


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 				_lowerCAmelCase							(						self				:				int		,				__a				:				int		,				__a				:				List[str]		,				__a				:				Tuple							)					->str:
          self.assertEqual(len(__a							)		,				len(__a							)							)
          for a, b in zip(__a		,				__a							):
             self.assertAlmostEqual(__a		,				__a		,				delta=__a							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Tuple:
          lowerCamelCase_						:   str		= 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 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->int:
          lowerCamelCase_						:   List[str]		= None
          ops.enable_eager_execution_internal()
          lowerCamelCase_						:   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_						:   Optional[Any]		= tf.config.list_logical_devices(device_type="""CPU"""							)
          lowerCamelCase_						:   Optional[Any]		= tf.distribute.MirroredStrategy(devices=devices[:2]							)

          with strategy.scope():
             lowerCamelCase_						:   int		= GradientAccumulator()
             lowerCamelCase_						:   int		= tf.Variable([4.0, 3.0]							)
             lowerCamelCase_,	lowerCamelCase_						:   int		= create_optimizer(5e-5		,				10		,				5							)
             lowerCamelCase_						:   Tuple		= tf.Variable([0.0, 0.0]		,				trainable=__a							)

          def accumulate_on_replica(__a				:				Union[str, Any]							):
             accumulator([gradient]							)

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

          @tf.function
          def accumulate(__a				:				int		,				__a				:				int							):
             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				:				Optional[int]		,				__a				:				Optional[Any]							):
             lowerCamelCase_						:   Optional[int]		= 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]							)









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





