





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				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=[]				):
					_snake_case       :					List[Any]				       =   size[0] - overlap_pixels * 2
					_snake_case       :					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
					_snake_case       :					Optional[int]				       =   np.ones((size_y, size_x)  ,  dtype=np.uinta				) * 255
					_snake_case       :					Optional[Any]				       =   np.pad(UpperCAmelCase  ,  mode="linear_ramp"  ,  pad_width=UpperCAmelCase  ,  end_values=0				)

					if "l" in remove_borders:
										_snake_case       :					List[Any]				       =   mask[:, overlap_pixels : mask.shape[1]]
					if "r" in remove_borders:
										_snake_case       :					Tuple				       =   mask[:, 0 : mask.shape[1] - overlap_pixels]
					if "t" in remove_borders:
										_snake_case       :					Optional[Any]				       =   mask[overlap_pixels : mask.shape[0], :]
					if "b" in remove_borders:
										_snake_case       :					Optional[Any]				       =   mask[0 : mask.shape[0] - overlap_pixels, :]
					return mask







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return max(UpperCAmelCase  ,  min(UpperCAmelCase  ,  UpperCAmelCase				)				)







def    A				(							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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Union[str, Any]				       =   list(UpperCAmelCase				)
					rect[0] -= overlap
					rect[1] -= overlap
					rect[2] += overlap
					rect[3] += overlap
					_snake_case       :					Optional[int]				       =   clamp_rect(UpperCAmelCase  ,  [0, 0]  ,  [image_size[0], image_size[1]]				)
					return rect







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Union[str, 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    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
					_snake_case       :					Dict				       =   tile.crop(UpperCAmelCase				)
					return tile







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Dict				       =   n % d
					return n - divisor






class        _a(    __A    ):







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case = 3_5_0 ,						)      ->      Any:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						**__snake_case				)      ->      Any:


							'''simple docstring'''

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







		@torch.no_grad()
		def __call__(    self ,						__snake_case ,						__snake_case ,						__snake_case = 7_5 ,						__snake_case = 9.0 ,						__snake_case = 5_0 ,						__snake_case = None ,						__snake_case = 1 ,						__snake_case = 0.0 ,						__snake_case = None ,						__snake_case = None ,						__snake_case = None ,						__snake_case = 1 ,						__snake_case = 1_2_8 ,						__snake_case = 3_2 ,						__snake_case = 3_2 ,						)      ->      Optional[int]:


							'''simple docstring'''

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







def    A				(							):
					# Run a demo
					_snake_case       :					Optional[Any]				       =   "stabilityai/stable-diffusion-x4-upscaler"
					_snake_case       :					Optional[Any]				       =   StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCAmelCase  ,  revision="fp16"  ,  torch_dtype=torch.floataa				)
					_snake_case       :					Optional[int]				       =   pipe.to("cuda"				)
					_snake_case       :					int				       =   Image.open("../../docs/source/imgs/diffusers_library.jpg"				)

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

					_snake_case       :					Union[str, Any]				       =   pipe(image=UpperCAmelCase  ,  prompt="Black font, white background, vector"  ,  noise_level=40  ,  callback=UpperCAmelCase				)
					final_image.save("diffusers_library.jpg"				)


if __name__ == "__main__":
							main()






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def    A				(							UpperCAmelCase=None				):
					_snake_case       :					Union[str, Any]				       =   argparse.ArgumentParser(add_help=UpperCAmelCase  ,  allow_abbrev=UpperCAmelCase				)
					# The main config parser
					_snake_case       :					Tuple				       =   config_command_parser(UpperCAmelCase				)
					# The subparser to add commands to
					_snake_case       :					Any				       =   config_parser.add_subparsers(title="subcommands"  ,  dest="subcommand"				)

					# Then add other parsers with the parent parser
					default_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)
					update_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)

					return config_parser







def    A				(							):
					_snake_case       :					str				       =   get_config_parser()
					_snake_case       :					Union[str, Any]				       =   config_parser.parse_args()

					if not hasattr(UpperCAmelCase  ,  "func"				):
										config_parser.print_help()
										exit(1				)

					# Run
					args.func(UpperCAmelCase				)


if __name__ == "__main__":
							main()