




from random import randint
from tempfile import TemporaryFile

import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      0
  if start < end:
    lowerCAmelCase__										=      randint(lowercase_   , lowercase_				)
    lowerCAmelCase__										=      a[end]
    lowerCAmelCase__										=      a[pivot]
    lowerCAmelCase__										=      temp

    lowerCAmelCase__										=      _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_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      0
  lowerCAmelCase__										=      randint(lowercase_   , lowercase_				)
  lowerCAmelCase__										=      a[end]
  lowerCAmelCase__										=      a[pivot]
  lowerCAmelCase__										=      temp
  lowerCAmelCase__										=      start - 1
  for index in range(lowercase_   , lowercase_				):
    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


__magic_name__  :  Optional[int]					=       TemporaryFile()
__magic_name__  :  List[Any]					=       1_00  # 1000 elements are to be sorted


__magic_name__  :  List[str]					=       0, 1  # mean and standard deviation
__magic_name__  :  List[str]					=       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


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

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




print(z)




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



