


"""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 snake_case      (  tf.keras.optimizers.schedules.LearningRateSchedule):







		def __init__(					self      :       Tuple    ,   a__      :       float    ,   a__      :       Callable    ,   a__      :       int    ,   a__      :       float = 1.0    ,   a__      :       str = None    ,   )       ->						Optional[int]:





						'''simple docstring'''







						super().__init__()
						_A					  =			initial_learning_rate
						_A					  =			warmup_steps
						_A					  =			power
						_A					  =			decay_schedule_fn
						_A					  =			name







		def __call__(					self      :       Optional[Any]    ,   a__      :       int		)       ->						Dict:





						'''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`.
										_A					  =			tf.cast(a__    ,   tf.floataa		)
										_A					  =			tf.cast(self.warmup_steps    ,   tf.floataa		)
										_A					  =			global_step_float / warmup_steps_float
										_A					  =			self.initial_learning_rate * tf.math.pow(a__    ,   self.power		)
										return tf.cond(
										    global_step_float < warmup_steps_float    ,   lambda: warmup_learning_rate    ,   lambda: self.decay_schedule_fn(step - self.warmup_steps		)    ,   name=a__    ,   )







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = 0.0     ,			__lowercase = 0.9     ,			__lowercase = 0.999     ,			__lowercase = 1E-8     ,			__lowercase = None     ,			__lowercase = None     ,			__lowercase = 0.0     ,			__lowercase = 1.0     ,			__lowercase = None     ,			)	->	Union[str, Any]:
				_A					  =			tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=__lowercase     ,			decay_steps=num_train_steps - num_warmup_steps     ,			end_learning_rate=init_lr * min_lr_ratio     ,			power=__lowercase     ,			)
				if num_warmup_steps:
								_A					  =			WarmUp(
								    initial_learning_rate=__lowercase     ,			decay_schedule_fn=__lowercase     ,			warmup_steps=__lowercase     ,			)
				if weight_decay_rate > 0.0:
								_A					  =			AdamWeightDecay(
								    learning_rate=__lowercase     ,			weight_decay_rate=__lowercase     ,			beta_a=__lowercase     ,			beta_a=__lowercase     ,			epsilon=__lowercase     ,			clipnorm=__lowercase     ,			global_clipnorm=__lowercase     ,			exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]     ,			include_in_weight_decay=__lowercase     ,			)
				else:
								_A					  =			tf.keras.optimizers.Adam(
								    learning_rate=__lowercase     ,			beta_a=__lowercase     ,			beta_a=__lowercase     ,			epsilon=__lowercase     ,			clipnorm=__lowercase     ,			global_clipnorm=__lowercase     ,			)
				# 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      (  _UpperCamelCase):







		def __init__(					self      :       Optional[int]    ,   a__      :       Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1    ,   a__      :       float = 0.9    ,   a__      :       float = 0.9_9_9    ,   a__      :       float = 1E-7    ,   a__      :       bool = False    ,   a__      :       float = 0.0    ,   a__      :       Optional[List[str]] = None    ,   a__      :       Optional[List[str]] = None    ,   a__      :       str = "AdamWeightDecay"    ,   **a__      :       Union[str, Any]    ,   )       ->						str:





						'''simple docstring'''







						super().__init__(a__    ,   a__    ,   a__    ,   a__    ,   a__    ,   a__    ,   **a__		)
						_A					  =			weight_decay_rate
						_A					  =			include_in_weight_decay
						_A					  =			exclude_from_weight_decay







		@classmethod
		def  a_ (					cls      :       int    ,   a__      :       Dict		)       ->						int:





						'''simple docstring'''







						_A					  =			{"WarmUp": WarmUp}
						return super(a__    ,   cls		).from_config(a__    ,   custom_objects=a__		)







		def  a_ (					self      :       Any    ,   a__      :       Tuple    ,   a__      :       Dict    ,   a__      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						super(a__    ,   self		)._prepare_local(a__    ,   a__    ,   a__		)
						_A					  =			tf.constant(
						    self.weight_decay_rate    ,   name="adam_weight_decay_rate"		)







		def  a_ (					self      :       int    ,   a__      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       str		)       ->						str:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Dict    ,   a__      :       Any=None    ,   **a__      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A  ,		_A					  =			list(zip(*a__		)		)
						return super(a__    ,   self		).apply_gradients(zip(a__    ,   a__		)    ,   name=a__    ,   **a__		)







		def  a_ (					self      :       Optional[int]    ,   a__      :       Dict    ,   a__      :       Tuple    ,   a__      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







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

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

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







		def  a_ (					self      :       List[str]    ,   a__      :       Union[str, Any]    ,   a__      :       Optional[Any]    ,   a__      :       Optional[int]=None		)       ->						Tuple:





						'''simple docstring'''







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







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





						'''simple docstring'''







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







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





						'''simple docstring'''







						_A					  =			super().get_config()
						config.update({"weight_decay_rate": self.weight_decay_rate}		)
						return config







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





						'''simple docstring'''







						if self.weight_decay_rate == 0:
										return False

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

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





class snake_case      (  _UpperCamelCase):







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





						'''simple docstring'''







						_A					  =			[]
						_A					  =			None







		@property
		def  a_ (					self      :       str		)       ->						List[Any]:





						'''simple docstring'''







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

						return self._accum_steps.value()







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





						'''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      :       Tuple    ,   a__      :       Optional[Any]		)       ->						int:





						'''simple docstring'''







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

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

						self._accum_steps.assign_add(1		)







		def  a_ (					self      :       str		)       ->						int:





						'''simple docstring'''







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



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			StableDiffusionInpaintPipeline
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		__UpperCamelCase           =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__UpperCamelCase           =			frozenset(
		    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		__UpperCamelCase           =			frozenset([])







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





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			UNetaDConditionModel(
						    block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=9    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   attention_head_dim=(2, 4)    ,   use_linear_projection=a__    ,   )
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						torch.manual_seed(0		)
						_A					  =			AutoencoderKL(
						    block_out_channels=[32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=4    ,   sample_size=1_28    ,   )
						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
						_A					  =			CLIPTextModel(a__		)
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A					  =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple=0		)       ->						int:





						'''simple docstring'''







						_A					  =			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(a__		)		).to(a__		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		)[0]
						_A					  =			Image.fromarray(np.uinta(a__		)		).convert("RGB"		).resize((64, 64)		)
						_A					  =			Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
						if str(a__		).startswith("mps"		):
										_A					  =			torch.manual_seed(a__		)
						else:
										_A					  =			torch.Generator(device=a__		).manual_seed(a__		)
						_A					  =			{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": init_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs







		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.get_dummy_components()
						_A					  =			StableDiffusionInpaintPipeline(**a__		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			self.get_dummy_inputs(a__		)
						_A					  =			sd_pipe(**a__		).images
						_A					  =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]		)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2







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





						'''simple docstring'''







						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





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







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







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





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(a__    ,   safety_checker=a__		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 9E-3







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





						'''simple docstring'''







						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
						_A					  =			load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   torch_dtype=torch.floataa    ,   safety_checker=a__    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 5E-1







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





						'''simple docstring'''







						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"		)
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			PNDMScheduler.from_pretrained(a__    ,   subfolder="scheduler"		)
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(
						    a__    ,   safety_checker=a__    ,   scheduler=a__    ,   torch_dtype=torch.floataa    ,   )
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing(1		)
						pipe.enable_sequential_cpu_offload()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

						_A					  =			torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 10**9