


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_     (  snake_case			,							snake_case			,							snake_case=[]     )  ->				Any:
       lowercase__:   Optional[int]  			=   size[0] - overlap_pixels * 2
       lowercase__:   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
       lowercase__:   Tuple  			=   np.ones((size_y, size_x)			,							dtype=np.uinta     ) * 2_55
       lowercase__:   Dict  			=   np.pad(a_			,							mode='linear_ramp'			,							pad_width=a_			,							end_values=0     )

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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				str:
       return max(a_			,							min(a_			,							a_     )     )




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				str:
       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_     (  snake_case			,							snake_case			,							snake_case     )  ->				List[str]:
       lowercase__:   int  			=   list(a_     )
       rect[0] -= overlap
       rect[1] -= overlap
       rect[2] += overlap
       rect[3] += overlap
       lowercase__:   Union[str, Any]  			=   clamp_rect(a_			,							[0, 0]			,							[image_size[0], image_size[1]]     )
       return rect




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Tuple:
       lowercase__:   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(a_			,							(original_slice, 0)     )
       return result




def    snake_case_     (  snake_case			,							snake_case     )  ->				int:
       lowercase__:   Optional[int]  			=   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
       lowercase__:   Tuple  			=   tile.crop(a_     )
       return tile




def    snake_case_     (  snake_case			,							snake_case     )  ->				Any:
       lowercase__:   Tuple  			=   n % d
       return n - divisor





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 350				, )					->				List[str]:




        '''simple docstring'''
        super().__init__(
            vae=SCREAMING_SNAKE_CASE__				, text_encoder=SCREAMING_SNAKE_CASE__				, tokenizer=SCREAMING_SNAKE_CASE__				, unet=SCREAMING_SNAKE_CASE__				, low_res_scheduler=SCREAMING_SNAKE_CASE__				, scheduler=SCREAMING_SNAKE_CASE__				, max_noise_level=SCREAMING_SNAKE_CASE__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        torch.manual_seed(0 )
        lowercase__:   Tuple  			=   (
            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 ),
        )
        lowercase__:   Union[str, Any]  			=   add_overlap_rect(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, image.size )
        lowercase__:   Tuple  			=   image.crop(SCREAMING_SNAKE_CASE__ )
        lowercase__:   Optional[Any]  			=   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
        lowercase__:   int  			=   translated_slice_x - (original_image_slice / 2)
        lowercase__:   int  			=   max(0				, SCREAMING_SNAKE_CASE__ )
        lowercase__:   Any  			=   squeeze_tile(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ )
        lowercase__:   int  			=   to_input.size
        lowercase__:   List[str]  			=   to_input.resize((tile_size, tile_size)				, Image.BICUBIC )
        lowercase__:   Union[str, Any]  			=   super(SCREAMING_SNAKE_CASE__				, self ).__call__(image=SCREAMING_SNAKE_CASE__				, **SCREAMING_SNAKE_CASE__ ).images[0]
        lowercase__:   Any  			=   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				, Image.BICUBIC )
        lowercase__:   Union[str, Any]  			=   unsqueeze_tile(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ )
        lowercase__:   Optional[int]  			=   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)				, Image.BICUBIC )
        lowercase__:   Optional[int]  			=   []
        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' )
        lowercase__:   List[str]  			=   Image.fromarray(
            make_transparency_mask(
                (upscaled_tile.size[0], upscaled_tile.size[1])				, tile_border * 4				, remove_borders=SCREAMING_SNAKE_CASE__ )				, mode='L'				, )
        final_image.paste(
            SCREAMING_SNAKE_CASE__				, (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)				, SCREAMING_SNAKE_CASE__ )
 @torch.no_grad()
 def __call__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 75				, lowerCAmelCase__ = 9.0				, lowerCAmelCase__ = 50				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 0.0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 128				, lowerCAmelCase__ = 32				, lowerCAmelCase__ = 32				, )					->				List[Any]:




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





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

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

       lowercase__:   Optional[Any]  			=   pipe(image=a_			,							prompt='Black font, white background, vector'			,							noise_level=40			,							callback=a_     )
       final_image.save('diffusers_library.jpg'     )


if __name__ == "__main__":
 main()









"""simple docstring"""
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''' ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42 ,   dataset_name='''my_dataset'''							)}							),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42							)}							),
        SplitDict({'''train''': SplitInfo()}							),
    ] ,   )
def      __lowerCamelCase   (							a_     :		SplitDict							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  split_dict._to_yaml_list()
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SplitDict._from_yaml_list(a_							)
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            __SCREAMING_SNAKE_CASE   :int						=  None
            # the split name of split_dict takes over the name of the split info object
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  split_name
      assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info''' ,   [SplitInfo(), SplitInfo(dataset_name=a_							), SplitInfo(dataset_name='''my_dataset'''							)]							)
def      __lowerCamelCase   (							a_     :		Dict							)			->      Any:
      # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
      # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  asdict(SplitDict({'''train''': split_info}							)							)
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name