






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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=[]	)->  Tuple:





						"""simple docstring"""



						_UpperCAmelCase									=			size[0] - overlap_pixels * 2
						_UpperCAmelCase									=			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
						_UpperCAmelCase									=			np.ones((size_y, size_x) ,		dtype=np.uinta	) * 255
						_UpperCAmelCase									=			np.pad(__lowerCAmelCase ,		mode='linear_ramp' ,		pad_width=__lowerCAmelCase ,		end_values=0	)

						if "l" in remove_borders:
												_UpperCAmelCase									=			mask[:, overlap_pixels : mask.shape[1]]
						if "r" in remove_borders:
												_UpperCAmelCase									=			mask[:, 0 : mask.shape[1] - overlap_pixels]
						if "t" in remove_borders:
												_UpperCAmelCase									=			mask[overlap_pixels : mask.shape[0], :]
						if "b" in remove_borders:
												_UpperCAmelCase									=			mask[0 : mask.shape[0] - overlap_pixels, :]
						return mask


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Any:





						"""simple docstring"""



						return max(__lowerCAmelCase ,		min(__lowerCAmelCase ,		__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			list(__lowerCAmelCase	)
						rect[0] -= overlap
						rect[1] -= overlap
						rect[2] += overlap
						rect[3] += overlap
						_UpperCAmelCase									=			clamp_rect(__lowerCAmelCase ,		[0, 0] ,		[image_size[0], image_size[1]]	)
						return rect


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			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(__lowerCAmelCase ,		(original_slice, 0)	)
						return result


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Any:





						"""simple docstring"""



						_UpperCAmelCase									=			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
						_UpperCAmelCase									=			tile.crop(__lowerCAmelCase	)
						return tile


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			n % d
						return n - divisor





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = 350       ,    ):
							"""simple docstring"""







							super().__init__(
							    vae=UpperCAmelCase       ,    text_encoder=UpperCAmelCase       ,    tokenizer=UpperCAmelCase       ,    unet=UpperCAmelCase       ,    low_res_scheduler=UpperCAmelCase       ,    scheduler=UpperCAmelCase       ,    max_noise_level=UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			(
							    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    ),
							)
							_UpperCAmelCase									=			add_overlap_rect(UpperCAmelCase       ,    UpperCAmelCase       ,    image.size    )
							_UpperCAmelCase									=			image.crop(UpperCAmelCase    )
							_UpperCAmelCase									=			((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
							_UpperCAmelCase									=			translated_slice_x - (original_image_slice / 2)
							_UpperCAmelCase									=			max(0       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			squeeze_tile(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			to_input.size
							_UpperCAmelCase									=			to_input.resize((tile_size, tile_size)       ,    Image.BICUBIC    )
							_UpperCAmelCase									=			super(UpperCAmelCase       ,    self    ).__call__(image=UpperCAmelCase       ,    **UpperCAmelCase    ).images[0]
							_UpperCAmelCase									=			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)       ,    Image.BICUBIC    )
							_UpperCAmelCase									=			unsqueeze_tile(UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)       ,    Image.BICUBIC    )
							_UpperCAmelCase									=			[]
							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'    )
							_UpperCAmelCase									=			Image.fromarray(
							    make_transparency_mask(
							        (upscaled_tile.size[0], upscaled_tile.size[1])       ,    tile_border * 4       ,    remove_borders=UpperCAmelCase    )       ,    mode='L'       ,    )
							final_image.paste(
							    UpperCAmelCase       ,    (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)       ,    UpperCAmelCase    )

	@torch.no_grad()
	def __call__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = 75       ,    UpperCAmelCase = 9.0       ,    UpperCAmelCase = 50       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    UpperCAmelCase = 0.0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    UpperCAmelCase = 128       ,    UpperCAmelCase = 32       ,    UpperCAmelCase = 32       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			Image.new('RGB'       ,    (image.size[0] * 4, image.size[1] * 4)    )
							_UpperCAmelCase									=			math.ceil(image.size[0] / tile_size    )
							_UpperCAmelCase									=			math.ceil(image.size[1] / tile_size    )
							_UpperCAmelCase									=			tcx * tcy
							_UpperCAmelCase									=			0
							for y in range(UpperCAmelCase    ):
													for x in range(UpperCAmelCase    ):
																			self._process_tile(
																			    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    prompt=UpperCAmelCase       ,    num_inference_steps=UpperCAmelCase       ,    guidance_scale=UpperCAmelCase       ,    noise_level=UpperCAmelCase       ,    negative_prompt=UpperCAmelCase       ,    num_images_per_prompt=UpperCAmelCase       ,    eta=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    latents=UpperCAmelCase       ,    )
																			current_count += 1
																			if callback is not None:
																									callback({'progress': current_count / total_tile_count, 'image': final_image}    )
							return final_image



def __A    (      )->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			'stabilityai/stable-diffusion-x4-upscaler'
						_UpperCAmelCase									=			StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase ,		revision='fp16' ,		torch_dtype=torch.floataa	)
						_UpperCAmelCase									=			pipe.to('cuda'	)
						_UpperCAmelCase									=			Image.open('../../docs/source/imgs/diffusers_library.jpg'	)

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

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


if __name__ == "__main__":
		main()








from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

							if text_encoding is not None:
													encoding_image_processor.update(UpperCAmelCase    )

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
