
"""simple docstring"""







import math
from typing import Callable, List, Optional, Union

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

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=[]		):
	__lowerCAmelCase      :						List[Any]						      =				size[0] - overlap_pixels * 2
	__lowerCAmelCase      :						List[Any]						      =				size[1] - overlap_pixels * 2
	for letter in ["l", "r"]:
		if letter in remove_borders:
			size_x += overlap_pixels
	for letter in ["t", "b"]:
		if letter in remove_borders:
			size_y += overlap_pixels
	__lowerCAmelCase      :						List[Any]						      =				np.ones((size_y, size_x)	,  dtype=np.uinta		) * 255
	__lowerCAmelCase      :						List[Any]						      =				np.pad(_UpperCamelCase	,  mode='linear_ramp'	,  pad_width=_UpperCamelCase	,  end_values=0		)

	if "l" in remove_borders:
		__lowerCAmelCase      :						Union[str, Any]						      =				mask[:, overlap_pixels : mask.shape[1]]
	if "r" in remove_borders:
		__lowerCAmelCase      :						Dict						      =				mask[:, 0 : mask.shape[1] - overlap_pixels]
	if "t" in remove_borders:
		__lowerCAmelCase      :						Union[str, Any]						      =				mask[overlap_pixels : mask.shape[0], :]
	if "b" in remove_borders:
		__lowerCAmelCase      :						Optional[Any]						      =				mask[0 : mask.shape[0] - overlap_pixels, :]
	return mask
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	return max(_UpperCamelCase	,  min(_UpperCamelCase	,  _UpperCamelCase		)		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	return (
	    clamp(rect[0]	,  min[0]	,  max[0]		),
	    clamp(rect[1]	,  min[1]	,  max[1]		),
	    clamp(rect[2]	,  min[0]	,  max[0]		),
	    clamp(rect[3]	,  min[1]	,  max[1]		),
	)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				list(_UpperCamelCase		)
	rect[0] -= overlap
	rect[1] -= overlap
	rect[2] += overlap
	rect[3] += overlap
	__lowerCAmelCase      :						List[Any]						      =				clamp_rect(_UpperCamelCase	,  [0, 0]	,  [image_size[0], image_size[1]]		)
	return rect
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						List[Any]						      =				Image.new('RGB'	,  (tile.size[0] + original_slice, tile.size[1])		)
	result.paste(
	    original_image.resize((tile.size[0], tile.size[1])	,  Image.BICUBIC		).crop(
	        (slice_x, 0, slice_x + original_slice, tile.size[1])		)	,  (0, 0)	,  )
	result.paste(_UpperCamelCase	,  (original_slice, 0)		)
	return result
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
	__lowerCAmelCase      :						List[Any]						      =				tile.crop(_UpperCamelCase		)
	return tile
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				n % d
	return n - divisor




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 3_50	,		):
			super().__init__(
			    vae=_SCREAMING_SNAKE_CASE	,		text_encoder=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE	,		unet=_SCREAMING_SNAKE_CASE	,		low_res_scheduler=_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE	,		max_noise_level=_SCREAMING_SNAKE_CASE	,		)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						int						      =				(
			    min(image.size[0] - (tile_size + original_image_slice)	,		x * tile_size			),
			    min(image.size[1] - (tile_size + original_image_slice)	,		y * tile_size			),
			    min(image.size[0]	,		(x + 1) * tile_size			),
			    min(image.size[1]	,		(y + 1) * tile_size			),
			)
			__lowerCAmelCase      :						Dict						      =				add_overlap_rect(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		image.size			)
			__lowerCAmelCase      :						str						      =				image.crop(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
			__lowerCAmelCase      :						Optional[int]						      =				translated_slice_x - (original_image_slice / 2)
			__lowerCAmelCase      :						Any						      =				max(0	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				squeeze_tile(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				to_input.size
			__lowerCAmelCase      :						Optional[int]						      =				to_input.resize((tile_size, tile_size)	,		Image.BICUBIC			)
			__lowerCAmelCase      :						Union[str, Any]						      =				super(_SCREAMING_SNAKE_CASE	,		self			).__call__(image=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			).images[0]
			__lowerCAmelCase      :						int						      =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)	,		Image.BICUBIC			)
			__lowerCAmelCase      :						str						      =				unsqueeze_tile(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)	,		Image.BICUBIC			)
			__lowerCAmelCase      :						Union[str, Any]						      =				[]
			if x == 0:
				remove_borders.append('l'			)
			elif crop_rect[2] == image.size[0]:
				remove_borders.append('r'			)
			if y == 0:
				remove_borders.append('t'			)
			elif crop_rect[3] == image.size[1]:
				remove_borders.append('b'			)
			__lowerCAmelCase      :						List[str]						      =				Image.fromarray(
			    make_transparency_mask(
			        (upscaled_tile.size[0], upscaled_tile.size[1])	,		tile_border * 4	,		remove_borders=_SCREAMING_SNAKE_CASE			)	,		mode='L'	,		)
			final_image.paste(
			    _SCREAMING_SNAKE_CASE	,		(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)	,		_SCREAMING_SNAKE_CASE			)







		@torch.no_grad()
		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 75	,		_SCREAMING_SNAKE_CASE = 9.0	,		_SCREAMING_SNAKE_CASE = 50	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = 0.0	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = 1_28	,		_SCREAMING_SNAKE_CASE = 32	,		_SCREAMING_SNAKE_CASE = 32	,		):
			__lowerCAmelCase      :						Any						      =				Image.new('RGB'	,		(image.size[0] * 4, image.size[1] * 4)			)
			__lowerCAmelCase      :						Optional[int]						      =				math.ceil(image.size[0] / tile_size			)
			__lowerCAmelCase      :						int						      =				math.ceil(image.size[1] / tile_size			)
			__lowerCAmelCase      :						Any						      =				tcx * tcy
			__lowerCAmelCase      :						Any						      =				0
			for y in range(_SCREAMING_SNAKE_CASE			):
				for x in range(_SCREAMING_SNAKE_CASE			):
					self._process_tile(
					    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		prompt=_SCREAMING_SNAKE_CASE	,		num_inference_steps=_SCREAMING_SNAKE_CASE	,		guidance_scale=_SCREAMING_SNAKE_CASE	,		noise_level=_SCREAMING_SNAKE_CASE	,		negative_prompt=_SCREAMING_SNAKE_CASE	,		num_images_per_prompt=_SCREAMING_SNAKE_CASE	,		eta=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE	,		latents=_SCREAMING_SNAKE_CASE	,		)
					current_count += 1
					if callback is not None:
						callback({'progress': current_count / total_tile_count, 'image': final_image}			)
			return final_image



def    __lowerCAmelCase    ():
	# Run a demo
	__lowerCAmelCase      :						str						      =				'stabilityai/stable-diffusion-x4-upscaler'
	__lowerCAmelCase      :						List[str]						      =				StableDiffusionTiledUpscalePipeline.from_pretrained(_UpperCamelCase	,  revision='fp16'	,  torch_dtype=torch.floataa		)
	__lowerCAmelCase      :						int						      =				pipe.to('cuda'		)
	__lowerCAmelCase      :						List[Any]						      =				Image.open('../../docs/source/imgs/diffusers_library.jpg'		)

	def callback(_UpperCamelCase		):
		print(F"progress: {obj['progress']:.4f}"		)
		obj["image"].save('diffusers_library_progress.jpg'		)

	__lowerCAmelCase      :						str						      =				pipe(image=_UpperCamelCase	,  prompt='Black font, white background, vector'	,  noise_level=40	,  callback=_UpperCamelCase		)
	final_image.save('diffusers_library.jpg'		)


if __name__ == "__main__":
							main()

"""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			)