


'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:str						)				->					List[Any]:




					'''simple docstring'''



					_a      =							0
					if start < end:
										_a      =							randint(lowerCAmelCase__			,  lowerCAmelCase__						)
										_a      =							a[end]
										_a      =							a[pivot]
										_a      =							temp

										_a ,				_a      =							_in_place_partition(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
										count += _in_place_quick_sort(lowerCAmelCase__			,  lowerCAmelCase__			,  p - 1						)
										count += _in_place_quick_sort(lowerCAmelCase__			,  p + 1			,  lowerCAmelCase__						)
					return count






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:List[Any]						)				->					List[Any]:




					'''simple docstring'''



					_a      =							0
					_a      =							randint(lowerCAmelCase__			,  lowerCAmelCase__						)
					_a      =							a[end]
					_a      =							a[pivot]
					_a      =							temp
					_a      =							start - 1
					for index in range(lowerCAmelCase__			,  lowerCAmelCase__						):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      =							new_pivot_index + 1
															_a      =							a[new_pivot_index]
															_a      =							a[index]
															_a      =							temp

					_a      =							a[new_pivot_index + 1]
					_a      =							a[end]
					_a      =							temp
					return new_pivot_index + 1, count


a_   :      List[Any]				    =      TemporaryFile()
a_   :      Union[str, Any]				    =      1_0_0  # 1000 elements are to be sorted


a_   :      Dict				    =      0, 1  # mean and standard deviation
a_   :      Tuple				    =      np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
a_   :      Optional[int]				    =      np.load(outfile)
a_   :      List[Any]				    =      len(M) - 1
a_   :      str				    =      _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)






print(z)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_   :      str				    =      {
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NllbMoeForConditionalGeneration",
     "NllbMoeModel",
     "NllbMoePreTrainedModel",
     "NllbMoeTop2Router",
     "NllbMoeSparseMLP",
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 a_   :      str				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

