
"""simple docstring"""







import os
from distutils.util import strtobool
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	for e in env_keys:
		__lowerCAmelCase      :						int						      =				int(os.environ.get(_UpperCamelCase	,  -1		)		)
		if val >= 0:
			return val
	return default
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=False		):
	__lowerCAmelCase      :						int						      =				os.environ.get(_UpperCamelCase	,  str(_UpperCamelCase		)		)
	return strtobool(_UpperCamelCase		) == 1  # As its name indicates `strtobool` actually returns an int...
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase="no"		):
	__lowerCAmelCase      :						Optional[int]						      =				os.environ.get(_UpperCamelCase	,  str(_UpperCamelCase		)		)
	return value






"""simple docstring"""







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 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	List[Any]           =     KandinskyVaaInpaintPipeline
		A_						:	Optional[int]           =     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:	Any           =     [
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:	Any           =     [
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:	Any           =     False

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

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

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

		@property
		def 							__lowerCamelCase							(				self			):
			return 1_00

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

			__lowerCAmelCase      :						Optional[int]						      =				{
			    '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,
			}

			__lowerCAmelCase      :						Any						      =				UNetaDConditionModel(**_SCREAMING_SNAKE_CASE			)
			return model

		@property
		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						Optional[Any]						      =				VQModel(**self.dummy_movq_kwargs			)
			return model

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.dummy_unet
			__lowerCAmelCase      :						Optional[Any]						      =				self.dummy_movq

			__lowerCAmelCase      :						Tuple						      =				DDIMScheduler(
			    num_train_timesteps=10_00	,		beta_schedule='linear'	,		beta_start=0.0_0085	,		beta_end=0.012	,		clip_sample=_SCREAMING_SNAKE_CASE	,		set_alpha_to_one=_SCREAMING_SNAKE_CASE	,		steps_offset=1	,		prediction_type='epsilon'	,		thresholding=_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						str						      =				{
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			__lowerCAmelCase      :						List[Any]						      =				floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				floats_tensor((1, self.text_embedder_hidden_size)	,		rng=random.Random(seed + 1			)			).to(
			    _SCREAMING_SNAKE_CASE			)
			# create init_image
			__lowerCAmelCase      :						int						      =				floats_tensor((1, 3, 64, 64)	,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				image.cpu().permute(0	,		2	,		3	,		1			)[0]
			__lowerCAmelCase      :						str						      =				Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE			)			).convert('RGB'			).resize((2_56, 2_56)			)
			# create mask
			__lowerCAmelCase      :						Dict						      =				np.ones((64, 64)	,		dtype=np.floataa			)
			__lowerCAmelCase      :						List[str]						      =				0

			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						Optional[int]						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						List[Any]						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				{
			    '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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'cpu'

			__lowerCAmelCase      :						Dict						      =				self.get_dummy_components()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Optional[Any]						      =				output.images

			__lowerCAmelCase      :						Any						      =				pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)	,		return_dict=_SCREAMING_SNAKE_CASE	,		)[0]

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

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

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

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




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

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'			)

			__lowerCAmelCase      :						Union[str, Any]						      =				load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'			)
			__lowerCAmelCase      :						Any						      =				np.ones((7_68, 7_68)	,		dtype=np.floataa			)
			__lowerCAmelCase      :						int						      =				0

			__lowerCAmelCase      :						str						      =				'a hat'

			__lowerCAmelCase      :						str						      =				KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'	,		torch_dtype=torch.floataa			)
			pipe_prior.to(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'	,		torch_dtype=torch.floataa			)
			__lowerCAmelCase      :						Tuple						      =				pipeline.to(_SCREAMING_SNAKE_CASE			)
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

			__lowerCAmelCase      :						Tuple						      =				pipeline(
			    image=_SCREAMING_SNAKE_CASE	,		mask_image=_SCREAMING_SNAKE_CASE	,		image_embeds=_SCREAMING_SNAKE_CASE	,		negative_image_embeds=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		num_inference_steps=1_00	,		height=7_68	,		width=7_68	,		output_type='np'	,		)

			__lowerCAmelCase      :						Optional[Any]						      =				output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)