





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)






import re



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""     )
   if match := re.search(_lowerCAmelCase  ,_lowerCAmelCase     ):
      return match.string == phone
   return False


if __name__ == "__main__":
      print(indian_phone_validator("""+918827897895"""))






