'''THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE
DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY,
WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.'''

# Bitcoin Cash (BCH)   qpz32c4lg7x7lnk9jg6qg7s4uavdce89myax5v5nuk
# Ether (ETH) -        0x843d3DEC2A4705BD4f45F674F641cE2D0022c9FB
# Litecoin (LTC) -     Lfk5y4F7KZa9oRxpazETwjQnHszEPvqPvu
# Bitcoin (BTC) -      34L8qWiQyKr8k4TnHDacfjbaSqQASbBtTd

# contact :- <EMAIL>



import random

"""
A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted
https://en.wikipedia.org/wiki/Quickselect
"""


def _partition(data, pivot):
    """
    Three way partition the data into smaller, equal and greater lists,
    in relationship to the pivot
    :param data: The data to be sorted (a list)
    :param pivot: The value to partition the data on
    :return: Three list: smaller, equal and greater
    """
    less, equal, greater = [], [], []
    for element in data:
        if element < pivot:
            less.append(element)
        elif element > pivot:
            greater.append(element)
        else:
            equal.append(element)
    return less, equal, greater


def quickSelect(list, k):
    # k = len(list) // 2 when trying to find the median (index that value
    # would be when list is sorted)

    # invalid input
    if k >= len(list) or k < 0:
        return None

    smaller = []
    larger = []
    pivot = random.randint(0, len(list) - 1)
    pivot = list[pivot]
    count = 0
    smaller, equal, larger = _partition(list, pivot)
    count = len(equal)
    m = len(smaller)

    # k is the pivot
    if m <= k < m + count:
        return pivot
    # must be in smaller
    elif m > k:
        return quickSelect(smaller, k)
    # must be in larger
    else:
        return quickSelect(larger, k - (m + count))

'''THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE
DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY,
WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.'''

# Bitcoin Cash (BCH)   qpz32c4lg7x7lnk9jg6qg7s4uavdce89myax5v5nuk
# Ether (ETH) -        0x843d3DEC2A4705BD4f45F674F641cE2D0022c9FB
# Litecoin (LTC) -     Lfk5y4F7KZa9oRxpazETwjQnHszEPvqPvu
# Bitcoin (BTC) -      34L8qWiQyKr8k4TnHDacfjbaSqQASbBtTd

# contact :- <EMAIL>



import random

"""
A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted
https://en.wikipedia.org/wiki/Quickselect
"""


def _partition(data, pivot):
    """
    Three way partition the data into smaller, equal and greater lists,
    in relationship to the pivot
    :param data: The data to be sorted (a list)
    :param pivot: The value to partition the data on
    :return: Three list: smaller, equal and greater
    """
    less, equal, greater = [], [], []
    for element in data:
        if element < pivot:
            less.append(element)
        elif element > pivot:
            greater.append(element)
        else:
            equal.append(element)
    return less, equal, greater


def quickSelect(list, k):
    # k = len(list) // 2 when trying to find the median (index that value
    # would be when list is sorted)

    # invalid input
    if k >= len(list) or k < 0:
        return None

    smaller = []
    larger = []
    pivot = random.randint(0, len(list) - 1)
    pivot = list[pivot]
    count = 0
    smaller, equal, larger = _partition(list, pivot)
    count = len(equal)
    m = len(smaller)

    # k is the pivot
    if m <= k < m + count:
        return pivot
    # must be in smaller
    elif m > k:
        return quickSelect(smaller, k)
    # must be in larger
    else:
        return quickSelect(larger, k - (m + count))

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Bitcoin Cash (BCH)   qpz32c4lg7x7lnk9jg6qg7s4uavdce89myax5v5nuk # Ether (ETH) -        0x843d3DEC2A4705BD4f45F674F641cE2D0022c9FB # Litecoin (LTC) -     Lfk5y4F7KZa9oRxpazETwjQnHszEPvqPvu # Bitcoin (BTC) -      34L8qWiQyKr8k4TnHDacfjbaSqQASbBtTd # contact :- <EMAIL> A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted https://en.wikipedia.org/wiki/Quickselect Three way partition the data into smaller, equal and greater lists,     in relationship to the pivot     :param data: The data to be sorted (a list)     :param pivot: The value to partition the data on     :return: Three list: smaller, equal and greater # k = len(list) // 2 when trying to find the median (index that value # would be when list is sorted) # invalid input # k is the pivot # must be in smaller # must be in larger