'''simple docstring'''

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFCvtForImageClassification, TFCvtModel
  from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Dict						= self.config_class(**self.inputs_dict			)
        self.parent.assertTrue(hasattr(__lowerCAmelCase     ,       '''embed_dim'''			)			)
        self.parent.assertTrue(hasattr(__lowerCAmelCase     ,       '''num_heads'''			)			)



class       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=13     ,       __lowerCAmelCase=64     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=[16, 48, 96]     ,       __lowerCAmelCase=[1, 3, 6]     ,       __lowerCAmelCase=[1, 2, 10]     ,       __lowerCAmelCase=[7, 3, 3]     ,       __lowerCAmelCase=[4, 2, 2]     ,       __lowerCAmelCase=[2, 1, 1]     ,       __lowerCAmelCase=[2, 2, 2]     ,       __lowerCAmelCase=[False, False, True]     ,       __lowerCAmelCase=[0.0, 0.0, 0.0]     ,       __lowerCAmelCase=0.0_2     ,       __lowerCAmelCase=1E-12     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=2     ,       )  ->  Tuple:
        lowercase__    :   Union[str, Any]						= parent
        lowercase__    :   str						= batch_size
        lowercase__    :   int						= image_size
        lowercase__    :   int						= patch_sizes
        lowercase__    :   Dict						= patch_stride
        lowercase__    :   Dict						= patch_padding
        lowercase__    :   List[str]						= is_training
        lowercase__    :   Dict						= use_labels
        lowercase__    :   Any						= num_labels
        lowercase__    :   Optional[int]						= num_channels
        lowercase__    :   Optional[Any]						= embed_dim
        lowercase__    :   List[Any]						= num_heads
        lowercase__    :   Optional[int]						= stride_kv
        lowercase__    :   int						= depth
        lowercase__    :   str						= cls_token
        lowercase__    :   int						= attention_drop_rate
        lowercase__    :   str						= initializer_range
        lowercase__    :   str						= layer_norm_eps

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Dict						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__    :   Optional[Any]						= None
        if self.use_labels:
         # create a random int32 tensor of given shape
         lowercase__    :   Dict						= ids_tensor([self.batch_size]     ,       self.num_labels			)

        lowercase__    :   Tuple						= self.get_config()
        return config, pixel_values, labels

       def _lowerCAmelCase(						self			)  ->  str:
        return CvtConfig(
            image_size=self.image_size     ,       num_labels=self.num_labels     ,       num_channels=self.num_channels     ,       embed_dim=self.embed_dim     ,       num_heads=self.num_heads     ,       patch_sizes=self.patch_sizes     ,       patch_padding=self.patch_padding     ,       patch_stride=self.patch_stride     ,       stride_kv=self.stride_kv     ,       depth=self.depth     ,       cls_token=self.cls_token     ,       attention_drop_rate=self.attention_drop_rate     ,       initializer_range=self.initializer_range     ,       )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   Optional[int]						= TFCvtModel(config=__lowerCAmelCase			)
        lowercase__    :   Dict						= model(__lowerCAmelCase     ,       training=__lowerCAmelCase			)
        lowercase__    :   str						= (self.image_size, self.image_size)
        lowercase__			,							lowercase__    :   Tuple						= image_size[0], image_size[1]
        for i in range(len(self.depth			)			):
         lowercase__    :   int						= floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1			)
         lowercase__    :   Optional[int]						= floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.embed_dim[-1], height, width)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Optional[int]						= self.num_labels
        lowercase__    :   Optional[int]						= TFCvtForImageClassification(__lowerCAmelCase			)
        lowercase__    :   List[str]						= model(__lowerCAmelCase     ,       labels=__lowerCAmelCase     ,       training=__lowerCAmelCase			)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)			)


       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   Tuple						= self.prepare_config_and_inputs()
        lowercase__			,							lowercase__			,							lowercase__    :   List[str]						= config_and_inputs
        lowercase__    :   Dict						= {'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase  (						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE 		=   (
           {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
           if is_tf_available()
           else {}
       )
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Dict						= TFCvtModelTester(self			)
        lowercase__    :   str						= TFCvtConfigTester(self     ,       config_class=__lowerCAmelCase     ,       has_text_modality=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        self.config_tester.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()

       @unittest.skip(reason='''Cvt does not output attentions'''			)
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        pass

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

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

       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''			)			) == 0     ,       reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,       )
       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_dataset_conversion()

       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''			)			) == 0     ,       reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,       )
       @slow
       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_keras_fit()

       @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''			)
       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[str]						= tf.keras.mixed_precision.Policy('''mixed_float16'''			)
        tf.keras.mixed_precision.set_global_policy(__lowerCAmelCase			)
        super().test_keras_fit()
        tf.keras.mixed_precision.set_global_policy('''float32'''			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__			,							lowercase__    :   str						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   int						= model_class(__lowerCAmelCase			)
         lowercase__    :   List[str]						= inspect.signature(model.call			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase__    :   Any						= [*signature.parameters.keys()]

         lowercase__    :   Dict						= ['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  str:
        def check_hidden_states_output(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			):
         lowercase__    :   int						= model_class(__lowerCAmelCase			)

         lowercase__    :   Tuple						= model(**self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase			)			)
         lowercase__    :   Optional[int]						= outputs.hidden_states

         lowercase__    :   List[str]						= len(self.model_tester.depth			)
         self.assertEqual(len(__lowerCAmelCase			)     ,       __lowerCAmelCase			)

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]			)     ,       [
                 self.model_tester.embed_dim[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ]     ,       )

        lowercase__			,							lowercase__    :   Optional[int]						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   Optional[int]						= True
         check_hidden_states_output(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

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

         check_hidden_states_output(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   str						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Any						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase			)


       @slow
       def _lowerCAmelCase(						self			)  ->  int:
        for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__    :   List[Any]						= TFCvtModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)


def       __UpperCamelCase	(       ):
 lowercase__    :   Any						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
 return image





@require_tf
@require_vision
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(						self			)  ->  str:
        return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)


       @slow
       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Tuple						= TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

        lowercase__    :   Optional[Any]						= self.default_image_processor
        lowercase__    :   Optional[int]						= prepare_img()
        lowercase__    :   Dict						= image_processor(images=__lowerCAmelCase     ,       return_tensors='''tf'''			)

        # forward pass
        lowercase__    :   str						= model(**__lowerCAmelCase			)

        # verify the logits
        lowercase__    :   List[str]						= tf.TensorShape((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,       __lowerCAmelCase			)

        lowercase__    :   Optional[int]						= tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]			)
        self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,       __lowerCAmelCase     ,       atol=1E-4			)			)


'''simple docstring'''

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





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = None     ,       )  ->  Union[str, Any]:
        super().__init__()
        lowercase__    :   Any						= initial_learning_rate
        lowercase__    :   str						= warmup_steps
        lowercase__    :   Dict						= power
        lowercase__    :   int						= decay_schedule_fn
        lowercase__    :   Dict						= name

       def __call__(						self     ,       __lowerCAmelCase			)  ->  int:
        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__    :   Union[str, Any]						= tf.cast(__lowerCAmelCase     ,       tf.floataa			)
         lowercase__    :   Optional[int]						= tf.cast(self.warmup_steps     ,       tf.floataa			)
         lowercase__    :   Dict						= global_step_float / warmup_steps_float
         lowercase__    :   Dict						= 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			)  ->  List[str]:
        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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 0.9		,					UpperCAmelCase = 0.9_9_9		,					UpperCAmelCase = 1E-8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = None		,					):
 lowercase__    :   List[Any]						= tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=UpperCAmelCase		,					)
 if num_warmup_steps:
  lowercase__    :   Optional[Any]						= WarmUp(
      initial_learning_rate=UpperCAmelCase		,					decay_schedule_fn=UpperCAmelCase		,					warmup_steps=UpperCAmelCase		,					)
 if weight_decay_rate > 0.0:
  lowercase__    :   str						= AdamWeightDecay(
      learning_rate=UpperCAmelCase		,					weight_decay_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=UpperCAmelCase		,					)
 else:
  lowercase__    :   Any						= tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					)
 # 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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase = 0.0_0_1     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = 0.9_9_9     ,       __lowerCAmelCase = 1E-7     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = 0.0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "AdamWeightDecay"     ,       **__lowerCAmelCase     ,       )  ->  str:
        super().__init__(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   int						= weight_decay_rate
        lowercase__    :   Dict						= include_in_weight_decay
        lowercase__    :   List[str]						= exclude_from_weight_decay

       @classmethod
       def _lowerCAmelCase(						cls     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= {'''WarmUp''': WarmUp}
        return super(__lowerCAmelCase     ,       cls			).from_config(__lowerCAmelCase     ,       custom_objects=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        super(__lowerCAmelCase     ,       self			)._prepare_local(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= tf.constant(
            self.weight_decay_rate     ,       name='''adam_weight_decay_rate'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Any						= list(zip(*__lowerCAmelCase			)			)
        return super(__lowerCAmelCase     ,       self			).apply_gradients(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       name=__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

        lowercase__    :   Union[str, Any]						= apply_state or {}
        lowercase__    :   Optional[Any]						= apply_state.get((var_device, var_dtype)			)
        if coefficients is None:
         lowercase__    :   Union[str, Any]						= self._fallback_apply_state(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Optional[int]						= coefficients

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= 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     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= 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			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}			)
        return config


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= []
        lowercase__    :   Optional[int]						= None

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if self._accum_steps is None:
         lowercase__    :   int						= 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			)  ->  Union[str, Any]:
        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     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self._gradients:
         lowercase__    :   str						= 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			)  ->  Optional[Any]:
        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			)			)

