREAMING_SNAKE_CASE_    ) - 1		,       -1		,       -1    ):
                if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                    _lowerCAmelCase				=      _k
                    break

            if max_jump >= 0:
                _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      jumps[max_jump]
                # since the difference between jumps is cached, add c
                _lowerCAmelCase				=      diff + c
                for j in range(min(SCREAMING_SNAKE_CASE_		,       len(SCREAMING_SNAKE_CASE_    )    )    ):
                    _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(SCREAMING_SNAKE_CASE_		,       10    )
                if new_c > 0:
                    add(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )

        else:
            _lowerCAmelCase				=      []
    else:
        _lowerCAmelCase				=      {c: []}
        _lowerCAmelCase				=      sub_memo

    if dn >= max_dn or c + diff >= base[k]:
        return diff, dn

    if k > ks[0]:
        while True:
            # keep doing smaller jumps
            _lowerCAmelCase			,			_lowerCAmelCase				=      next_term(SCREAMING_SNAKE_CASE_		,       k - 1		,       i + dn		,       SCREAMING_SNAKE_CASE_    )
            diff += _diff
            dn += terms_jumped

            if dn >= max_dn or c + diff >= base[k]:
                break
    else:
        # would be too small a jump, just compute sequential terms instead
        _lowerCAmelCase			,			_lowerCAmelCase				=      compute(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       i + dn		,       SCREAMING_SNAKE_CASE_    )
        diff += _diff
        dn += terms_jumped

    _lowerCAmelCase				=      sub_memo[c]

    # keep jumps sorted by # of terms skipped
    _lowerCAmelCase				=      0
    while j < len(SCREAMING_SNAKE_CASE_    ):
        if jumps[j][1] > dn:
            break
        j += 1

    # cache the jump for this value digitsum(b) and c
    sub_memo[c].insert(SCREAMING_SNAKE_CASE_		,       (diff, dn, k)    )
    return (diff, dn)


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    if i >= n:
        return 0, i
    if k > len(SCREAMING_SNAKE_CASE_    ):
        a_i.extend([0 for _ in range(k - len(SCREAMING_SNAKE_CASE_    )    )]    )

    # note: a_i -> b * 10^k + c
    # ds_b -> digitsum(b)
    # ds_c -> digitsum(c)
    _lowerCAmelCase				=      i
    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      0, 0, 0
    for j in range(len(SCREAMING_SNAKE_CASE_    )    ):
        if j >= k:
            ds_b += a_i[j]
        else:
            ds_c += a_i[j]

    while i < n:
        i += 1
        _lowerCAmelCase				=      ds_c + ds_b
        diff += addend
        _lowerCAmelCase				=      0
        for j in range(SCREAMING_SNAKE_CASE_    ):
            _lowerCAmelCase				=      a_i[j] + addend
            _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(SCREAMING_SNAKE_CASE_		,       10    )

            ds_c += a_i[j]

        if addend > 0:
            break

    if addend > 0:
        add(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return diff, i - start_i


def 	__a(SCREAMING_SNAKE_CASE_  :					List[Any]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





    for j in range(SCREAMING_SNAKE_CASE_		,       len(SCREAMING_SNAKE_CASE_    )    ):
        _lowerCAmelCase				=      digits[j] + addend
        if s >= 10:
            _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(SCREAMING_SNAKE_CASE_		,       10    )
            _lowerCAmelCase				=      addend // 10 + quotient
        else:
            _lowerCAmelCase				=      s
            _lowerCAmelCase				=      addend // 10

        if addend == 0:
            break

    while addend > 0:
        _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(SCREAMING_SNAKE_CASE_		,       10    )
        digits.append(SCREAMING_SNAKE_CASE_    )


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 10**15    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [1]
    _lowerCAmelCase				=      1
    _lowerCAmelCase				=      0
    while True:
        _lowerCAmelCase			,			_lowerCAmelCase				=      next_term(SCREAMING_SNAKE_CASE_		,       20		,       i + dn		,       SCREAMING_SNAKE_CASE_    )
        dn += terms_jumped
        if dn == n - i:
            break

    _lowerCAmelCase				=      0
    for j in range(len(SCREAMING_SNAKE_CASE_    )    ):
        a_n += digits[j] * 10**j
    return a_n


if __name__ == "__main__":
       print(f'''{solution() = }''')


