





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__UpperCAmelCase						=      get_logger(__name__)
def 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       str		)	->      List[Any]:
    '''simple docstring'''





    if text.startswith(lowercase__		):
        return text[len(lowercase__		) :]
    return text  # or whatever
def 			__UpperCamelCase				(			lowercase__	:       List[str]		)	->      str:
    '''simple docstring'''





    return {remove_prefix(lowercase__		,    """model."""		): v for k, v in sd.items()}
def 			__UpperCamelCase				(			lowercase__	:       List[Dict[str, torch.Tensor]]		)	->      List[Any]:
    '''simple docstring'''





    lowerCAmelCase_					:	Dict						=   {}
    for k in state_dicts[0].keys():
        lowerCAmelCase_					:	str						=   [sd[k] for sd in state_dicts]
        lowerCAmelCase_					:	List[str]						=   sum(lowercase__		) / len(lowercase__		)
        assert isinstance(lowercase__		,    torch.Tensor		)
        lowerCAmelCase_					:	Union[str, Any]						=   new_t
    return new_sd
def 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       str		,    lowercase__	:       str		)	->      None:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   AutoModelForSeqaSeqLM.from_pretrained(lowercase__		)
    if os.path.isfile(lowercase__		):
        lowerCAmelCase_					:	Dict						=   [pl_ckpt_path]
    else:
        assert os.path.isdir(lowercase__		)
        lowerCAmelCase_					:	int						=   list(Path(lowercase__		).glob("""*.ckpt"""		)		)
        assert ckpt_files, f'could not find any ckpt files inside the {pl_ckpt_path} directory'

    if len(lowercase__		) > 1:
        logger.info(f'averaging the weights of {ckpt_files}'		)

    lowerCAmelCase_					:	str						=   [sanitize(torch.load(lowercase__		,    map_location="""cpu"""		)["""state_dict"""]		) for x in ckpt_files]
    lowerCAmelCase_					:	Dict						=   average_state_dicts(lowercase__		)

    lowerCAmelCase_		,		lowerCAmelCase_					:	Dict						=   hf_model.load_state_dict(lowercase__		,    strict=lowercase__		)
    assert not missing, f'missing keys: {missing}'
    hf_model.save_pretrained(lowercase__		)
    try:
        lowerCAmelCase_					:	List[Any]						=   AutoTokenizer.from_pretrained(lowercase__		)
        tok.save_pretrained(lowercase__		)
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)









from __future__ import annotations

import math
from itertools import permutations
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      bool:
    '''simple docstring'''





    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,    int(math.sqrt(lowercase__		) + 1		)		,    6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True
def 			__UpperCamelCase				(			lowercase__	:       int = 7		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   """""".join(str(lowercase__		) for i in range(1		,    n + 1		)		)
    lowerCAmelCase_					:	Any						=   [int("""""".join(lowercase__		)		) for i in permutations(lowercase__		,    lowercase__		)]
    lowerCAmelCase_					:	List[Any]						=   [num for num in perm_list if is_prime(lowercase__		)]
    return max(lowercase__		) if pandigitals else 0


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


