




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

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

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












from bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






