


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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					(     lowerCamelCase__       ,   unittest.TestCase	):
					lowerCAmelCase      :str   =					KandinskyVaaImgaImgPipeline
					lowerCAmelCase      :Any   =					['''image_embeds''', '''negative_image_embeds''', '''image''']
					lowerCAmelCase      :int   =					[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					lowerCAmelCase      :Union[str, Any]   =					[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase      :Tuple   =					False




					@property
					def 				snake_case__						(      self):
								return 32




					@property
					def 				snake_case__						(      self):
								return 32




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




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




					@property
					def 				snake_case__						(      self):
								return 100




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

								UpperCAmelCase__  :					int        =     {
								    '''in_channels''': 4,
								    # 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,
								}

								UpperCAmelCase__  :					Dict        =     UNetaDConditionModel(**__lowerCamelCase)
								return model




					@property
					def 				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 				snake_case__						(      self):
								torch.manual_seed(0)
								UpperCAmelCase__  :					Union[str, Any]        =     VQModel(**self.dummy_movq_kwargs)
								return model




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.dummy_unet
								UpperCAmelCase__  :					Any        =     self.dummy_movq

								UpperCAmelCase__  :					Any        =     {
								    '''num_train_timesteps''': 1000,
								    '''beta_schedule''': '''linear''',
								    '''beta_start''': 0.00085,
								    '''beta_end''': 0.012,
								    '''clip_sample''': False,
								    '''set_alpha_to_one''': False,
								    '''steps_offset''': 0,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								}

								UpperCAmelCase__  :					Optional[int]        =     DDIMScheduler(**__lowerCamelCase)

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

								return components




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Optional[int]        =     floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(__lowerCamelCase)).to(__lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(seed + 1)).to(
								    __lowerCamelCase)
								# create init_image
								UpperCAmelCase__  :					Any        =     floats_tensor((1, 3, 64, 64)							,							rng=random.Random(__lowerCamelCase)).to(__lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     image.cpu().permute(0							,							2							,							3							,							1)[0]
								UpperCAmelCase__  :					Union[str, Any]        =     Image.fromarray(np.uinta(__lowerCamelCase)).convert("""RGB""").resize((256, 256))

								if str(__lowerCamelCase).startswith("""mps"""):
											UpperCAmelCase__  :					Optional[Any]        =     torch.manual_seed(__lowerCamelCase)
								else:
											UpperCAmelCase__  :					List[str]        =     torch.Generator(device=__lowerCamelCase).manual_seed(__lowerCamelCase)
								UpperCAmelCase__  :					Union[str, Any]        =     {
								    '''image''': init_image,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 64,
								    '''width''': 64,
								    '''num_inference_steps''': 10,
								    '''guidance_scale''': 7.0,
								    '''strength''': 0.2,
								    '''output_type''': '''np''',
								}
								return inputs




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     '''cpu'''

								UpperCAmelCase__  :					int        =     self.get_dummy_components()

								UpperCAmelCase__  :					List[Any]        =     self.pipeline_class(**__lowerCamelCase)
								UpperCAmelCase__  :					int        =     pipe.to(__lowerCamelCase)

								pipe.set_progress_bar_config(disable=__lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     pipe(**self.get_dummy_inputs(__lowerCamelCase))
								UpperCAmelCase__  :					Union[str, Any]        =     output.images

								UpperCAmelCase__  :					Tuple        =     pipe(
								    **self.get_dummy_inputs(__lowerCamelCase)							,							return_dict=__lowerCamelCase							,							)[0]

								UpperCAmelCase__  :					str        =     image[0, -3:, -3:, -1]
								UpperCAmelCase__  :					Union[str, Any]        =     image_from_tuple[0, -3:, -3:, -1]

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

								UpperCAmelCase__  :					Union[str, Any]        =     np.array(
								    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263])
								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()}'''


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




					def 				snake_case__						(      self):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_img2img_frog.npy""")

								UpperCAmelCase__  :					Optional[Any]        =     load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
								UpperCAmelCase__  :					Union[str, Any]        =     '''A red cartoon frog, 4k'''

								UpperCAmelCase__  :					List[Any]        =     KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""							,							torch_dtype=torch.floataa)
								pipe_prior.to(__lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     KandinskyVaaImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder"""							,							torch_dtype=torch.floataa)
								UpperCAmelCase__  :					Any        =     pipeline.to(__lowerCamelCase)

								pipeline.set_progress_bar_config(disable=__lowerCamelCase)

								UpperCAmelCase__  :					Optional[Any]        =     torch.Generator(device="""cpu""").manual_seed(0)
								UpperCAmelCase__  :					List[str]        =     pipe_prior(
								    __lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=5							,							negative_prompt=""""""							,							).to_tuple()

								UpperCAmelCase__  :					str        =     pipeline(
								    image=__lowerCamelCase							,							image_embeds=__lowerCamelCase							,							negative_image_embeds=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=100							,							height=768							,							width=768							,							strength=0.2							,							output_type="""np"""							,							)

								UpperCAmelCase__  :					List[Any]        =     output.images[0]

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

								assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase)



'''simple docstring'''



import warnings
from functools import wraps
from typing import Callable







def     _UpperCamelCase      (		UpperCamelCase__					):

			@wraps(UpperCamelCase__					)
			def _inner_fn(*UpperCamelCase__ ,      **UpperCamelCase__					):
						warnings.warn(
						    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') ,      UpperCamelCase__ ,      )
						return fn(*UpperCamelCase__ ,      **UpperCamelCase__					)

			return _inner_fn