
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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=KandinskyVaaInpaintPipeline
							lowercase_       :       Union[str, Any]       					=['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							lowercase_       :       Optional[int]       					=[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							lowercase_       :       Union[str, Any]       					=[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Tuple       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




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




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




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												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(**A__)
												return model




							@property
							def  A__      (				self):
												return {
												    "block_out_channels": [3_2, 6_4],
												    "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": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						VQModel(**self.dummy_movq_kwargs)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_unet
												lowercase							=						self.dummy_movq

												lowercase							=						DDIMScheduler(
												    num_train_timesteps=1_0_0_0   ,beta_schedule='''linear'''   ,beta_start=0.00085   ,beta_end=0.012   ,clip_sample=A__   ,set_alpha_to_one=A__   ,steps_offset=1   ,prediction_type='''epsilon'''   ,thresholding=A__   ,)

												lowercase							=						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1)).to(
												    A__)
												# create init_image
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1)[0]
												lowercase							=						Image.fromarray(np.uinta(A__)).convert('''RGB''').resize((2_5_6, 2_5_6))
												# create mask
												lowercase							=						np.ones((6_4, 6_4)   ,dtype=np.floataa)
												lowercase							=						0

												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.images

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[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, 6_4, 6_4, 3)

												lowercase							=						np.array(
												    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848])

												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  A__      (				self):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




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





							def  A__      (				self):
												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((7_6_8, 7_6_8)   ,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(A__)

												lowercase							=						KandinskyVaaInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,torch_dtype=torch.floataa)
												lowercase							=						pipeline.to(A__)
												pipeline.set_progress_bar_config(disable=A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase	,						lowercase							=						pipe_prior(
												    A__   ,generator=A__   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

												lowercase							=						pipeline(
												    image=A__   ,mask_image=A__   ,image_embeds=A__   ,negative_image_embeds=A__   ,generator=A__   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(A__   ,A__)






from functools import lru_cache



@lru_cache
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if num < 0:
										raise ValueError('''Number should not be negative.'''		)

					return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




