


import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class      __lowerCAmelCase  :






  """simple docstring"""





  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Tuple=1_3     ,					_lowerCAmelCase							:							Any=3_2     ,					_lowerCAmelCase							:							Dict=3     ,					_lowerCAmelCase							:							Tuple=4     ,					_lowerCAmelCase							:							Dict=[1_0, 2_0, 3_0, 4_0]     ,					_lowerCAmelCase							:							Optional[int]=[2, 2, 3, 2]     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Any=True     ,					_lowerCAmelCase							:							str=3_7     ,					_lowerCAmelCase							:							List[Any]="gelu"     ,					_lowerCAmelCase							:							List[Any]=1_0     ,					_lowerCAmelCase							:							str=0.02     ,					_lowerCAmelCase							:							Any=["stage2", "stage3", "stage4"]     ,					_lowerCAmelCase							:							Union[str, Any]=[2, 3, 4]     ,					_lowerCAmelCase							:							Optional[int]=None     ,					) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       image_size
     snake_case_            =       num_channels
     snake_case_            =       num_stages
     snake_case_            =       hidden_sizes
     snake_case_            =       depths
     snake_case_            =       is_training
     snake_case_            =       use_labels
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_labels
     snake_case_            =       initializer_range
     snake_case_            =       out_features
     snake_case_            =       out_indices
     snake_case_            =       scope



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     snake_case_            =       None
     if self.use_labels:
        snake_case_            =       ids_tensor([self.batch_size]     ,					self.num_labels	)

     snake_case_            =       self.get_config()
     return config, pixel_values, labels



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




     """simple docstring"""
     return ConvNextConfig(
         num_channels=self.num_channels     ,					hidden_sizes=self.hidden_sizes     ,					depths=self.depths     ,					num_stages=self.num_stages     ,					hidden_act=self.hidden_act     ,					is_decoder=_lowerCAmelCase     ,					initializer_range=self.initializer_range     ,					out_features=self.out_features     ,					out_indices=self.out_indices     ,					num_labels=self.num_labels     ,					)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							int	) ->			Dict:




     """simple docstring"""
     snake_case_            =       ConvNextModel(config=_lowerCAmelCase	)
     model.to(_lowerCAmelCase	)
     model.eval()
     snake_case_            =       model(_lowerCAmelCase	)
     # expected last hidden states: B, C, H // 32, W // 32
     self.parent.assertEqual(
         result.last_hidden_state.shape     ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,					)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       ConvNextForImageClassification(_lowerCAmelCase	)
     model.to(_lowerCAmelCase	)
     model.eval()
     snake_case_            =       model(_lowerCAmelCase     ,					labels=_lowerCAmelCase	)
     self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)	)



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							str	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ConvNextBackbone(config=_lowerCAmelCase	)
     model.to(_lowerCAmelCase	)
     model.eval()
     snake_case_            =       model(_lowerCAmelCase	)

     # verify hidden states
     self.parent.assertEqual(len(result.feature_maps	)     ,					len(config.out_features	)	)
     self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,					[self.batch_size, self.hidden_sizes[1], 4, 4]	)

     # verify channels
     self.parent.assertEqual(len(model.channels	)     ,					len(config.out_features	)	)
     self.parent.assertListEqual(model.channels     ,					config.hidden_sizes[1:]	)

     # verify backbone works with out_features=None
     snake_case_            =       None
     snake_case_            =       ConvNextBackbone(config=_lowerCAmelCase	)
     model.to(_lowerCAmelCase	)
     model.eval()
     snake_case_            =       model(_lowerCAmelCase	)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps	)     ,					1	)
     self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

     # verify channels
     self.parent.assertEqual(len(model.channels	)     ,					1	)
     self.parent.assertListEqual(model.channels     ,					[config.hidden_sizes[-1]]	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			int:




     """simple docstring"""
     snake_case_            =       self.prepare_config_and_inputs()
     snake_case_     ,     snake_case_     ,     snake_case_            =       config_and_inputs
     snake_case_            =       {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class      __lowerCAmelCase  (			a			,	a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							(
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE		     =							(
      {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE		     =							True
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			int:




     """simple docstring"""
     snake_case_            =       ConvNextModelTester(self	)
     snake_case_            =       ConfigTester(self     ,					config_class=_lowerCAmelCase     ,					has_text_modality=_lowerCAmelCase     ,					hidden_size=3_7	)



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




     """simple docstring"""
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()



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




     """simple docstring"""
     return



  @unittest.skip(reason="ConvNext does not use inputs_embeds"	)
  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     pass



  @unittest.skip(reason="ConvNext does not support input and output embeddings"	)
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Union[str, Any]:




     """simple docstring"""
     pass



  @unittest.skip(reason="ConvNext does not use feedforward chunking"	)
  def        lowerCAmelCase__	(  self							:							Dict	) ->			int:




     """simple docstring"""
     pass



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Dict:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        snake_case_            =       model_class(_lowerCAmelCase	)
        snake_case_            =       inspect.signature(model.forward	)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        snake_case_            =       [*signature.parameters.keys()]

        snake_case_            =       ["pixel_values"]
        self.assertListEqual(arg_names[:1]     ,					_lowerCAmelCase	)



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




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Optional[Any]:




     """simple docstring"""
     def check_hidden_states_output(_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							int	):
        snake_case_            =       model_class(_lowerCAmelCase	)
        model.to(_lowerCAmelCase	)
        model.eval()

        with torch.no_grad():
           snake_case_            =       model(**self._prepare_for_class(_lowerCAmelCase     ,					_lowerCAmelCase	)	)

        snake_case_            =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        snake_case_            =       self.model_tester.num_stages
        self.assertEqual(len(_lowerCAmelCase	)     ,					expected_num_stages + 1	)

        # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]	)     ,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,					)

     snake_case_     ,     snake_case_            =       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        snake_case_            =       True
        check_hidden_states_output(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        snake_case_            =       True

        check_hidden_states_output(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase	)



  @slow
  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case_            =       ConvNextModel.from_pretrained(_lowerCAmelCase	)
        self.assertIsNotNone(_lowerCAmelCase	)






def       _lowerCAmelCase    (				)->List[Any]:
   '''simple docstring'''






   snake_case_            =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
   return image


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






  """simple docstring"""





  @cached_property
  def        lowerCAmelCase__	(  self							:							int	) ->			Union[str, Any]:




     """simple docstring"""
     return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"	) if is_vision_available() else None



  @slow
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"	).to(_lowerCAmelCase	)

     snake_case_            =       self.default_image_processor
     snake_case_            =       prepare_img()
     snake_case_            =       image_processor(images=_lowerCAmelCase     ,					return_tensors="pt"	).to(_lowerCAmelCase	)

     # forward pass
     with torch.no_grad():
        snake_case_            =       model(**_lowerCAmelCase	)

     # verify the logits
     snake_case_            =       torch.Size((1, 1_0_0_0)	)
     self.assertEqual(outputs.logits.shape     ,					_lowerCAmelCase	)

     snake_case_            =       torch.tensor([-0.0_260, -0.4_739, 0.1_911]	).to(_lowerCAmelCase	)

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






@require_torch
class      __lowerCAmelCase  (			unittest.TestCase			,	a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							(ConvNextBackbone,) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE		     =							ConvNextConfig

  _SCREAMING_SNAKE_CASE		     =							False



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




     """simple docstring"""
     snake_case_            =       ConvNextModelTester(self	)








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      __lowerCAmelCase  (			tf.keras.optimizers.schedules.LearningRateSchedule       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							float     ,					_lowerCAmelCase							:							Callable     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							float = 1.0     ,					_lowerCAmelCase							:							str = None     ,					) ->			str:




     """simple docstring"""
     super().__init__()
     snake_case_            =       initial_learning_rate
     snake_case_            =       warmup_steps
     snake_case_            =       power
     snake_case_            =       decay_schedule_fn
     snake_case_            =       name



  def __call__(  self							:							Any     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """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`.
        snake_case_            =       tf.cast(_lowerCAmelCase     ,					tf.floataa	)
        snake_case_            =       tf.cast(self.warmup_steps     ,					tf.floataa	)
        snake_case_            =       global_step_float / warmup_steps_float
        snake_case_            =       self.initial_learning_rate * tf.math.pow(_lowerCAmelCase     ,					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=_lowerCAmelCase     ,					)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Dict:




     """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       _lowerCAmelCase    (				lowerCAmelCase_				:float		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 0.9		,    lowerCAmelCase_				:float = 0.9_9_9		,    lowerCAmelCase_				:float = 1e-8		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 1.0		,    lowerCAmelCase_				:Optional[List[str]] = None		,    )->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       tf.keras.optimizers.schedules.PolynomialDecay(
       initial_learning_rate=lowerCAmelCase_		,    decay_steps=num_train_steps - num_warmup_steps		,    end_learning_rate=init_lr * min_lr_ratio		,    power=lowerCAmelCase_		,    )
   if num_warmup_steps:
      snake_case_            =       WarmUp(
          initial_learning_rate=lowerCAmelCase_		,    decay_schedule_fn=lowerCAmelCase_		,    warmup_steps=lowerCAmelCase_		,    )
   if weight_decay_rate > 0.0:
      snake_case_            =       AdamWeightDecay(
          learning_rate=lowerCAmelCase_		,    weight_decay_rate=lowerCAmelCase_		,    beta_a=lowerCAmelCase_		,    beta_a=lowerCAmelCase_		,    epsilon=lowerCAmelCase_		,    clipnorm=lowerCAmelCase_		,    global_clipnorm=lowerCAmelCase_		,    exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]		,    include_in_weight_decay=lowerCAmelCase_		,    )
   else:
      snake_case_            =       tf.keras.optimizers.Adam(
          learning_rate=lowerCAmelCase_		,    beta_a=lowerCAmelCase_		,    beta_a=lowerCAmelCase_		,    epsilon=lowerCAmelCase_		,    clipnorm=lowerCAmelCase_		,    global_clipnorm=lowerCAmelCase_		,    )
   # 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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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




     """simple docstring"""
     super().__init__(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       weight_decay_rate
     snake_case_            =       include_in_weight_decay
     snake_case_            =       exclude_from_weight_decay



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       {"WarmUp": WarmUp}
     return super(_lowerCAmelCase     ,					cls	).from_config(_lowerCAmelCase     ,					custom_objects=_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     super(_lowerCAmelCase     ,					self	)._prepare_local(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     snake_case_            =       tf.constant(
         self.weight_decay_rate     ,					name="adam_weight_decay_rate"	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[int]=None     ,					**_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       list(zip(*_lowerCAmelCase	)	)
     return super(_lowerCAmelCase     ,					self	).apply_gradients(zip(_lowerCAmelCase     ,					_lowerCAmelCase	)     ,					name=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[int]	) ->			int:




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

     snake_case_            =       apply_state or {}
     snake_case_            =       apply_state.get((var_device, var_dtype)	)
     if coefficients is None:
        snake_case_            =       self._fallback_apply_state(_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       coefficients

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



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       self._decay_weights_op(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     with tf.control_dependencies([decay]	):
        return super(_lowerCAmelCase     ,					self	)._resource_apply_dense(_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       self._decay_weights_op(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     with tf.control_dependencies([decay]	):
        return super(_lowerCAmelCase     ,					self	)._resource_apply_sparse(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       super().get_config()
     config.update({"weight_decay_rate": self.weight_decay_rate}	)
     return config



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			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(_lowerCAmelCase     ,					_lowerCAmelCase	) is not None:
              return True

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






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							str	) ->			Dict:




     """simple docstring"""
     snake_case_            =       []
     snake_case_            =       None



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			List[Any]:




     """simple docstring"""
     if self._accum_steps is None:
        snake_case_            =       tf.Variable(
            tf.constant(0     ,					dtype=tf.intaa	)     ,					trainable=_lowerCAmelCase     ,					synchronization=tf.VariableSynchronization.ON_READ     ,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,					)

     return self._accum_steps.value()



  @property
  def        lowerCAmelCase__	(  self							:							Tuple	) ->			List[str]:




     """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							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]	) ->			Optional[Any]:




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

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

     self._accum_steps.assign_add(1	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Optional[Any]:




     """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(_lowerCAmelCase	)	)




