





from random import randint
from tempfile import TemporaryFile

import numpy as np



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				int:
 UpperCamelCase			:      List[Any] 			=     0
 if start < end:
  UpperCamelCase			:      List[str] 			=     randint(_lowerCAmelCase							,     _lowerCAmelCase  )
  UpperCamelCase			:      Union[str, Any] 			=     a[end]
  UpperCamelCase			:      Any 			=     a[pivot]
  UpperCamelCase			:      Union[str, Any] 			=     temp

  UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     _in_place_partition(_lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )
  count += _in_place_quick_sort(_lowerCAmelCase							,     _lowerCAmelCase							,     p - 1  )
  count += _in_place_quick_sort(_lowerCAmelCase							,     p + 1							,     _lowerCAmelCase  )
 return count



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

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


__lowerCamelCase :  Union[str, Any]										=  TemporaryFile()
__lowerCamelCase :  Optional[int]										=  100  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
__lowerCamelCase :  List[Any]										=  np.load(outfile)
__lowerCamelCase :  Optional[Any]										=  len(M) - 1
__lowerCamelCase :  Optional[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 math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
