


from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_lowercase       :      int	=	HfArgumentParser(InitializationArguments)
_lowercase       :      Optional[int]	=	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       :      Dict	=	{
    "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       :      str	=	AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_lowercase       :      int	=	AutoModelForCausalLM.from_config(config)

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







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



