
'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase     :							int       						=     {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							Union[str, Any]       						=     ['LlamaTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							Optional[int]       						=     ['LlamaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							int       						=     [
          '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

      lowercase     :							Dict       						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




