
from math import factorial, radians




def     UpperCamelCase    (      _a       ,		_a = 1_8       ,		_a = 1_0		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

     # Converting from degrees to radians
     lowercase_       :Optional[int]     =       radians(_a		)

     lowercase_       :Optional[int]     =       angle_in_radians
     lowercase_       :Optional[int]     =       3
     lowercase_       :List[str]     =       -1

     for _ in range(_a		):
          result += (b * (angle_in_radians**a)) / factorial(_a		)

          lowercase_       :Union[str, Any]     =       -b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(_a       ,		_a		)


if __name__ == "__main__":
   __import__("doctest").testmod()





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

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

