


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     KandinskyVaaInpaintPipeline
    lowerCamelCase    :   Dict					=     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
    lowerCamelCase    :   List[Any]					=     [
        'image_embeds',
        'negative_image_embeds',
        'image',
        'mask_image',
    ]
    lowerCamelCase    :   Union[str, Any]					=     [
        'generator',
        'height',
        'width',
        'latents',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase    :   str					=     False







    @property
    def  lowercase_							(				self      )							->	Union[str, Any]:
         return 32







    @property
    def  lowercase_							(				self      )							->	str:
         return 32







    @property
    def  lowercase_							(				self      )							->	Tuple:
         return self.time_input_dim







    @property
    def  lowercase_							(				self      )							->	Optional[Any]:
         return self.time_input_dim * 4







    @property
    def  lowercase_							(				self      )							->	Optional[Any]:
         return 1_00







    @property
    def  lowercase_							(				self      )							->	Any:
         torch.manual_seed(0      )

         __lowerCamelCase  :      str														=						{
             'in_channels': 9,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'image',
             'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
             'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
             'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
             'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
             'layers_per_block': 1,
             'encoder_hid_dim': self.text_embedder_hidden_size,
             'encoder_hid_dim_type': 'image_proj',
             'cross_attention_dim': self.cross_attention_dim,
             'attention_head_dim': 4,
             'resnet_time_scale_shift': 'scale_shift',
             'class_embed_type': None,
         }

         __lowerCamelCase  :      Union[str, Any]														=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE_      )
         return model







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }







    @property
    def  lowercase_							(				self      )							->	Optional[int]:
         torch.manual_seed(0      )
         __lowerCamelCase  :      List[Any]														=						VQModel(**self.dummy_movq_kwargs      )
         return model







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.dummy_unet
         __lowerCamelCase  :      str														=						self.dummy_movq

         __lowerCamelCase  :      List[str]														=						DDIMScheduler(
             num_train_timesteps=10_00     ,	beta_schedule='linear'     ,	beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	clip_sample=SCREAMING_SNAKE_CASE_     ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_     ,	steps_offset=1     ,	prediction_type='epsilon'     ,	thresholding=SCREAMING_SNAKE_CASE_     ,	)

         __lowerCamelCase  :      Optional[Any]														=						{
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=0      )							->	Tuple:
         __lowerCamelCase  :      Union[str, Any]														=						floats_tensor((1, self.text_embedder_hidden_size)     ,	rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						floats_tensor((1, self.text_embedder_hidden_size)     ,	rng=random.Random(seed + 1      )      ).to(
             SCREAMING_SNAKE_CASE_      )
         # create init_image
         __lowerCamelCase  :      Optional[Any]														=						floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
         __lowerCamelCase  :      Optional[int]														=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_      )      ).convert('RGB'      ).resize((2_56, 2_56)      )
         # create mask
         __lowerCamelCase  :      Optional[Any]														=						np.ones((64, 64)     ,	dtype=np.floataa      )
         __lowerCamelCase  :      Dict														=						0

         if str(SCREAMING_SNAKE_CASE_      ).startswith('mps'      ):
              __lowerCamelCase  :      Any														=						torch.manual_seed(SCREAMING_SNAKE_CASE_      )
         else:
              __lowerCamelCase  :      Optional[int]														=						torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						{
             'image': init_image,
             'mask_image': mask,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 64,
             'width': 64,
             'num_inference_steps': 2,
             'guidance_scale': 4.0,
             'output_type': 'np',
         }
         return inputs







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[str]														=						'cpu'

         __lowerCamelCase  :      int														=						self.get_dummy_components()

         __lowerCamelCase  :      Optional[int]														=						self.pipeline_class(**SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						pipe.to(SCREAMING_SNAKE_CASE_      )

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )      )
         __lowerCamelCase  :      List[str]														=						output.images

         __lowerCamelCase  :      Dict														=						pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)[0]

         __lowerCamelCase  :      int														=						image[0, -3:, -3:, -1]
         __lowerCamelCase  :      Optional[Any]														=						image_from_tuple[0, -3:, -3:, -1]

         print(f'image.shape {image.shape}'      )

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase  :      Dict														=						np.array(
             [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]      )

         assert (
             np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
         ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
         ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







    def  lowercase_							(				self      )							->	List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







@slow
@require_torch_gpu
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      str														=						load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'      )

         __lowerCamelCase  :      Optional[Any]														=						load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
         __lowerCamelCase  :      str														=						np.ones((7_68, 7_68)     ,	dtype=np.floataa      )
         __lowerCamelCase  :      str														=						0

         __lowerCamelCase  :      Tuple														=						'a hat'

         __lowerCamelCase  :      Union[str, Any]														=						KandinskyVaaPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'     ,	torch_dtype=torch.floataa      )
         pipe_prior.to(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						KandinskyVaaInpaintPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-decoder-inpaint'     ,	torch_dtype=torch.floataa      )
         __lowerCamelCase  :      Optional[Any]														=						pipeline.to(SCREAMING_SNAKE_CASE_      )
         pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						torch.Generator(device='cpu'      ).manual_seed(0      )
         __lowerCamelCase							,			__lowerCamelCase  :      str														=						pipe_prior(
             SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

         __lowerCamelCase  :      Optional[Any]														=						pipeline(
             image=SCREAMING_SNAKE_CASE_     ,	mask_image=SCREAMING_SNAKE_CASE_     ,	image_embeds=SCREAMING_SNAKE_CASE_     ,	negative_image_embeds=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=1_00     ,	height=7_68     ,	width=7_68     ,	output_type='np'     ,	)

         __lowerCamelCase  :      Optional[int]														=						output.images[0]

         assert image.shape == (7_68, 7_68, 3)

         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )










'''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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Any:
         super().__init__()
         __lowerCamelCase  :      Optional[Any]														=						initial_learning_rate
         __lowerCamelCase  :      Optional[Any]														=						warmup_steps
         __lowerCamelCase  :      Union[str, Any]														=						power
         __lowerCamelCase  :      Optional[int]														=						decay_schedule_fn
         __lowerCamelCase  :      Any														=						name







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         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`.
              __lowerCamelCase  :      str														=						tf.cast(SCREAMING_SNAKE_CASE_     ,	tf.floataa      )
              __lowerCamelCase  :      Optional[int]														=						tf.cast(self.warmup_steps     ,	tf.floataa      )
              __lowerCamelCase  :      List[Any]														=						global_step_float / warmup_steps_float
              __lowerCamelCase  :      Optional[Any]														=						self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_     ,	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=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	Optional[Any]:
         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_       :							float ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 0.9 ,       UpperCAmelCase_       :							float = 0.999 ,       UpperCAmelCase_       :							float = 1e-8 ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 1.0 ,       UpperCAmelCase_       :							Optional[List[str]] = None ,       )	->						int:
     __lowerCamelCase  :      int														=						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:
          __lowerCamelCase  :      str														=						WarmUp(
              initial_learning_rate=UpperCAmelCase_ ,       decay_schedule_fn=UpperCAmelCase_ ,       warmup_steps=UpperCAmelCase_ ,       )
     if weight_decay_rate > 0.0:
          __lowerCamelCase  :      List[Any]														=						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:
          __lowerCamelCase  :      Tuple														=						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_    (_UpperCAmelCase    ):





    """simple docstring"""






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







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Any														=						{'WarmUp': WarmUp}
         return super(SCREAMING_SNAKE_CASE_     ,	cls      ).from_config(SCREAMING_SNAKE_CASE_     ,	custom_objects=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         super(SCREAMING_SNAKE_CASE_     ,	self      )._prepare_local(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tf.constant(
             self.weight_decay_rate     ,	name='adam_weight_decay_rate'      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      Tuple														=						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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						list(zip(*SCREAMING_SNAKE_CASE_      )      )
         return super(SCREAMING_SNAKE_CASE_     ,	self      ).apply_gradients(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )     ,	name=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if apply_state is None:
              return self._decayed_lr_t[var_dtype], {}

         __lowerCamelCase  :      Optional[int]														=						apply_state or {}
         __lowerCamelCase  :      Dict														=						apply_state.get((var_device, var_dtype)      )
         if coefficients is None:
              __lowerCamelCase  :      List[Any]														=						self._fallback_apply_state(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Optional[Any]														=						coefficients

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_dense(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_sparse(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}      )
         return config







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return True

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







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						[]
         __lowerCamelCase  :      Optional[Any]														=						None







    @property
    def  lowercase_							(				self      )							->	List[str]:
         if self._accum_steps is None:
              __lowerCamelCase  :      Tuple														=						tf.Variable(
                  tf.constant(0     ,	dtype=tf.intaa      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)

         return self._accum_steps.value()







    @property
    def  lowercase_							(				self      )							->	List[str]:
         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     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if not self._gradients:
              __lowerCamelCase  :      List[str]														=						self.step  # Create the step variable.
              self._gradients.extend(
                  [
                      tf.Variable(
                          tf.zeros_like(SCREAMING_SNAKE_CASE_      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)
                      if gradient is not None
                      else gradient
                      for gradient in gradients
                  ]      )
         if len(SCREAMING_SNAKE_CASE_      ) != len(self._gradients      ):
              raise ValueError(f'Expected {len(self._gradients      )} gradients, but got {len(SCREAMING_SNAKE_CASE_      )}'      )

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

         self._accum_steps.assign_add(1      )







    def  lowercase_							(				self      )							->	int:
         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(SCREAMING_SNAKE_CASE_      )      )






