



"""simple docstring"""
import random



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      str:
		a_     :      Tuple 						=     a[left_index]
		a_     :      str 						=     left_index + 1
		for j in range(left_index + 1, SCREAMING_SNAKE_CASE__	):
				if a[j] < pivot:
						a_						,				a_     :      Optional[Any] 						=     a[i], a[j]
						i += 1
		a_						,				a_     :      Union[str, Any] 						=     a[i - 1], a[left_index]
		return i - 1



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Tuple:
		if left < right:
				a_     :      Any 						=     random.randint(SCREAMING_SNAKE_CASE__, right - 1	)
				a_						,				a_     :      Union[str, Any] 						=     (
				    a[left],
				    a[pivot],
				)  # switches the pivot with the left most bound
				a_     :      Union[str, Any] 						=     partition(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				quick_sort_random(
				    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)  # recursive quicksort to the left of the pivot point
				quick_sort_random(
				    SCREAMING_SNAKE_CASE__, pivot_index + 1, SCREAMING_SNAKE_CASE__	)  # recursive quicksort to the right of the pivot point



def        lowerCAmelCase_						(							)			->      Tuple:
		a_     :      Dict 						=     input("Enter numbers separated by a comma:\n"	).strip()
		a_     :      Any 						=     [int(SCREAMING_SNAKE_CASE__	) for item in user_input.split(","	)]

		quick_sort_random(SCREAMING_SNAKE_CASE__, 0, len(SCREAMING_SNAKE_CASE__	)	)

		print(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)