


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




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = 1.0   ,    _lowerCamelCase = None   ,    ):
       super().__init__()
       a		:Union[str, Any]									=			initial_learning_rate
       a		:Tuple									=			warmup_steps
       a		:Tuple									=			power
       a		:Dict									=			decay_schedule_fn
       a		:str									=			name




 def __call__(						self   ,    _lowerCamelCase       ):
       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`.
             a		:Union[str, Any]									=			tf.cast(_lowerCamelCase   ,    tf.floataa       )
             a		:Union[str, Any]									=			tf.cast(self.warmup_steps   ,    tf.floataa       )
             a		:Union[str, Any]									=			global_step_float / warmup_steps_float
             a		:Optional[Any]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       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		(					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					,    ):




      """simple docstring"""





      a		: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:
            a		:Optional[int]									=			WarmUp(
                initial_learning_rate=UpperCAmelCase_					,    decay_schedule_fn=UpperCAmelCase_					,    warmup_steps=UpperCAmelCase_					,    )
      if weight_decay_rate > 0.0:
            a		: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:
            a		:Union[str, 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 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase = 0.001   ,    _lowerCamelCase = 0.9   ,    _lowerCamelCase = 0.999   ,    _lowerCamelCase = 1e-7   ,    _lowerCamelCase = False   ,    _lowerCamelCase = 0.0   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = "AdamWeightDecay"   ,    **_lowerCamelCase   ,    ):
       super().__init__(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Dict									=			weight_decay_rate
       a		:Union[str, Any]									=			include_in_weight_decay
       a		:List[str]									=			exclude_from_weight_decay




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls   ,    _lowerCamelCase       ):
       a		:List[str]									=			{'''WarmUp''': WarmUp}
       return super(_lowerCamelCase   ,    cls       ).from_config(_lowerCamelCase   ,    custom_objects=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       super(_lowerCamelCase   ,    self       )._prepare_local(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       a		:Union[str, Any]									=			tf.constant(
           self.weight_decay_rate   ,    name='''adam_weight_decay_rate'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:int									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None   ,    **_lowerCamelCase       ):
       a				,							a		:Optional[int]									=			list(zip(*_lowerCamelCase       )       )
       return super(_lowerCamelCase   ,    self       ).apply_gradients(zip(_lowerCamelCase   ,    _lowerCamelCase       )   ,    name=_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       if apply_state is None:
             return self._decayed_lr_t[var_dtype], {}

       a		:Tuple									=			apply_state or {}
       a		:int									=			apply_state.get((var_device, var_dtype)       )
       if coefficients is None:
             a		:List[str]									=			self._fallback_apply_state(_lowerCamelCase   ,    _lowerCamelCase       )
             a		:Union[str, Any]									=			coefficients

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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a				,							a		:int									=			self._get_lr(var.device   ,    var.dtype.base_dtype   ,    _lowerCamelCase       )
       a		:Union[str, Any]									=			self._decay_weights_op(_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       with tf.control_dependencies([decay]       ):
             return super(_lowerCamelCase   ,    self       )._resource_apply_dense(_lowerCamelCase   ,    _lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a				,							a		:Union[str, Any]									=			self._get_lr(var.device   ,    var.dtype.base_dtype   ,    _lowerCamelCase       )
       a		:Optional[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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			super().get_config()
       config.update({'''weight_decay_rate''': self.weight_decay_rate}       )
       return config




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       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 						_snake_case    ( _snake_case				):




 def __init__(						self       ):
       a		:Union[str, Any]									=			[]
       a		:int									=			None




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       if self._accum_steps is None:
             a		:Tuple									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       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       ):
       if not self._gradients:
             a		:List[Any]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       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       )       )









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 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			KandinskyVaaInpaintPipeline
 SCREAMING_SNAKE_CASE__ =			['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
 SCREAMING_SNAKE_CASE__ =			[
     'image_embeds',
     'negative_image_embeds',
     'image',
     'mask_image',
 ]
 SCREAMING_SNAKE_CASE__ =			[
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 SCREAMING_SNAKE_CASE__ =			False




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.time_input_dim




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.time_input_dim * 4




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 100




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )

       a		:Tuple									=			{
           '''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,
       }

       a		:int									=			UNetaDConditionModel(**_lowerCamelCase       )
       return model




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:Any									=			VQModel(**self.dummy_movq_kwargs       )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_unet
       a		:Any									=			self.dummy_movq

       a		:Dict									=			DDIMScheduler(
           num_train_timesteps=1000   ,    beta_schedule='''linear'''   ,    beta_start=0.0_0085   ,    beta_end=0.012   ,    clip_sample=_lowerCamelCase   ,    set_alpha_to_one=_lowerCamelCase   ,    steps_offset=1   ,    prediction_type='''epsilon'''   ,    thresholding=_lowerCamelCase   ,    )

       a		:Tuple									=			{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=0       ):
       a		:Tuple									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:int									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(seed + 1       )       ).to(
           _lowerCamelCase       )
       # create init_image
       a		:int									=			floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:Union[str, Any]									=			image.cpu().permute(0   ,    2   ,    3   ,    1       )[0]
       a		:Any									=			Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('''RGB'''       ).resize((256, 256)       )
       # create mask
       a		:Dict									=			np.ones((64, 64)   ,    dtype=np.floataa       )
       a		:Tuple									=			0

       if str(_lowerCamelCase       ).startswith('''mps'''       ):
             a		:Dict									=			torch.manual_seed(_lowerCamelCase       )
       else:
             a		:Union[str, Any]									=			torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
       a		:Tuple									=			{
           '''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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			'''cpu'''

       a		:List[Any]									=			self.get_dummy_components()

       a		:int									=			self.pipeline_class(**_lowerCamelCase       )
       a		:Tuple									=			pipe.to(_lowerCamelCase       )

       pipe.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:str									=			pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
       a		:Optional[int]									=			output.images

       a		:List[str]									=			pipe(
           **self.get_dummy_inputs(_lowerCamelCase       )   ,    return_dict=_lowerCamelCase   ,    )[0]

       a		:Optional[int]									=			image[0, -3:, -3:, -1]
       a		:Tuple									=			image_from_tuple[0, -3:, -3:, -1]

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

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

       a		:str									=			np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]       )

       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''       )

       a		:Tuple									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
       a		:List[Any]									=			np.ones((768, 768)   ,    dtype=np.floataa       )
       a		:Optional[Any]									=			0

       a		:List[Any]									=			'''a hat'''

       a		:int									=			KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''   ,    torch_dtype=torch.floataa       )
       pipe_prior.to(_lowerCamelCase       )

       a		:List[Any]									=			KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,    torch_dtype=torch.floataa       )
       a		:str									=			pipeline.to(_lowerCamelCase       )
       pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Union[str, Any]									=			torch.Generator(device='''cpu'''       ).manual_seed(0       )
       a				,							a		:Optional[Any]									=			pipe_prior(
           _lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=5   ,    negative_prompt=''''''   ,    ).to_tuple()

       a		:List[str]									=			pipeline(
           image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    image_embeds=_lowerCamelCase   ,    negative_image_embeds=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=100   ,    height=768   ,    width=768   ,    output_type='''np'''   ,    )

       a		:Any									=			output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_lowerCamelCase   ,    _lowerCamelCase       )





