



"""simple docstring"""
from random import randint
from tempfile import TemporaryFile

import numpy as np
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			0
						if start < end:
												snake_case_ :Dict						=			randint(_lowercase,					_lowercase	)
												snake_case_ :List[Any]						=			a[end]
												snake_case_ :str						=			a[pivot]
												snake_case_ :List[Any]						=			temp

												snake_case_,       snake_case_ :Any						=			_in_place_partition(_lowercase,					_lowercase,					_lowercase	)
												count += _in_place_quick_sort(_lowercase,					_lowercase,					p - 1	)
												count += _in_place_quick_sort(_lowercase,					p + 1,					_lowercase	)
						return count
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


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

						snake_case_ :int						=			a[new_pivot_index + 1]
						snake_case_ :Union[str, Any]						=			a[end]
						snake_case_ :str						=			temp
						return new_pivot_index + 1, count


__a      =				TemporaryFile()
__a      =				1_00  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
__a      =				np.load(outfile)
__a      =				len(M) - 1
__a      =				_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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

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