




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=[])      ->					List[str]:
     a     =				size[0] - overlap_pixels * 2
     a     =				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
     a     =				np.ones((size_y, size_x)		, dtype=np.uinta) * 2_55
     a     =				np.pad(snake_case__		, mode="linear_ramp"		, pad_width=snake_case__		, end_values=0)

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     return max(snake_case__		, min(snake_case__		, snake_case__))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     a     =				list(snake_case__)
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     a     =				clamp_rect(snake_case__		, [0, 0]		, [image_size[0], image_size[1]])
     return rect

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[int]:
     a     =				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(snake_case__		, (original_slice, 0))
     return result

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     a     =				tile.crop(snake_case__)
     return tile

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Any:
     a     =				n % d
     return n - divisor




class       a__	( __lowerCAmelCase				):







     def __init__(     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A = 350  ,		)  ->   int:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		**A			)  ->   List[Any]:



          '''simple docstring'''



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







     @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  ,		)  ->   Union[str, Any]:



          '''simple docstring'''



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







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

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

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


if __name__ == "__main__":
       main()







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__				:      Tuple	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}




class       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """resnet"""
     a		:      Tuple		 =     ["""basic""", """bottleneck"""]







     def __init__(     self  ,		A=3  ,		A=64  ,		A=[256, 512, 1024, 2048]  ,		A=[3, 4, 6, 3]  ,		A="bottleneck"  ,		A="relu"  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Any:



          '''simple docstring'''



          super().__init__(**A			)
          if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
          a     =				num_channels
          a     =				embedding_size
          a     =				hidden_sizes
          a     =				depths
          a     =				layer_type
          a     =				hidden_act
          a     =				downsample_in_first_stage
          a     =				["stem"] + [F'''stage{idx}''' for idx in range(1  ,		len(A			) + 1			)]
          a  ,				a     =				get_aligned_output_features_output_indices(
              out_features=A  ,		out_indices=A  ,		stage_names=self.stage_names			)







class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-3

