
"""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 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Any							,						snake_case_   :							Dict							,						snake_case_   :							str    ):
												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 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       None
												ops.enable_eager_execution_internal()
												__snake_case     			=       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()]    )
												__snake_case     			=       tf.config.list_logical_devices(device_type="CPU"    )
												__snake_case     			=       tf.distribute.MirroredStrategy(devices=devices[:2]    )

												with strategy.scope():
																			__snake_case     			=       GradientAccumulator()
																			__snake_case     			=       tf.Variable([4.0, 3.0]    )
																			__snake_case			,   __snake_case     			=       create_optimizer(5e-5							,						10							,						5    )
																			__snake_case     			=       tf.Variable([0.0, 0.0]							,						trainable=snake_case_    )

												def accumulate_on_replica(snake_case_   :							Optional[Any]    ):
																			accumulator([gradient]    )

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

												@tf.function
												def accumulate(snake_case_   :							Dict							,						snake_case_   :							Any    ):
																			with strategy.scope():
																										__snake_case     			=       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_   :							Tuple							,						snake_case_   :							str    ):
																			__snake_case     			=       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

from dataclasses import dataclass







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
														if node is None:
																					return True

														if not isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					return False

														try:
																					float(node.data )
														except (TypeError, ValueError):
																					return False

														return is_valid_tree(node.left ) and is_valid_tree(node.right )

							if not is_valid_tree(SCREAMING_SNAKE_CASE ):
														raise ValueError(
														    "Each node should be type of TreeNode and data should be float." )

							def is_binary_search_tree_recursive_check(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> bool:

														if node is None:
																					return True

														return (
														    left_bound < node.data < right_bound
														    and is_binary_search_tree_recursive_check(node.left							,    SCREAMING_SNAKE_CASE							,    node.data )
														    and is_binary_search_tree_recursive_check(
														        node.right							,    node.data							,    SCREAMING_SNAKE_CASE )
														)

							return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE							,    -float("inf" )							,    float("inf" ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
