
"""simple docstring"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer




@require_tf
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	len(_SCREAMING_SNAKE_CASE				)				)
			for a, b in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				self.assertAlmostEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	delta=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   GradientAccumulator()
			accumulator([tf.constant([1.0, 2.0]				)]				)
			accumulator([tf.constant([-2.0, 1.0]				)]				)
			accumulator([tf.constant([-1.0, 2.0]				)]				)
			with self.assertRaises(_SCREAMING_SNAKE_CASE				):
				accumulator([tf.constant([1.0, 1.0]				), tf.constant([2.0, 2.0]				)]				)
			self.assertEqual(accumulator.step ,	3				)
			self.assertEqual(len(accumulator.gradients				) ,	1				)
			self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,	[-2.0, 5.0] ,	tol=1E-2				)
			accumulator.reset()
			self.assertEqual(accumulator.step ,	0				)
			self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,	[0.0, 0.0] ,	tol=1E-2				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   None
			ops.enable_eager_execution_internal()
			__lowerCAmelCase    :		Tuple		    =   tf.config.list_physical_devices('CPU'				)
			if len(_SCREAMING_SNAKE_CASE				) == 1:
				tf.config.set_logical_device_configuration(
				    physical_devices[0] ,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
			__lowerCAmelCase    :		List[str]		    =   tf.config.list_logical_devices(device_type='CPU'				)
			__lowerCAmelCase    :		Optional[int]		    =   tf.distribute.MirroredStrategy(devices=devices[:2]				)

			with strategy.scope():
				__lowerCAmelCase    :		Dict		    =   GradientAccumulator()
				__lowerCAmelCase    :		Union[str, Any]		    =   tf.Variable([4.0, 3.0]				)
				__lowerCAmelCase    :		Any		    =   create_optimizer(5E-5 ,	10 ,	5				)
				__lowerCAmelCase    :		Optional[int]		    =   tf.Variable([0.0, 0.0] ,	trainable=_SCREAMING_SNAKE_CASE				)

			def accumulate_on_replica(_SCREAMING_SNAKE_CASE				):
				accumulator([gradient]				)

			def apply_on_replica():
				optimizer.apply_gradients(list(zip(accumulator.gradients ,	[variable]				)				)				)

			@tf.function
			def accumulate(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				with strategy.scope():
					__lowerCAmelCase    :		Any		    =   strategy.experimental_local_results(_SCREAMING_SNAKE_CASE				)
					local_variables[0].assign(_SCREAMING_SNAKE_CASE				)
					local_variables[1].assign(_SCREAMING_SNAKE_CASE				)
					strategy.run(_SCREAMING_SNAKE_CASE ,	args=(gradient_placeholder,)				)

			@tf.function
			def apply_grad():
				with strategy.scope():
					strategy.run(_SCREAMING_SNAKE_CASE				)

			def _check_local_values(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Dict		    =   strategy.experimental_local_results(accumulator._gradients[0]				)
				self.assertListAlmostEqual(values[0].value() ,	_SCREAMING_SNAKE_CASE ,	tol=1E-2				)
				self.assertListAlmostEqual(values[1].value() ,	_SCREAMING_SNAKE_CASE ,	tol=1E-2				)

			accumulate([1.0, 2.0] ,	[-1.0, 1.0]				)
			accumulate([3.0, -1.0] ,	[-1.0, -1.0]				)
			accumulate([-2.0, 2.0] ,	[3.0, -2.0]				)
			self.assertEqual(accumulator.step ,	3				)
			_check_local_values([2.0, 3.0] ,	[1.0, -2.0]				)
			apply_grad()
			self.assertListAlmostEqual(variable.value() ,	[4.0, 3.0] ,	tol=1E-2				)
			accumulator.reset()
			self.assertEqual(accumulator.step ,	0				)
			_check_local_values([0.0, 0.0] ,	[0.0, 0.0]				)

"""simple docstring"""







from collections.abc import Sequence
from queue import Queue




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   start
			__lowerCAmelCase    :		Union[str, Any]		    =   end
			__lowerCAmelCase    :		Union[str, Any]		    =   val
			__lowerCAmelCase    :		Dict		    =   (start + end) // 2
			__lowerCAmelCase    :		Dict		    =   left
			__lowerCAmelCase    :		List[Any]		    =   right







		def __repr__(   self				):
			return f"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   collection
			__lowerCAmelCase    :		Optional[int]		    =   function
			if self.collection:
				__lowerCAmelCase    :		str		    =   self._build_tree(0 ,	len(_SCREAMING_SNAKE_CASE				) - 1				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self._update_tree(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return self._query_range(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if start == end:
				return SegmentTreeNode(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.collection[start]				)
			__lowerCAmelCase    :		Tuple		    =   (start + end) // 2
			__lowerCAmelCase    :		List[str]		    =   self._build_tree(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   self._build_tree(mid + 1 ,	_SCREAMING_SNAKE_CASE				)
			return SegmentTreeNode(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.fn(left.val ,	right.val				) ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if node.start == i and node.end == i:
				__lowerCAmelCase    :		int		    =   val
				return
			if i <= node.mid:
				self._update_tree(node.left ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			else:
				self._update_tree(node.right ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   self.fn(node.left.val ,	node.right.val				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if node.start == i and node.end == j:
				return node.val

			if i <= node.mid:
				if j <= node.mid:
					# range in left child tree
					return self._query_range(node.left ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				else:
					# range in left child tree and right child tree
					return self.fn(
					    self._query_range(node.left ,	_SCREAMING_SNAKE_CASE ,	node.mid				) ,	self._query_range(node.right ,	node.mid + 1 ,	_SCREAMING_SNAKE_CASE				) ,	)
			else:
				# range in right child tree
				return self._query_range(node.right ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			if self.root is not None:
				__lowerCAmelCase    :		Optional[Any]		    =   Queue()
				queue.put(self.root				)
				while not queue.empty():
					__lowerCAmelCase    :		str		    =   queue.get()
					yield node

					if node.left is not None:
						queue.put(node.left				)

					if node.right is not None:
						queue.put(node.right				)


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print("""*""" * 50)
														lowerCamelCase__           =	SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()