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 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        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      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  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      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  None
        ops.enable_eager_execution_internal()
        _A        =  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()]							)
        _A        =  tf.config.list_logical_devices(device_type="""CPU"""							)
        _A        =  tf.distribute.MirroredStrategy(devices=devices[:2]							)

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

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

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

        @tf.function
        def accumulate(lowerCAmelCase_		,				lowerCAmelCase_							):
               with strategy.scope():
                      _A        =  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(lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  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'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




