from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime







class        UpperCAmelCase_					(				_a     ):




    def __init__(       self	: Dict   ,   *lowerCAmelCase	: List[Any]   ,   **lowerCAmelCase	: Union[str, Any] )						->			Dict:
      super().__init__(*lowerCAmelCase   ,   **lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[str]   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Optional[Any] )						->			List[Any]:
      a__  		=  (
          next_prime(value % self.size_table )
          if not is_prime(value % self.size_table )
          else value % self.size_table
      )  # gt = bigger than
      return next_prime_gt - (data % next_prime_gt)




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: List[Any] )						->			List[str]:
      return (increment * self.__hash_function_a(lowerCAmelCase   ,   lowerCAmelCase )) % self.size_table




    def     __lowerCamelCase			(       self	: List[Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Dict=None )						->			Union[str, Any]:
      a__  		=  1
      a__  		=  self.hash_function(lowerCAmelCase )

      while self.values[new_key] is not None and self.values[new_key] != key:
        a__  		=  (
            self.__hash_double_function(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase )
            if self.balanced_factor() >= self.lim_charge
            else None
        )
        if new_key is None:
          break
        else:
          i += 1

      return new_key


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


_SCREAMING_SNAKE_CASE     =     get_logger(__name__)
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  if text.startswith(lowerCamelCase__       ):
    return text[len(lowerCamelCase__       ) :]
  return text  # or whatever
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  return {remove_prefix(lowerCamelCase__			,					"model."       ): v for k, v in sd.items()}
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}
  for k in state_dicts[0].keys():
    a__  		=  [sd[k] for sd in state_dicts]
    a__  		=  sum(lowerCamelCase__       ) / len(lowerCamelCase__       )
    assert isinstance(lowerCamelCase__			,					torch.Tensor       )
    a__  		=  new_t
  return new_sd
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->None:

  '''simple docstring'''




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

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

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

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


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

