



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 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       List[Any]				,				A_     :       Dict				,				A_     :       str):
									self.assertEqual(len(A_)				,				len(A_))
									for a, b in zip(A_				,				A_):
											self.assertAlmostEqual(A_				,				A_				,				delta=A_)




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

									with strategy.scope():
											lowerCAmelCase_			:     int									= GradientAccumulator()
											lowerCAmelCase_			:     List[str]									= tf.Variable([4.0, 3.0])
											lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= create_optimizer(5e-5				,				1_0				,				5)
											lowerCAmelCase_			:     List[str]									= tf.Variable([0.0, 0.0]				,				trainable=A_)

									def accumulate_on_replica(A_     :       Dict):
											accumulator([gradient])

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

									@tf.function
									def accumulate(A_     :       Union[str, Any]				,				A_     :       List[Any]):
											with strategy.scope():
													lowerCAmelCase_			:     Optional[Any]									= strategy.experimental_local_results(A_)
													local_variables[0].assign(A_)
													local_variables[1].assign(A_)
													strategy.run(A_				,				args=(gradient_placeholder,))

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

									def _check_local_values(A_     :       Union[str, Any]				,				A_     :       List[str]):
											lowerCAmelCase_			:     Any									= strategy.experimental_local_results(accumulator._gradients[0])
											self.assertListAlmostEqual(values[0].value()				,				A_				,				tol=1e-2)
											self.assertListAlmostEqual(values[1].value()				,				A_				,				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])










A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





