



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 			_lowerCAmelCase   ( __snake_case    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			KandinskyVaaInpaintPipeline
			lowercase__      :  int      =			["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowercase__      :  Optional[Any]      =			[
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowercase__      :  List[str]      =			[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowercase__      :  Optional[int]      =			False





			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									return 32





			@property
			def 		snake_case__			(				self	:       Any   )						->					Any:
									"""simple docstring"""







									return 32





			@property
			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									return self.time_input_dim





			@property
			def 		snake_case__			(				self	:       Any   )						->					str:
									"""simple docstring"""







									return self.time_input_dim * 4





			@property
			def 		snake_case__			(				self	:       Tuple   )						->					str:
									"""simple docstring"""







									return 100





			@property
			def 		snake_case__			(				self	:       Optional[int]   )						->					Any:
									"""simple docstring"""







									torch.manual_seed(0   )

									__lowercase					=				{
									    """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,
									}

									__lowercase					=				UNetaDConditionModel(**_lowercase   )
									return model





			@property
			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									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 		snake_case__			(				self	:       List[str]   )						->					Tuple:
									"""simple docstring"""







									torch.manual_seed(0   )
									__lowercase					=				VQModel(**self.dummy_movq_kwargs   )
									return model





			def 		snake_case__			(				self	:       Dict   )						->					str:
									"""simple docstring"""







									__lowercase					=				self.dummy_unet
									__lowercase					=				self.dummy_movq

									__lowercase					=				DDIMScheduler(
									    num_train_timesteps=1_000			,     beta_schedule="""linear"""			,     beta_start=0.0_0085			,     beta_end=0.012			,     clip_sample=_lowercase			,     set_alpha_to_one=_lowercase			,     steps_offset=1			,     prediction_type="""epsilon"""			,     thresholding=_lowercase			,     )

									__lowercase					=				{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components





			def 		snake_case__			(				self	:       int			,     lowercase	:       Optional[Any]			,     lowercase	:       Dict=0   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(_lowercase   )   ).to(_lowercase   )
									__lowercase					=				floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(seed + 1   )   ).to(
									    _lowercase   )
									# create init_image
									__lowercase					=				floats_tensor((1, 3, 64, 64)			,     rng=random.Random(_lowercase   )   ).to(_lowercase   )
									__lowercase					=				image.cpu().permute(0			,     2			,     3			,     1   )[0]
									__lowercase					=				Image.fromarray(np.uinta(_lowercase   )   ).convert("""RGB"""   ).resize((256, 256)   )
									# create mask
									__lowercase					=				np.ones((64, 64)			,     dtype=np.floataa   )
									__lowercase					=				0

									if str(_lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(_lowercase   )
									else:
															__lowercase					=				torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
									__lowercase					=				{
									    """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 		snake_case__			(				self	:       Optional[int]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				"""cpu"""

									__lowercase					=				self.get_dummy_components()

									__lowercase					=				self.pipeline_class(**_lowercase   )
									__lowercase					=				pipe.to(_lowercase   )

									pipe.set_progress_bar_config(disable=_lowercase   )

									__lowercase					=				pipe(**self.get_dummy_inputs(_lowercase   )   )
									__lowercase					=				output.images

									__lowercase					=				pipe(
									    **self.get_dummy_inputs(_lowercase   )			,     return_dict=_lowercase			,     )[0]

									__lowercase					=				image[0, -3:, -3:, -1]
									__lowercase					=				image_from_tuple[0, -3:, -3:, -1]

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

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

									__lowercase					=				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 		snake_case__			(				self	:       Optional[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







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





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






			"""simple docstring"""





			def 		snake_case__			(				self	:       int   )						->					Any:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       Optional[int]   )						->					int:
									"""simple docstring"""







									__lowercase					=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""   )

									__lowercase					=				load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
									__lowercase					=				np.ones((768, 768)			,     dtype=np.floataa   )
									__lowercase					=				0

									__lowercase					=				"""a hat"""

									__lowercase					=				KandinskyVaaPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-prior"""			,     torch_dtype=torch.floataa   )
									pipe_prior.to(_lowercase   )

									__lowercase					=				KandinskyVaaInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""			,     torch_dtype=torch.floataa   )
									__lowercase					=				pipeline.to(_lowercase   )
									pipeline.set_progress_bar_config(disable=_lowercase   )

									__lowercase					=				torch.Generator(device="""cpu"""   ).manual_seed(0   )
									__lowercase					=				pipe_prior(
									    _lowercase			,     generator=_lowercase			,     num_inference_steps=5			,     negative_prompt=""""""			,     ).to_tuple()

									__lowercase					=				pipeline(
									    image=_lowercase			,     mask_image=_lowercase			,     image_embeds=_lowercase			,     negative_image_embeds=_lowercase			,     generator=_lowercase			,     num_inference_steps=100			,     height=768			,     width=768			,     output_type="""np"""			,     )

									__lowercase					=				output.images[0]

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

									assert_mean_pixel_difference(_lowercase			,     _lowercase   )










import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


UpperCamelCase__ =						"scheduler_config.json"
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  List[Any]      =			1
			lowercase__      :  Tuple      =			2
			lowercase__      :  Union[str, Any]      =			3
			lowercase__      :  Union[str, Any]      =			4
			lowercase__      :  str      =			5
			lowercase__      :  Any      =			6
			lowercase__      :  Any      =			7
			lowercase__      :  List[str]      =			8
			lowercase__      :  Union[str, Any]      =			9
			lowercase__      :  int      =			10
			lowercase__      :  List[str]      =			11
			lowercase__      :  List[Any]      =			12
			lowercase__      :  str      =			13
			lowercase__      :  Optional[int]      =			14





@dataclass
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  torch.FloatTensor





class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			SCHEDULER_CONFIG_NAME
			lowercase__      :  int      =			[]
			lowercase__      :  Dict      =			True





			@classmethod
			def 		snake_case__			(				cls	:       str			,     lowercase	:       Dict[str, Any] = None			,     lowercase	:       Optional[str] = None			,     lowercase	:       Any=False			,     **lowercase	:       List[str]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase	,			__lowercase	,			__lowercase					=				cls.load_config(
									    pretrained_model_name_or_path=lowercase			,     subfolder=lowercase			,     return_unused_kwargs=lowercase			,     return_commit_hash=lowercase			,     **lowercase			,     )
									return cls.from_config(lowercase			,     return_unused_kwargs=lowercase			,     **lowercase   )





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Union[str, os.PathLike]			,     lowercase	:       bool = False			,     **lowercase	:       List[str]   )						->					Optional[Any]:
									"""simple docstring"""







									self.save_config(save_directory=lowercase			,     push_to_hub=lowercase			,     **lowercase   )





			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict:
									"""simple docstring"""







									return self._get_compatibles()







			@classmethod
			def 		snake_case__			(				cls	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				list(set([cls.__name__] + cls._compatibles   )   )
									__lowercase					=				importlib.import_module(__name__.split("""."""   )[0]   )
									__lowercase					=				[
									    getattr(lowercase			,     lowercase   ) for c in compatible_classes_str if hasattr(lowercase			,     lowercase   )
									]
									return compatible_classes





