



import random
from typing import Any






def      lowerCAmelCase__		(   lowerCamelCase_		:		list):





				'''simple docstring'''

				for _ in range(len(lowerCamelCase_)):
								lowerCAmelCase__					:    Optional[Any]												=					random.randint(0	,len(lowerCamelCase_) - 1)
								lowerCAmelCase__					:    Union[str, Any]												=					random.randint(0	,len(lowerCamelCase_) - 1)
								lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					data[b], data[a]
				return data


if __name__ == "__main__":
							__snake_case    :		Any  =[0, 1, 2, 3, 4, 5, 6, 7]
							__snake_case    :		List[str]  =['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

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

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

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

