

"""simple docstring"""




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


lowerCAmelCase       :				int         =					get_logger(__name__)

def 				A_					(							a   ,				a					):

      """simple docstring"""



      if text.startswith(a					):
            return text[len(a					) :]
      return text  # or whatever

def 				A_					(							a					):

      """simple docstring"""



      return {remove_prefix(a   ,				'model.'					): v for k, v in sd.items()}

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_			:					str          =       [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_			:					List[str]          =       sum(a					) / len(a					)
            assert isinstance(a   ,				torch.Tensor					)
            SCREAMING_SNAKE_CASE_			:					str          =       new_t
      return new_sd

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



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

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

      SCREAMING_SNAKE_CASE_			:					Dict          =       [sanitize(torch.load(a   ,				map_location='cpu'					)['state_dict']					) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       average_state_dicts(a					)

      SCREAMING_SNAKE_CASE_			:					int          =       hf_model.load_state_dict(a   ,				strict=a					)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(a					)
      try:
            SCREAMING_SNAKE_CASE_			:					Dict          =       AutoTokenizer.from_pretrained(a					)
            tok.save_pretrained(a					)
      except Exception:
            pass
            # dont copy tokenizer if cant


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









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






