'''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 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
         self.assertEqual(len(lowerCamelCase__      )				,     len(lowerCamelCase__      )      )
         for a, b in zip(lowerCamelCase__				,     lowerCamelCase__      ):
           self.assertAlmostEqual(lowerCamelCase__				,     lowerCamelCase__				,     delta=lowerCamelCase__      )






       def 			_snake_case						(      self      ):
         __UpperCAmelCase  :				Optional[int]       				=		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 			_snake_case						(      self      ):
         __UpperCAmelCase  :				int       				=		None
         ops.enable_eager_execution_internal()
         __UpperCAmelCase  :				Optional[int]       				=		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()]      )
         __UpperCAmelCase  :				List[Any]       				=		tf.config.list_logical_devices(device_type="CPU"      )
         __UpperCAmelCase  :				Union[str, Any]       				=		tf.distribute.MirroredStrategy(devices=devices[:2]      )

         with strategy.scope():
           __UpperCAmelCase  :				Dict       				=		GradientAccumulator()
           __UpperCAmelCase  :				Optional[Any]       				=		tf.Variable([4.0, 3.0]      )
           __UpperCAmelCase  :				int       				=		create_optimizer(5E-5				,     10				,     5      )
           __UpperCAmelCase  :				Optional[int]       				=		tf.Variable([0.0, 0.0]				,     trainable=lowerCamelCase__      )

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

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

         @tf.function
         def accumulate(UpperCamelCase_				,     UpperCamelCase_      ):
           with strategy.scope():
             __UpperCAmelCase  :				Union[str, Any]       				=		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(UpperCamelCase_				,     UpperCamelCase_      ):
           __UpperCAmelCase  :				Any       				=		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]      )







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






