

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 		lowercase__       (		self	:			Dict			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			str			):
        self.assertEqual(len(_lowerCAmelCase			)			, len(_lowerCAmelCase			)			)
        for a, b in zip(_lowerCAmelCase			, _lowerCAmelCase			):
            self.assertAlmostEqual(_lowerCAmelCase			, _lowerCAmelCase			, delta=_lowerCAmelCase			)






    def 		lowercase__       (		self	:			List[str]			):
        lowerCAmelCase :	Union[str, Any]				=					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 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	Optional[Any]				=					None
        ops.enable_eager_execution_internal()
        lowerCAmelCase :	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()]			)
        lowerCAmelCase :	Union[str, Any]				=					tf.config.list_logical_devices(device_type='CPU'			)
        lowerCAmelCase :	Optional[Any]				=					tf.distribute.MirroredStrategy(devices=devices[:2]			)

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

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

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

        @tf.function
        def accumulate(UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			Dict			):
            with strategy.scope():
                lowerCAmelCase :	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_	:			int			, UpperCAmelCase_	:			List[Any]			):
            lowerCAmelCase :	int				=					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]			)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)