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)




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()



