'''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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     int:
								self.assertEqual(len(_lowercase				)		,						len(_lowercase				)				)
								for a, b in zip(_lowercase		,						_lowercase				):
														self.assertAlmostEqual(_lowercase		,						_lowercase		,						delta=_lowercase				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=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(_lowercase				):
														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				) ->     Any:
								_lowerCAmelCase										=None
								ops.enable_eager_execution_internal()
								_lowerCAmelCase										=tf.config.list_physical_devices('CPU'				)
								if len(_lowercase				) == 1:
														tf.config.set_logical_device_configuration(
														    physical_devices[0]		,						[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
								_lowerCAmelCase										=tf.config.list_logical_devices(device_type='CPU'				)
								_lowerCAmelCase										=tf.distribute.MirroredStrategy(devices=devices[:2]				)

								with strategy.scope():
														_lowerCAmelCase										=GradientAccumulator()
														_lowerCAmelCase										=tf.Variable([4.0, 3.0]				)
														_lowerCAmelCase						,				_lowerCAmelCase										=create_optimizer(5E-5		,						10		,						5				)
														_lowerCAmelCase										=tf.Variable([0.0, 0.0]		,						trainable=_lowercase				)

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

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

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

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

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

lowercase_     		= 10



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=1
						_lowerCAmelCase										=max(a__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												_lowerCAmelCase										=[[] for _ in range(a__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		_lowerCAmelCase										=int((i / placement) % RADIX      )
																		buckets[tmp].append(a__      )
												# put each buckets' contents into list_of_ints
												_lowerCAmelCase										=0
												for b in range(a__      ):
																		for i in buckets[b]:
																								_lowerCAmelCase										=i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
							import doctest

							doctest.testmod()





