




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  ):





				'''simple docstring'''



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											self.assertEqual(len(UpperCAmelCase_						)       , len(UpperCAmelCase_						)						)
											for a, b in zip(UpperCAmelCase_       , UpperCAmelCase_						):
																		self.assertAlmostEqual(UpperCAmelCase_       , UpperCAmelCase_       , delta=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		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(UpperCAmelCase_						):
																		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						):
											lowerCAmelCase   =		None
											ops.enable_eager_execution_internal()
											lowerCAmelCase   =		tf.config.list_physical_devices('''CPU'''						)
											if len(UpperCAmelCase_						) == 1:
																		tf.config.set_logical_device_configuration(
																		    physical_devices[0]       , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
											lowerCAmelCase   =		tf.config.list_logical_devices(device_type='''CPU'''						)
											lowerCAmelCase   =		tf.distribute.MirroredStrategy(devices=devices[:2]						)

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

											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():
																									lowerCAmelCase   =		strategy.experimental_local_results(UpperCAmelCase_						)
																									local_variables[0].assign(UpperCAmelCase_						)
																									local_variables[1].assign(UpperCAmelCase_						)
																									strategy.run(UpperCAmelCase_       , args=(gradient_placeholder,)						)

											@tf.function
											def apply_grad():
																		with strategy.scope():
																									strategy.run(UpperCAmelCase_						)

											def _check_local_values(UpperCAmelCase_       , UpperCAmelCase_						):
																		lowerCAmelCase   =		strategy.experimental_local_results(accumulator._gradients[0]						)
																		self.assertListAlmostEqual(values[0].value()       , UpperCAmelCase_       , tol=1E-2						)
																		self.assertListAlmostEqual(values[1].value()       , UpperCAmelCase_       , 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]						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



