


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 				snake_case__				(							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 				snake_case__				(							self				):

						'''simple docstring'''


						UpperCamelCase__					=       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 				snake_case__				(							self				):

						'''simple docstring'''


						UpperCamelCase__					=       None
						ops.enable_eager_execution_internal()
						UpperCamelCase__					=       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()]				)
						UpperCamelCase__					=       tf.config.list_logical_devices(device_type="CPU"				)
						UpperCamelCase__					=       tf.distribute.MirroredStrategy(devices=devices[:2]				)

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






def        UpperCamelCase_(				_A   :str  )->		int:
			UpperCamelCase__					=       hex_num.strip()
			if not hex_num:
						raise ValueError("No value was passed to the function"  )

			UpperCamelCase__					=       hex_num[0] == "-"
			if is_negative:
						UpperCamelCase__					=       hex_num[1:]

			try:
						UpperCamelCase__					=       int(_A				,     16  )
			except ValueError:
						raise ValueError("Invalid value was passed to the function"  )

			UpperCamelCase__					=       ""
			while int_num > 0:
						UpperCamelCase__					=       str(int_num % 2  ) + bin_str
						int_num >>= 1

			return int(("-" + bin_str) if is_negative else bin_str  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


