






'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      str       =  0
  if start < end:
    a		:      List[Any]       =  randint(_lowercase				,    _lowercase       )
    a		:      int       =  a[end]
    a		:      str       =  a[pivot]
    a		:      Tuple       =  temp

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

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


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


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


outfile.seek(0)  # using the same array
lowerCAmelCase:      int		 =		np.load(outfile)
lowerCAmelCase:      Any		 =		len(M) - 1
lowerCAmelCase:      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'''






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.bert.modeling_flax_bert import (
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForNextSentencePrediction,
         FlaxBertForPreTraining,
         FlaxBertForQuestionAnswering,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertModel,
     )

class 							a__(      unittest.TestCase			):







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Any=13		,    __snake_case				:						Any=7		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						Optional[Any]=99		,    __snake_case				:						List[str]=32		,    __snake_case				:						List[Any]=5		,    __snake_case				:						int=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						int="gelu"		,    __snake_case				:						Tuple=0.1		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						Any=5_12		,    __snake_case				:						Tuple=16		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						str=0.02		,    __snake_case				:						Union[str, Any]=4		,    ):
      a		:      List[Any]       =  parent
      a		:      List[str]       =  batch_size
      a		:      Dict       =  seq_length
      a		:      str       =  is_training
      a		:      Optional[int]       =  use_attention_mask
      a		:      Union[str, Any]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Dict       =  vocab_size
      a		:      Tuple       =  hidden_size
      a		:      Optional[Any]       =  num_hidden_layers
      a		:      List[str]       =  num_attention_heads
      a		:      Tuple       =  intermediate_size
      a		:      Dict       =  hidden_act
      a		:      List[str]       =  hidden_dropout_prob
      a		:      str       =  attention_probs_dropout_prob
      a		:      str       =  max_position_embeddings
      a		:      Tuple       =  type_vocab_size
      a		:      Optional[Any]       =  type_sequence_label_size
      a		:      Union[str, Any]       =  initializer_range
      a		:      Union[str, Any]       =  num_choices







    def     lowercase_ (   self				:						Any			):
      a		:      List[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[str]       =  None
      if self.use_attention_mask:
        a		:      List[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Dict       =  None
      if self.use_token_type_ids:
        a		:      List[str]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size			)

      a		:      Dict       =  BertConfig(
          vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    is_decoder=__snake_case		,    initializer_range=self.initializer_range		,    )

      return config, input_ids, token_type_ids, attention_mask







    def     lowercase_ (   self				:						Tuple			):
      a		:      Optional[int]       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Union[str, Any]       =  config_and_inputs
      a		:      str       =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						List[str]			):
      a		:      int       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Tuple       =  config_and_inputs

      a		:      int       =  True
      a		:      int       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      a		:      Optional[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2			)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )

@require_flax
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					True

    lowercase__				=					(
        (
            FlaxBertModel,
            FlaxBertForPreTraining,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForQuestionAnswering,
            FlaxBertForNextSentencePrediction,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      str       =  FlaxBertModelTester(self			)







    @slow
    def     lowercase_ (   self				:						Any			):
      # Only check this for base model, not necessary for all model classes.
      # This will also help speed-up tests.
      a		:      Optional[Any]       =  FlaxBertModel.from_pretrained('bert-base-cased'			)
      a		:      Union[str, Any]       =  model(np.ones((1, 1)			)			)
      self.assertIsNotNone(__snake_case			)