code: 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     :   Dict					=				get_logger(__name__)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	Union[str, Any]				):
         if text.startswith(lowercase_				):
                return text[len(lowercase_				) :]
         return text  # or whatever
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]				):
         return {remove_prefix(lowercase_		,						'model.'				): v for k, v in sd.items()}
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
         _UpperCamelCase     :       Dict  						=  {}
         for k in state_dicts[0].keys():
                _UpperCamelCase     :       Dict  						=  [sd[k] for sd in state_dicts]
                _UpperCamelCase     :       Tuple  						=  sum(lowercase_				) / len(lowercase_				)
                assert isinstance(lowercase_		,						torch.Tensor				)
                _UpperCamelCase     :       List[str]  						=  new_t
         return new_sd
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[str]		,						lowercase_:	Optional[int]				):
         _UpperCamelCase     :       int  						=  AutoModelForSeqaSeqLM.from_pretrained(lowercase_				)
         if os.path.isfile(lowercase_				):
                _UpperCamelCase     :       Any  						=  [pl_ckpt_path]
         else:
                assert os.path.isdir(lowercase_				)
                _UpperCamelCase     :       List[Any]  						=  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}"				)
  
         _UpperCamelCase     :       List[Any]  						=  [sanitize(torch.load(lowercase_		,						map_location='cpu'				)['state_dict']				) for x in ckpt_files]
         _UpperCamelCase     :       List[Any]  						=  average_state_dicts(lowercase_				)
  
         _UpperCamelCase     :       int  						=  hf_model.load_state_dict(lowercase_		,						strict=lowercase_				)
         assert not missing, F"missing keys: {missing}"
         hf_model.save_pretrained(lowercase_				)
         try:
                _UpperCamelCase     :       str  						=  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)
  
  
  
  
style_context: import math
  from collections.abc import Generator
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                # only need to check for factors up to sqrt(i)
                _UpperCamelCase     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)		,						2				))
         # It's useless to test even numbers as they will not be prime
         if max_n > 2:
                yield 2  # Because 2 will not be tested, it's necessary to yield it now
         for i in (n for n in numbers if n > 1):
                _UpperCamelCase     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(3		,						lowercase_		,						2				):
                       # As we removed the even numbers, we don't need them now
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         from timeit import timeit
  
         _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
         print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
   for ret in primes(number):
    print(ret)
   benchmark()
  
  
  
  