"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       float    ,							UpperCAmelCase_					:       float    ,							UpperCAmelCase_					:       float    ,							UpperCAmelCase_					:       float    ,							UpperCAmelCase_					:       float    ,							)							->    float:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				[redshift, radiation_density, matter_density, dark_energy]
	if any(p < 0 for p in parameters						):
		raise ValueError('All input parameters must be positive'						)

	if any(p > 1 for p in parameters[1:4]						):
		raise ValueError('Relative densities cannot be greater than one'						)
	else:
		__snake_case    :							Optional[Any]   	=				1 - (matter_density + radiation_density + dark_energy)

		__snake_case    :							str   	=				(
		    radiation_density * (redshift + 1) ** 4
		    + matter_density * (redshift + 1) ** 3
		    + curvature * (redshift + 1) ** 2
		    + dark_energy
		)

		__snake_case    :							str   	=				hubble_constant * e_a ** (1 / 2)
		return hubble


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo LCDM approximation
	_a  :  Tuple=				0.3

	print(
	    hubble_parameter(
	        hubble_constant=6_8.3,
	        radiation_density=1e-4,
	        matter_density=matter_density,
	        dark_energy=1 - matter_density,
	        redshift=0,
	    )
	)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

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

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "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)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

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





