



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Any=[]							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     size[0] - overlap_pixels * 2
	lowerCAmelCase   =     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   =     np.ones((size_y, size_x)							,		dtype=np.uinta							) * 255
	lowerCAmelCase   =     np.pad(_SCREAMING_SNAKE_CASE							,		mode="""linear_ramp"""							,		pad_width=_SCREAMING_SNAKE_CASE							,		end_values=0							)

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						str:







	"""simple docstring"""
	return max(_SCREAMING_SNAKE_CASE							,		min(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						[int]							)      ->						List[str]:







	"""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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						[int]							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     list(_SCREAMING_SNAKE_CASE							)
	rect[0] -= overlap
	rect[1] -= overlap
	rect[2] += overlap
	rect[3] += overlap
	lowerCAmelCase   =     clamp_rect(_SCREAMING_SNAKE_CASE							,		[0, 0]							,		[image_size[0], image_size[1]]							)
	return rect






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     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(_SCREAMING_SNAKE_CASE							,		(original_slice, 0)							)
	return result






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     (original_image_slice * 4, 0, tile.size[0], tile.size[1])
	lowerCAmelCase   =     tile.crop(_SCREAMING_SNAKE_CASE							)
	return tile






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     n % d
	return n - divisor

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_ = 350     ,	)     ->     List[str]:
							super().__init__(
							    vae=A_     ,	text_encoder=A_     ,	tokenizer=A_     ,	unet=A_     ,	low_res_scheduler=A_     ,	scheduler=A_     ,	max_noise_level=A_     ,	)


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




						@torch.no_grad()
						def __call__( self     ,	A_     ,	A_     ,	A_ = 75     ,	A_ = 9.0     ,	A_ = 50     ,	A_ = None     ,	A_ = 1     ,	A_ = 0.0     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = 1     ,	A_ = 128     ,	A_ = 32     ,	A_ = 32     ,	)     ->     Dict:
							lowerCAmelCase   =     Image.new("""RGB"""     ,	(image.size[0] * 4, image.size[1] * 4)				)
							lowerCAmelCase   =     math.ceil(image.size[0] / tile_size				)
							lowerCAmelCase   =     math.ceil(image.size[1] / tile_size				)
							lowerCAmelCase   =     tcx * tcy
							lowerCAmelCase   =     0
							for y in range(A_				):
								for x in range(A_				):
									self._process_tile(
									    A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	prompt=A_     ,	num_inference_steps=A_     ,	guidance_scale=A_     ,	noise_level=A_     ,	negative_prompt=A_     ,	num_images_per_prompt=A_     ,	eta=A_     ,	generator=A_     ,	latents=A_     ,	)
									current_count += 1
									if callback is not None:
										callback({"""progress""": current_count / total_tile_count, """image""": final_image}				)
							return final_image







def 		_snake_case		( )      ->						Union[str, Any]:







	"""simple docstring"""
	# Run a demo
	lowerCAmelCase   =     """stabilityai/stable-diffusion-x4-upscaler"""
	lowerCAmelCase   =     StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE							,		revision="""fp16"""							,		torch_dtype=torch.floataa							)
	lowerCAmelCase   =     pipe.to("""cuda"""							)
	lowerCAmelCase   =     Image.open("""../../docs/source/imgs/diffusers_library.jpg"""							)

	def callback(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
		print(f'progress: {obj["progress"]:.4f}'							)
		obj["image"].save("""diffusers_library_progress.jpg"""							)

	lowerCAmelCase   =     pipe(image=_SCREAMING_SNAKE_CASE							,		prompt="""Black font, white background, vector"""							,		noise_level=40							,		callback=_SCREAMING_SNAKE_CASE							)
	final_image.save("""diffusers_library.jpg"""							)


if __name__ == "__main__":
				main()




'''simple docstring'''

from typing import List, Optional, Union

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

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							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:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

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

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)