
import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	tuple:
				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							Tuple	=						[], [], []
				for element in data:
								if element < pivot:
												less.append(lowerCamelCase						)
								elif element > pivot:
												greater.append(lowerCamelCase						)
								else:
												equal.append(lowerCamelCase						)
				return less, equal, greater







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[int]:
				# index = len(items) // 2 when trying to find the median
				#   (value of index when items is sorted)

				# invalid input
				if index >= len(lowerCamelCase						) or index < 0:
								return None

				UpperCamelCase_:							List[Any]	=						items[random.randint(0     ,    len(lowerCamelCase						) - 1						)]
				UpperCamelCase_:							List[str]	=						0
				UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						_partition(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							str	=						len(lowerCamelCase						)
				UpperCamelCase_:							int	=						len(lowerCamelCase						)

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








lowerCamelCase_						: Optional[Any]         =  """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCamelCase_						: Union[str, Any]         =  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCamelCase_						: Optional[Any]         =  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






