



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 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[int]:
									self.assertEqual(len(__lowerCamelCase )	,	len(__lowerCamelCase ) )
									for a, b in zip(__lowerCamelCase	,	__lowerCamelCase ):
												self.assertAlmostEqual(__lowerCamelCase	,	__lowerCamelCase	,	delta=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[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(__lowerCamelCase ):
												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 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Union[str, Any]              =						None
									ops.enable_eager_execution_internal()
									A							:					Tuple              =						tf.config.list_physical_devices("CPU" )
									if len(__lowerCamelCase ) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]	,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
									A							:					Dict              =						tf.config.list_logical_devices(device_type="CPU" )
									A							:					List[str]              =						tf.distribute.MirroredStrategy(devices=devices[:2] )

									with strategy.scope():
												A							:					Optional[int]              =						GradientAccumulator()
												A							:					Tuple              =						tf.Variable([4.0, 3.0] )
												A       , A							:					List[Any]              =						create_optimizer(5e-5	,	10	,	5 )
												A							:					List[str]              =						tf.Variable([0.0, 0.0]	,	trainable=__lowerCamelCase )

									def accumulate_on_replica(__lowerCamelCase      :			Tuple ):
												accumulator([gradient] )

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

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

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

									def _check_local_values(__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any] ):
												A							:					Optional[int]              =						strategy.experimental_local_results(accumulator._gradients[0] )
												self.assertListAlmostEqual(values[0].value()	,	__lowerCamelCase	,	tol=1e-2 )
												self.assertListAlmostEqual(values[1].value()	,	__lowerCamelCase	,	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] )




from math import sqrt




def 	UpperCAmelCase   (						_lowerCamelCase = 100_0000    ):
			A							:					int              =						0
			A							:					int              =						0
			A							:					int

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2 ,    2 * max_cuboid_size + 1    ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
												num_cuboids += (
												    min(_lowerCamelCase ,    sum_shortest_sides // 2    )
												    - max(1 ,    sum_shortest_sides - max_cuboid_size    )
												    + 1
												)

			return max_cuboid_size


if __name__ == "__main__":
							print(F"""{solution() = }""")