






"""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    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												self.assertEqual(len(snake_case__      )	,  len(snake_case__      )      )
												for a, b in zip(snake_case__	,  snake_case__      ):
																		self.assertAlmostEqual(snake_case__	,  snake_case__	,  delta=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	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(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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	None
												ops.enable_eager_execution_internal()
												lowercase__    :				List[Any]=	tf.config.list_physical_devices("CPU"      )
												if len(snake_case__      ) == 1:
																		tf.config.set_logical_device_configuration(
																		    physical_devices[0]	,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]      )
												lowercase__    :				List[Any]=	tf.config.list_logical_devices(device_type="CPU"      )
												lowercase__    :				Dict=	tf.distribute.MirroredStrategy(devices=devices[:2]      )

												with strategy.scope():
																		lowercase__    :				int=	GradientAccumulator()
																		lowercase__    :				Union[str, Any]=	tf.Variable([4.0, 3.0]      )
																		lowercase__,     lowercase__    :				Union[str, Any]=	create_optimizer(5e-5	,  10	,  5      )
																		lowercase__    :				Dict=	tf.Variable([0.0, 0.0]	,  trainable=snake_case__      )

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

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

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

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

												def _check_local_values(snake_case__	,  snake_case__      ):
																		lowercase__    :				str=	strategy.experimental_local_results(accumulator._gradients[0]      )
																		self.assertListAlmostEqual(values[0].value()	,  snake_case__	,  tol=1e-2      )
																		self.assertListAlmostEqual(values[1].value()	,  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 __future__ import annotations


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	data
												lowercase__    :				Node | None=	None
												lowercase__    :				Node | None=	None





def 						lowercase__(A						)    ->None:  # In Order traversal of the tree




						"""simple docstring"""



						if tree:
												display(tree.left						)
												print(tree.data						)
												display(tree.right						)





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						return 1 + max(depth_of_tree(tree.left						) ,		depth_of_tree(tree.right						)						) if tree else 0





def 						lowercase__(A						)    ->bool:




						"""simple docstring"""



						if not tree:
												return True
						if tree.left and tree.right:
												return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
						else:
												return not tree.left and not tree.right





def 						lowercase__()    ->None:  # Main function for testing.




						"""simple docstring"""



						lowercase__    :				Tuple=	Node(1						)
						lowercase__    :				Optional[int]=	Node(2						)
						lowercase__    :				List[str]=	Node(3						)
						lowercase__    :				Tuple=	Node(4						)
						lowercase__    :				Optional[int]=	Node(5						)
						lowercase__    :				Any=	Node(6						)
						lowercase__    :				Optional[Any]=	Node(7						)
						lowercase__    :				Optional[int]=	Node(8						)
						lowercase__    :				List[str]=	Node(9						)

						print(is_full_binary_tree(A						)						)
						print(depth_of_tree(A						)						)
						print("Tree is: "						)
						display(A						)


if __name__ == "__main__":
			main()






