



"""simple docstring"""





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase  			=		HfArgumentParser(InitializationArguments)
lowerCamelCase  			=		parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase  			=		AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase  			=		{
    '''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)
lowerCamelCase  			=		AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase  			=		AutoModelForCausalLM.from_config(config)

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











from math import factorial



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:

		# If either of the conditions are true, the function is being asked
		# to calculate a factorial of a negative number, which is not possible
		if n < k or k < 0:
				raise ValueError('Please enter positive integers for n and k where n >= k'							)
		return factorial(snake_case							) // (factorial(snake_case							) * factorial(n - k							))


if __name__ == "__main__":
		print(
		    '''The number of five-card hands possible from a standard''',
		    F"""fifty-two card deck is: {combinations(52, 5)}\n""",
		)

		print(
		    '''If a class of 40 students must be arranged into groups of''',
		    F"""4 for group projects, there are {combinations(40, 4)} ways""",
		    '''to arrange them.\n''',
		)

		print(
		    '''If 10 teams are competing in a Formula One race, there''',
		    F"""are {combinations(10, 3)} ways that first, second and""",
		    '''third place can be awarded.''',
		)




