

'''simple docstring'''
from math import isqrt, loga


def 			_lowerCAmelCase   (		_UpperCamelCase		:	int							) ->     list[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =[True] * max_number
     for i in range(2				,					isqrt(max_number - 1							) + 1							):
          if is_prime[i]:
               for j in range(i**2				,					__lowercase				,					__lowercase							):
                    _SCREAMING_SNAKE_CASE				       =False

     return [i for i in range(2				,					__lowercase							) if is_prime[i]]


def 			_lowerCAmelCase   (		_UpperCamelCase		:	int = 80_08_00				,					_UpperCamelCase		:	int = 80_08_00							) ->     int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =degree * loga(__lowercase							)
     _SCREAMING_SNAKE_CASE				       =int(__lowercase							)
     _SCREAMING_SNAKE_CASE				       =calculate_prime_numbers(__lowercase							)

     _SCREAMING_SNAKE_CASE				       =0
     _SCREAMING_SNAKE_CASE				       =0
     _SCREAMING_SNAKE_CASE				       =len(__lowercase							) - 1
     while left < right:
          while (
              prime_numbers[right] * loga(prime_numbers[left]							)
              + prime_numbers[left] * loga(prime_numbers[right]							)
              > upper_bound
          ):
               right -= 1
          hybrid_integers_count += right - left
          left += 1

     return hybrid_integers_count


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








from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



