


"""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		(		lowercase						:						str		,							lowercase						:						str		,							lowercase						:						Dict=[]				)			->		Optional[Any]:


    """simple docstring"""





    snake_case					:   str         =					size[0] - overlap_pixels * 2
    snake_case					:   Optional[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					:   Tuple         =					np.ones((size_y, size_x)		,							dtype=np.uinta				) * 255
    snake_case					:   Dict         =					np.pad(lowercase		,							mode="linear_ramp"		,							pad_width=lowercase		,							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					:   Optional[int]         =					mask[:, 0 : mask.shape[1] - overlap_pixels]
    if "t" in remove_borders:
        snake_case					:   Optional[int]         =					mask[overlap_pixels : mask.shape[0], :]
    if "b" in remove_borders:
        snake_case					:   Tuple         =					mask[0 : mask.shape[0] - overlap_pixels, :]
    return mask







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						Tuple		,							lowercase						:						List[str]				)			->		Tuple:


    """simple docstring"""





    return max(lowercase		,							min(lowercase		,							lowercase				)				)







def 						__lowerCAmelCase		(		lowercase						:						[int]		,							lowercase						:						[int]		,							lowercase						:						[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 						__lowerCAmelCase		(		lowercase						:						[int]		,							lowercase						:						int		,							lowercase						:						[int]				)			->		Dict:


    """simple docstring"""





    snake_case					:   Dict         =					list(lowercase				)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    snake_case					:   str         =					clamp_rect(lowercase		,							[0, 0]		,							[image_size[0], image_size[1]]				)
    return rect







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						List[Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						Tuple				)			->		str:


    """simple docstring"""





    snake_case					:   List[str]         =					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(lowercase		,							(original_slice, 0)				)
    return result







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


    """simple docstring"""





    snake_case					:   Tuple         =					(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    snake_case					:   Tuple         =					tile.crop(lowercase				)
    return tile







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						Dict				)			->		str:


    """simple docstring"""





    snake_case					:   int         =					n % d
    return n - divisor







class _lowerCAmelCase       (   snake_case_			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = 350		,			)			->	Dict:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   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(UpperCamelCase__		,			UpperCamelCase__		,			image.size		)
       snake_case					:   Union[str, Any]         =					image.crop(UpperCamelCase__		)
       snake_case					:   int         =					((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       snake_case					:   int         =					translated_slice_x - (original_image_slice / 2)
       snake_case					:   Union[str, Any]         =					max(0		,			UpperCamelCase__		)
       snake_case					:   Optional[int]         =					squeeze_tile(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)
       snake_case					:   Any         =					to_input.size
       snake_case					:   Tuple         =					to_input.resize((tile_size, tile_size)		,			Image.BICUBIC		)
       snake_case					:   Any         =					super(UpperCamelCase__		,			self		).__call__(image=UpperCamelCase__		,			**UpperCamelCase__		).images[0]
       snake_case					:   Optional[Any]         =					upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,			Image.BICUBIC		)
       snake_case					:   Tuple         =					unsqueeze_tile(UpperCamelCase__		,			UpperCamelCase__		)
       snake_case					:   Optional[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					:   Dict         =					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		,			)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					Image.new("RGB"		,			(image.size[0] * 4, image.size[1] * 4)		)
       snake_case					:   List[str]         =					math.ceil(image.size[0] / tile_size		)
       snake_case					:   List[str]         =					math.ceil(image.size[1] / tile_size		)
       snake_case					:   str         =					tcx * tcy
       snake_case					:   Optional[Any]         =					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 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					"stabilityai/stable-diffusion-x4-upscaler"
    snake_case					:   Optional[Any]         =					StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase		,							revision="fp16"		,							torch_dtype=torch.floataa				)
    snake_case					:   Union[str, Any]         =					pipe.to("cuda"				)
    snake_case					:   Tuple         =					Image.open("../../docs/source/imgs/diffusers_library.jpg"				)

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

    snake_case					:   Any         =					pipe(image=lowercase		,							prompt="Black font, white background, vector"		,							noise_level=40		,							callback=lowercase				)
    final_image.save("diffusers_library.jpg"				)


if __name__ == "__main__":
  main()









"""simple docstring"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__snake_case         =					subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case         =					(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

__snake_case         =					"""|""".join(sys.argv[1:])
__snake_case         =					re.compile(RF'''^({joined_dirs}).*?\.py$''')

__snake_case         =					[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





