



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__:   Any								=			{
    "configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   List[str]								=			["BertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Optional[Any]								=			[
						    "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BertForMaskedLM",
						    "BertForMultipleChoice",
						    "BertForNextSentencePrediction",
						    "BertForPreTraining",
						    "BertForQuestionAnswering",
						    "BertForSequenceClassification",
						    "BertForTokenClassification",
						    "BertLayer",
						    "BertLMHeadModel",
						    "BertModel",
						    "BertPreTrainedModel",
						    "load_tf_weights_in_bert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   List[str]								=			[
						    "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFBertEmbeddings",
						    "TFBertForMaskedLM",
						    "TFBertForMultipleChoice",
						    "TFBertForNextSentencePrediction",
						    "TFBertForPreTraining",
						    "TFBertForQuestionAnswering",
						    "TFBertForSequenceClassification",
						    "TFBertForTokenClassification",
						    "TFBertLMHeadModel",
						    "TFBertMainLayer",
						    "TFBertModel",
						    "TFBertPreTrainedModel",
						]
try:
						if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Any								=			["TFBertTokenizer"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Any								=			[
						    "FlaxBertForCausalLM",
						    "FlaxBertForMaskedLM",
						    "FlaxBertForMultipleChoice",
						    "FlaxBertForNextSentencePrediction",
						    "FlaxBertForPreTraining",
						    "FlaxBertForQuestionAnswering",
						    "FlaxBertForSequenceClassification",
						    "FlaxBertForTokenClassification",
						    "FlaxBertModel",
						    "FlaxBertPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
						from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_fast import BertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert import (
												    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BertForMaskedLM,
												    BertForMultipleChoice,
												    BertForNextSentencePrediction,
												    BertForPreTraining,
												    BertForQuestionAnswering,
												    BertForSequenceClassification,
												    BertForTokenClassification,
												    BertLayer,
												    BertLMHeadModel,
												    BertModel,
												    BertPreTrainedModel,
												    load_tf_weights_in_bert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bert import (
												    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBertEmbeddings,
												    TFBertForMaskedLM,
												    TFBertForMultipleChoice,
												    TFBertForNextSentencePrediction,
												    TFBertForPreTraining,
												    TFBertForQuestionAnswering,
												    TFBertForSequenceClassification,
												    TFBertForTokenClassification,
												    TFBertLMHeadModel,
												    TFBertMainLayer,
												    TFBertModel,
												    TFBertPreTrainedModel,
												)

						try:
												if not is_tensorflow_text_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_tf import TFBertTokenizer

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bert import (
												    FlaxBertForCausalLM,
												    FlaxBertForMaskedLM,
												    FlaxBertForMultipleChoice,
												    FlaxBertForNextSentencePrediction,
												    FlaxBertForPreTraining,
												    FlaxBertForQuestionAnswering,
												    FlaxBertForSequenceClassification,
												    FlaxBertForTokenClassification,
												    FlaxBertModel,
												    FlaxBertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:   Any								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _in_place_partition(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      p - 1     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      p + 1      ,      SCREAMING_SNAKE_CASE_     )
					return count







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

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


UpperCamelCase__    			=			TemporaryFile()
UpperCamelCase__    			=			100  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
UpperCamelCase__    			=			np.load(outfile)
UpperCamelCase__    			=			len(M) - 1
UpperCamelCase__    			=			_in_place_quick_sort(M, 0, r)

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


print(z)




