

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 	=   get_logger(__name__)




def      UpperCAmelCase__							(     _A :					str      , _A :					str				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					List[Dict[str, torch.Tensor]]				):





  '''simple docstring'''







  a__    ={}
  for k in state_dicts[0].keys():
    a__    =[sd[k] for sd in state_dicts]
    a__    =sum(__UpperCamelCase				) / len(__UpperCamelCase				)
    assert isinstance(__UpperCamelCase      , torch.Tensor				)
    a__    =new_t
  return new_sd




def      UpperCAmelCase__							(     _A :					str      , _A :					str      , _A :					str				):





  '''simple docstring'''







  a__    =AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase				)
  if os.path.isfile(__UpperCamelCase				):
    a__    =[pl_ckpt_path]
  else:
    assert os.path.isdir(__UpperCamelCase				)
    a__    =list(Path(__UpperCamelCase				).glob('''*.ckpt'''				)				)
    assert ckpt_files, F"""could not find any ckpt files inside the {pl_ckpt_path} directory"""

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

  a__    =[sanitize(torch.load(__UpperCamelCase      , map_location='''cpu'''				)['''state_dict''']				) for x in ckpt_files]
  a__    =average_state_dicts(__UpperCamelCase				)

  a__, a__    =hf_model.load_state_dict(__UpperCamelCase      , strict=__UpperCamelCase				)
  assert not missing, F"""missing keys: {missing}"""
  hf_model.save_pretrained(__UpperCamelCase				)
  try:
    a__    =AutoTokenizer.from_pretrained(__UpperCamelCase				)
    tok.save_pretrained(__UpperCamelCase				)
  except Exception:
    pass
    # dont copy tokenizer if cant


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

"""simple docstring"""

from collections import defaultdict


def    lowerCAmelCase			(__UpperCamelCase							:    int = 5			):
							"""simple docstring"""


							__UpperCamelCase							=defaultdict(__UpperCamelCase			)
							__UpperCamelCase							=0

							while True:
														__UpperCamelCase							=get_digits(__UpperCamelCase			)
														freqs[digits].append(__UpperCamelCase			)

														if len(freqs[digits]			) == max_base:
																					__UpperCamelCase							=freqs[digits][0] ** 3
																					return base

														num += 1


def    lowerCAmelCase			(__UpperCamelCase							:    int			):
							"""simple docstring"""


							return "".join(sorted(str(num**3			)			)			)


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



