


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)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE       :		int         =				299_792_458

# Symbols
SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Optional[Any]         =				symbols("ct x y z")




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!'''		)

     return velocity / c




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return 1 / sqrt(1 - beta(_a		) ** 2		)




def     UpperCamelCase    (      _a		)					->							np.ndarray:
     '''simple docstring'''





     return np.array(
         [
             [gamma(_a		), -gamma(_a		) * beta(_a		), 0, 0],
             [-gamma(_a		) * beta(_a		), gamma(_a		), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]		)




def     UpperCamelCase    (      _a       ,		_a = None		)					->							np.ndarray:
     '''simple docstring'''





     if event is None:
          lowercase_       :Optional[int]     =       np.array([ct, x, y, z]		)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_a		) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   SCREAMING_SNAKE_CASE       :		List[str]         =				transform(29_979_245)
   print("Example of four vector: ")
   print(f"ct' = {four_vector[0]}")
   print(f"x' = {four_vector[1]}")
   print(f"y' = {four_vector[2]}")
   print(f"z' = {four_vector[3]}")

   # Substitute symbols with numerical values
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{ct: c, x: 1, y: 1, z: 1}
   SCREAMING_SNAKE_CASE       :		str         =				[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f"\n{numerical_vector}")

