





'''simple docstring'''




import os

# Precomputes a list of the 100 first triangular numbers
lowercase__   :				Optional[Any]      =   [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 	_lowerCAmelCase      (  )					->					Tuple:
     __A						:			Dict								=	os.path.dirname(os.path.realpath(__snake_case      )      )
     __A						:			Optional[Any]								=	os.path.join(__snake_case			,							'words.txt'      )

     __A						:			Tuple								=	''
     with open(__snake_case      ) as f:
          __A						:			List[Any]								=	f.readline()

     __A						:			Union[str, Any]								=	[word.strip('"'      ) for word in words.strip('\r\n'      ).split(','      )]
     __A						:			Optional[int]								=	[
         word
         for word in [sum(ord(__snake_case      ) - 64 for x in word      ) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case      )


if __name__ == "__main__":
  print(solution())






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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