


"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  str       =			0
    if start < end:
        snake_case_				:  List[Any]       =			randint(_a	,    _a					)
        snake_case_				:  int       =			a[end]
        snake_case_				:  Optional[int]       =			a[pivot]
        snake_case_				:  Optional[Any]       =			temp

        snake_case_				:  Dict       =			_in_place_partition(_a	,    _a	,    _a					)
        count += _in_place_quick_sort(_a	,    _a	,    p - 1					)
        count += _in_place_quick_sort(_a	,    p + 1	,    _a					)
    return count




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  List[Any]       =			0
    snake_case_				:  List[str]       =			randint(_a	,    _a					)
    snake_case_				:  List[str]       =			a[end]
    snake_case_				:  List[Any]       =			a[pivot]
    snake_case_				:  List[str]       =			temp
    snake_case_				:  str       =			start - 1
    for index in range(_a	,    _a					):
        count += 1
        if a[index] < a[end]:  # check if current val is less than pivot value
            snake_case_				:  Union[str, Any]       =			new_pivot_index + 1
            snake_case_				:  Union[str, Any]       =			a[new_pivot_index]
            snake_case_				:  Dict       =			a[index]
            snake_case_				:  List[Any]       =			temp

    snake_case_				:  Tuple       =			a[new_pivot_index + 1]
    snake_case_				:  Optional[int]       =			a[end]
    snake_case_				:  List[str]       =			temp
    return new_pivot_index + 1, count


lowercase__					:						Optional[int]												=			TemporaryFile()
lowercase__					:						Dict												=			1_00  # 1000 elements are to be sorted


lowercase__					:						Union[str, Any]												=			0, 1  # mean and standard deviation
lowercase__					:						Optional[Any]												=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
lowercase__					:						Dict												=			np.load(outfile)
lowercase__					:						Dict												=			len(M) - 1
lowercase__					:						Optional[Any]												=			_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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





