

def 	a (     A__      :  str	)       -> bool:





      """simple docstring"""



      if not all(x.isalpha() for x in string	):
            raise ValueError('String must only contain alphabetic characters.'	)

      _lowercase      			=sorted(string.lower()	)
      return len(A__	) == len(set(A__	)	)


if __name__ == "__main__":
  lowercase_   							=  input('Enter a string ').strip()

  lowercase_   							=  is_isogram(input_str)
  print(f"{input_str} is {'an' if isogram else 'not an'} isogram.")





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

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

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


