




import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
 from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor
class 						_A (  _lowerCamelCase				):






  def  __a      (  self		:     List[Any]				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.config_class(**self.inputs_dict				)
       self.parent.assertTrue(hasattr(lowercase_				,						'''hidden_sizes'''				)				)
       self.parent.assertTrue(hasattr(lowercase_				,						'''neck_hidden_sizes'''				)				)
       self.parent.assertTrue(hasattr(lowercase_				,						'''num_attention_heads'''				)				)


class 						_A :






  def __init__(  self		:     Any				,						_A		:     Dict				,						_A		:     str=13				,						_A		:     int=32				,						_A		:     Any=2				,						_A		:     Tuple=3				,						_A		:     Optional[int]=640				,						_A		:     Union[str, Any]=4				,						_A		:     Optional[int]="silu"				,						_A		:     Tuple=3				,						_A		:     List[str]=32				,						_A		:     int=0.1				,						_A		:     str=0.1				,						_A		:     Tuple=0.1				,						_A		:     str=0.02				,						_A		:     List[str]=True				,						_A		:     Optional[Any]=True				,						_A		:     str=10				,						_A		:     List[str]=None				,						)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Tuple       =			parent
       lowercase :       Dict       =			batch_size
       lowercase :       str       =			image_size
       lowercase :       Union[str, Any]       =			patch_size
       lowercase :       Optional[Any]       =			num_channels
       lowercase :       List[Any]       =			last_hidden_size
       lowercase :       int       =			num_attention_heads
       lowercase :       List[str]       =			hidden_act
       lowercase :       Any       =			conv_kernel_size
       lowercase :       List[str]       =			output_stride
       lowercase :       Optional[int]       =			hidden_dropout_prob
       lowercase :       Tuple       =			attention_probs_dropout_prob
       lowercase :       Union[str, Any]       =			classifier_dropout_prob
       lowercase :       Tuple       =			use_labels
       lowercase :       List[Any]       =			is_training
       lowercase :       int       =			num_labels
       lowercase :       List[str]       =			initializer_range
       lowercase :       List[Any]       =			scope






  def  __a      (  self		:     List[str]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       List[Any]       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       lowercase :       Any       =			None
       lowercase :       Tuple       =			None
       if self.use_labels:
            lowercase :       List[str]       =			ids_tensor([self.batch_size]				,						self.num_labels				)
            lowercase :       Union[str, Any]       =			ids_tensor([self.batch_size, self.image_size, self.image_size]				,						self.num_labels				)

       lowercase :       Tuple       =			self.get_config()

       return config, pixel_values, labels, pixel_labels






  def  __a      (  self		:     Optional[Any]				)		->   Optional[int]:






       """simple docstring"""







       return MobileViTConfig(
           image_size=self.image_size				,						patch_size=self.patch_size				,						num_channels=self.num_channels				,						num_attention_heads=self.num_attention_heads				,						hidden_act=self.hidden_act				,						conv_kernel_size=self.conv_kernel_size				,						output_stride=self.output_stride				,						hidden_dropout_prob=self.hidden_dropout_prob				,						attention_probs_dropout_prob=self.attention_probs_dropout_prob				,						classifier_dropout_prob=self.classifier_dropout_prob				,						initializer_range=self.initializer_range				,						)






  def  __a      (  self		:     str				,						_A		:     List[Any]				,						_A		:     Tuple				,						_A		:     Union[str, Any]				,						_A		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			MobileViTModel(config=lowercase_				)
       model.to(lowercase_				)
       model.eval()
       lowercase :       List[Any]       =			model(lowercase_				)
       self.parent.assertEqual(
           result.last_hidden_state.shape				,						(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )				,						)






  def  __a      (  self		:     List[Any]				,						_A		:     int				,						_A		:     Tuple				,						_A		:     int				,						_A		:     Optional[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.num_labels
       lowercase :       Optional[Any]       =			MobileViTForImageClassification(lowercase_				)
       model.to(lowercase_				)
       model.eval()
       lowercase :       Union[str, Any]       =			model(lowercase_				,						labels=lowercase_				)
       self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)				)






  def  __a      (  self		:     List[str]				,						_A		:     List[str]				,						_A		:     Optional[Any]				,						_A		:     Optional[int]				,						_A		:     Optional[int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Dict       =			self.num_labels
       lowercase :       Dict       =			MobileViTForSemanticSegmentation(lowercase_				)
       model.to(lowercase_				)
       model.eval()
       lowercase :       Optional[Any]       =			model(lowercase_				)
       self.parent.assertEqual(
           result.logits.shape				,						(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )				,						)
       lowercase :       Dict       =			model(lowercase_				,						labels=lowercase_				)
       self.parent.assertEqual(
           result.logits.shape				,						(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )				,						)






  def  __a      (  self		:     List[Any]				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       List[str]       =			self.prepare_config_and_inputs()
       lowercase      ,	lowercase      ,	lowercase      ,	lowercase :       List[str]       =			config_and_inputs
       lowercase :       Optional[int]       =			{'''pixel_values''': pixel_values}
       return config, inputs_dict


@require_torch
class 						_A (  _lowerCamelCase      ,       _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Optional[Any]			=      (
      (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  _UpperCamelCase       :      int			=      (
      {
          '''feature-extraction''': MobileViTModel,
          '''image-classification''': MobileViTForImageClassification,
          '''image-segmentation''': MobileViTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _UpperCamelCase       :      List[Any]			=      False
  _UpperCamelCase       :      Optional[int]			=      False
  _UpperCamelCase       :      str			=      False
  _UpperCamelCase       :      int			=      False






  def  __a      (  self		:     Dict				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       int       =			MobileViTModelTester(self				)
       lowercase :       int       =			MobileViTConfigTester(self				,						config_class=lowercase_				,						has_text_modality=lowercase_				)






  def  __a      (  self		:     int				)		->   Tuple:






       """simple docstring"""







       self.config_tester.run_common_tests()






  @unittest.skip(reason='''MobileViT does not use inputs_embeds'''				)
  def  __a      (  self		:     List[str]				)		->   str:






       """simple docstring"""







       pass






  @unittest.skip(reason='''MobileViT does not support input and output embeddings'''				)
  def  __a      (  self		:     List[str]				)		->   List[Any]:






       """simple docstring"""







       pass






  @unittest.skip(reason='''MobileViT does not output attentions'''				)
  def  __a      (  self		:     Optional[int]				)		->   str:






       """simple docstring"""







       pass






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase :       Tuple       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowercase :       List[str]       =			model_class(lowercase_				)
            lowercase :       Tuple       =			inspect.signature(model.forward				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            lowercase :       int       =			[*signature.parameters.keys()]

            lowercase :       Optional[int]       =			['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,						lowercase_				)






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
  def  __a      (  self		:     Any				)		->   Optional[int]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[int]				)		->   Dict:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_				)






  def  __a      (  self		:     List[str]				)		->   List[str]:






       """simple docstring"""







       def check_hidden_states_output(_A		:     Optional[Any]				,						_A		:     Optional[Any]				,						_A		:     Optional[Any]				):
            lowercase :       Optional[int]       =			model_class(lowercase_				)
            model.to(lowercase_				)
            model.eval()

            with torch.no_grad():
                 lowercase :       List[Any]       =			model(**self._prepare_for_class(lowercase_				,						lowercase_				)				)

            lowercase :       Optional[int]       =			outputs.hidden_states

            lowercase :       str       =			5
            self.assertEqual(len(lowercase_				)				,						lowercase_				)

            # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
            # with the width and height being successively divided by 2.
            lowercase :       str       =			2
            for i in range(len(lowercase_				)				):
                 self.assertListEqual(
                     list(hidden_states[i].shape[-2:]				)				,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]				,						)
                 divisor *= 2

            self.assertEqual(self.model_tester.output_stride				,						divisor // 2				)

       lowercase      ,	lowercase :       List[str]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowercase :       List[Any]       =			True
            check_hidden_states_output(lowercase_				,						lowercase_				,						lowercase_				)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowercase :       List[Any]       =			True

            check_hidden_states_output(lowercase_				,						lowercase_				,						lowercase_				)






  def  __a      (  self		:     int				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_				)






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







       lowercase :       Any       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_				)






  @slow
  def  __a      (  self		:     Optional[int]				)		->   Union[str, Any]:






       """simple docstring"""







       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase :       Optional[Any]       =			MobileViTModel.from_pretrained(lowercase_				)
            self.assertIsNotNone(lowercase_				)






def  snake_case(    )			->							List[str]:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
     return image



@require_torch
@require_vision
class 						_A (  unittest.TestCase				):






  @cached_property
  def  __a      (  self		:     Optional[int]				)		->   Dict:






       """simple docstring"""







       return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''				) if is_vision_available() else None






  @slow
  def  __a      (  self		:     Dict				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Any       =			MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''				).to(lowercase_				)

       lowercase :       Optional[Any]       =			self.default_image_processor
       lowercase :       Union[str, Any]       =			prepare_img()
       lowercase :       List[Any]       =			image_processor(images=lowercase_				,						return_tensors='''pt'''				).to(lowercase_				)

       # forward pass
       with torch.no_grad():
            lowercase :       int       =			model(**lowercase_				)

       # verify the logits
       lowercase :       str       =			torch.Size((1, 1_000)				)
       self.assertEqual(outputs.logits.shape				,						lowercase_				)

       lowercase :       List[str]       =			torch.tensor([-1.9_364, -1.2_327, -0.4_653]				).to(lowercase_				)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,						lowercase_				,						atol=1E-4				)				)






  @slow
  def  __a      (  self		:     Tuple				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       int       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)
       lowercase :       Dict       =			model.to(lowercase_				)

       lowercase :       Optional[Any]       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)

       lowercase :       Dict       =			prepare_img()
       lowercase :       Optional[Any]       =			image_processor(images=lowercase_				,						return_tensors='''pt'''				).to(lowercase_				)

       # forward pass
       with torch.no_grad():
            lowercase :       Optional[int]       =			model(**lowercase_				)
       lowercase :       Optional[Any]       =			outputs.logits

       # verify the logits
       lowercase :       Union[str, Any]       =			torch.Size((1, 21, 32, 32)				)
       self.assertEqual(logits.shape				,						lowercase_				)

       lowercase :       Union[str, Any]       =			torch.tensor(
           [
               [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
               [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
               [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
           ]				,						device=lowercase_				,						)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]				,						lowercase_				,						atol=1E-4				)				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   List[str]:






       """simple docstring"""







       lowercase :       str       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)
       lowercase :       List[Any]       =			model.to(lowercase_				)

       lowercase :       Tuple       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)

       lowercase :       List[str]       =			prepare_img()
       lowercase :       List[str]       =			image_processor(images=lowercase_				,						return_tensors='''pt'''				).to(lowercase_				)

       # forward pass
       with torch.no_grad():
            lowercase :       Optional[Any]       =			model(**lowercase_				)

       lowercase :       Optional[int]       =			outputs.logits.detach().cpu()

       lowercase :       Optional[int]       =			image_processor.post_process_semantic_segmentation(outputs=lowercase_				,						target_sizes=[(50, 60)]				)
       lowercase :       Optional[Any]       =			torch.Size((50, 60)				)
       self.assertEqual(segmentation[0].shape				,						lowercase_				)

       lowercase :       List[str]       =			image_processor.post_process_semantic_segmentation(outputs=lowercase_				)
       lowercase :       Dict       =			torch.Size((32, 32)				)
       self.assertEqual(segmentation[0].shape				,						lowercase_				)





import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
 from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
 from tensorflow.keras.optimizers import Adam
class 						_A (  tf.keras.optimizers.schedules.LearningRateSchedule				):






  def __init__(  self		:     str				,						_A		:     float				,						_A		:     Callable				,						_A		:     int				,						_A		:     float = 1.0				,						_A		:     str = None				,						)		->   List[str]:






       """simple docstring"""







       super().__init__()
       lowercase :       List[str]       =			initial_learning_rate
       lowercase :       List[str]       =			warmup_steps
       lowercase :       Tuple       =			power
       lowercase :       Any       =			decay_schedule_fn
       lowercase :       Union[str, Any]       =			name






  def __call__(  self		:     str				,						_A		:     Any				)		->   Optional[int]:






       """simple docstring"""







       with tf.name_scope(self.name or '''WarmUp'''				) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            lowercase :       List[Any]       =			tf.cast(_A				,						tf.floataa				)
            lowercase :       Union[str, Any]       =			tf.cast(self.warmup_steps				,						tf.floataa				)
            lowercase :       List[str]       =			global_step_float / warmup_steps_float
            lowercase :       int       =			self.initial_learning_rate * tf.math.pow(_A				,						self.power				)
            return tf.cond(
                global_step_float < warmup_steps_float				,						lambda: warmup_learning_rate				,						lambda: self.decay_schedule_fn(step - self.warmup_steps				)				,						name=_A				,						)






  def  __a      (  self		:     Dict				)		->   List[str]:






       """simple docstring"""







       return {
           "initial_learning_rate": self.initial_learning_rate,
           "decay_schedule_fn": self.decay_schedule_fn,
           "warmup_steps": self.warmup_steps,
           "power": self.power,
           "name": self.name,
       }






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = 0.0			,		__magic_name__ = 0.9			,		__magic_name__ = 0.9_9_9			,		__magic_name__ = 1e-8			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = 0.0			,		__magic_name__ = 1.0			,		__magic_name__ = None			,		)			->							int:





     '''simple docstring'''


     lowercase :       List[Any]       =			tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=__magic_name__			,		decay_steps=num_train_steps - num_warmup_steps			,		end_learning_rate=init_lr * min_lr_ratio			,		power=__magic_name__			,		)
     if num_warmup_steps:
          lowercase :       Optional[int]       =			WarmUp(
              initial_learning_rate=__magic_name__			,		decay_schedule_fn=__magic_name__			,		warmup_steps=__magic_name__			,		)
     if weight_decay_rate > 0.0:
          lowercase :       Optional[Any]       =			AdamWeightDecay(
              learning_rate=__magic_name__			,		weight_decay_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']			,		include_in_weight_decay=__magic_name__			,		)
     else:
          lowercase :       str       =			tf.keras.optimizers.Adam(
              learning_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		)
     # We return the optimizer and the LR scheduler in order to better track the
     # evolution of the LR independently of the optimizer.
     return optimizer, lr_schedule
class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001				,						_A		:     float = 0.9				,						_A		:     float = 0.999				,						_A		:     float = 1E-7				,						_A		:     bool = False				,						_A		:     float = 0.0				,						_A		:     Optional[List[str]] = None				,						_A		:     Optional[List[str]] = None				,						_A		:     str = "AdamWeightDecay"				,						**_A		:     Any				,						)		->   Optional[Any]:






       """simple docstring"""







       super().__init__(_A				,						_A				,						_A				,						_A				,						_A				,						_A				,						**_A				)
       lowercase :       Tuple       =			weight_decay_rate
       lowercase :       List[str]       =			include_in_weight_decay
       lowercase :       Optional[Any]       =			exclude_from_weight_decay






  @classmethod
  def  __a      (  cls		:     Tuple				,						_A		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       Optional[int]       =			{'''WarmUp''': WarmUp}
       return super(_A				,						cls				).from_config(_A				,						custom_objects=_A				)






  def  __a      (  self		:     Dict				,						_A		:     Tuple				,						_A		:     Dict				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       super(_A				,						self				)._prepare_local(_A				,						_A				,						_A				)
       lowercase :       List[Any]       =			tf.constant(
           self.weight_decay_rate				,						name='''adam_weight_decay_rate'''				)






  def  __a      (  self		:     Tuple				,						_A		:     Optional[int]				,						_A		:     Union[str, Any]				,						_A		:     List[Any]				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			self._do_use_weight_decay(var.name				)
       if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']				,						use_locking=self._use_locking				,						)
       return tf.no_op()






  def  __a      (  self		:     Union[str, Any]				,						_A		:     Any				,						_A		:     Optional[Any]=None				,						**_A		:     Tuple				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase :       Tuple       =			list(zip(*_A				)				)
       return super(_A				,						self				).apply_gradients(zip(_A				,						_A				)				,						name=_A				,						**_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     Optional[Any]				,						_A		:     str				,						_A		:     Union[str, Any]				)		->   Union[str, Any]:






       """simple docstring"""







       if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

       lowercase :       Any       =			apply_state or {}
       lowercase :       str       =			apply_state.get((var_device, var_dtype)				)
       if coefficients is None:
            lowercase :       List[Any]       =			self._fallback_apply_state(_A				,						_A				)
            lowercase :       List[Any]       =			coefficients

       return coefficients["lr_t"], {"apply_state": apply_state}






  def  __a      (  self		:     Tuple				,						_A		:     Union[str, Any]				,						_A		:     Tuple				,						_A		:     str=None				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       List[str]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       Optional[Any]       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_dense(_A				,						_A				,						**_A				)






  def  __a      (  self		:     Optional[int]				,						_A		:     List[Any]				,						_A		:     Dict				,						_A		:     Union[str, Any]				,						_A		:     str=None				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       Optional[int]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       str       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_sparse(_A				,						_A				,						_A				,						**_A				)






  def  __a      (  self		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			super().get_config()
       config.update({'''weight_decay_rate''': self.weight_decay_rate}				)
       return config






  def  __a      (  self		:     str				,						_A		:     Optional[int]				)		->   Tuple:






       """simple docstring"""







       if self.weight_decay_rate == 0:
            return False

       if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return True

       if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return False
       return True


class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			[]
       lowercase :       str       =			None






  @property
  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







       if self._accum_steps is None:
            lowercase :       Optional[Any]       =			tf.Variable(
                tf.constant(0				,						dtype=tf.intaa				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)

       return self._accum_steps.value()






  @property
  def  __a      (  self		:     int				)		->   List[Any]:






       """simple docstring"""







       if not self._gradients:
            raise ValueError('''The accumulator should be called first to initialize the gradients'''				)
       return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






  def __call__(  self		:     str				,						_A		:     int				)		->   str:






       """simple docstring"""







       if not self._gradients:
            lowercase :       Optional[Any]       =			self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(_A				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]				)
       if len(_A				) != len(self._gradients				):
            raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(_A				)}"""				)

       for accum_gradient, gradient in zip(self._gradients				,						_A				):
            if accum_gradient is not None and gradient is not None:
                 accum_gradient.assign_add(_A				)

       self._accum_steps.assign_add(1				)






  def  __a      (  self		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       if not self._gradients:
            return
       self._accum_steps.assign(0				)
       for gradient in self._gradients:
            if gradient is not None:
                 gradient.assign(tf.zeros_like(_A				)				)