





'''simple docstring'''




from collections.abc import Callable

import numpy as np






def 	_lowerCAmelCase      (  __snake_case  :			Callable			,							__snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float			,							__snake_case  :			float      )					->					np.array:
     __A						:			List[str]								=	int(np.ceil((x_end - xa) / step_size      )      )
     __A						:			Dict								=	np.zeros((n + 1,)      )
     __A						:			List[Any]								=	ya
     __A						:			List[Any]								=	xa

     for k in range(__snake_case      ):
          __A						:			Optional[Any]								=	y[k] + step_size * ode_func(__snake_case			,							y[k]      )
          __A						:			Optional[int]								=	y[k] + (
              (step_size / 2) * (ode_func(__snake_case			,							y[k]      ) + ode_func(x + step_size			,							__snake_case      ))
          )
          x += step_size

     return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''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)