
"""simple docstring"""
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)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				int:
      return int((input_a, input_a).count(0							) == 0							)



def 					__lowerCamelCase       (							)      ->				None:
      assert and_gate(0			,       0							) == 0
      assert and_gate(0			,       1							) == 0
      assert and_gate(1			,       0							) == 0
      assert and_gate(1			,       1							) == 1


if __name__ == "__main__":
  test_and_gate()
  print(and_gate(1, 0))
  print(and_gate(0, 0))
  print(and_gate(0, 1))
  print(and_gate(1, 1))