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)


from __future__ import annotations

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")

