"""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 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Optional[Any]     ,  _A    :				int     ,  _A    :				Union[str, Any])   ->     Union[str, Any]:
					self.assertEqual(len(_A)     ,  len(_A))
					for a, b in zip(_A     ,  _A):
						self.assertAlmostEqual(_A     ,  _A     ,  delta=_A)



				def  _lowercase					(self    :				int)   ->     int:
					__snake_case      :							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(_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  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					__snake_case      :							Any						=			None
					ops.enable_eager_execution_internal()
					__snake_case      :							Tuple						=			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()])
					__snake_case      :							Union[str, Any]						=			tf.config.list_logical_devices(device_type='CPU')
					__snake_case      :							Dict						=			tf.distribute.MirroredStrategy(devices=devices[:2])

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

					def accumulate_on_replica(_A    :				Tuple):
						accumulator([gradient])

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

					@tf.function
					def accumulate(_A    :				Optional[Any]     ,  _A    :				Union[str, Any]):
						with strategy.scope():
							__snake_case      :							Tuple						=			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    :				Tuple     ,  _A    :				List[Any]):
						__snake_case      :							Optional[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])






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_a					:					Dict=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				List[str]     ,  *_A    :				Dict     ,  **_A    :				Optional[Any])   ->     None:
					warnings.warn(
					    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use CLIPImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





