






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    (      a :Optional[Any]	,							a :Optional[Any]	,							a :Any=[] )							->			Optional[int]:
 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 ) * 255
 a		  =      np.pad(a	,							mode='''linear_ramp'''	,							pad_width=a	,							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 						_a    (      a :Optional[Any]	,							a :Optional[int]	,							a :Union[str, Any] )							->			Any:
 return max(a	,							min(a	,							a ) )
def 						_a    (      a :[int]	,							a :[int]	,							a :[int] )							->			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 						_a    (      a :[int]	,							a :int	,							a :[int] )							->			Optional[int]:
 a		  =      list(a )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 a		  =      clamp_rect(a	,							[0, 0]	,							[image_size[0], image_size[1]] )
 return rect
def 						_a    (      a :List[str]	,							a :str	,							a :Optional[int]	,							a :List[Any] )							->			Dict:
 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(a	,							(original_slice, 0) )
 return result
def 						_a    (      a :Union[str, Any]	,							a :List[Any] )							->			Dict:
 a		  =      (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 a		  =      tile.crop(a )
 return tile
def 						_a    (      a :List[str]	,							a :Optional[Any] )							->			int:
 a		  =      n % d
 return n - divisor






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Any ,					__UpperCAmelCase      :						AutoencoderKL ,					__UpperCAmelCase      :						CLIPTextModel ,					__UpperCAmelCase      :						CLIPTokenizer ,					__UpperCAmelCase      :						UNetaDConditionModel ,					__UpperCAmelCase      :						DDPMScheduler ,					__UpperCAmelCase      :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,					__UpperCAmelCase      :						int = 350 ,					)    ->int:




       """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      :						Dict ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					**__UpperCAmelCase      :						Dict  )    ->Optional[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(__UpperCAmelCase ,					__UpperCAmelCase ,					image.size  )
       a		  =      image.crop(__UpperCAmelCase  )
       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 ,					__UpperCAmelCase  )
       a		  =      squeeze_tile(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      to_input.size
       a		  =      to_input.resize((tile_size, tile_size) ,					Image.BICUBIC  )
       a		  =      super(__UpperCAmelCase ,					self  ).__call__(image=__UpperCAmelCase ,					**__UpperCAmelCase  ).images[0]
       a		  =      upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,					Image.BICUBIC  )
       a		  =      unsqueeze_tile(__UpperCAmelCase ,					__UpperCAmelCase  )
       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=__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      :						Any ,					__UpperCAmelCase      :						Union[str, List[str]] ,					__UpperCAmelCase      :						Union[PIL.Image.Image, List[PIL.Image.Image]] ,					__UpperCAmelCase      :						int = 75 ,					__UpperCAmelCase      :						float = 9.0 ,					__UpperCAmelCase      :						int = 50 ,					__UpperCAmelCase      :						Optional[Union[str, List[str]]] = None ,					__UpperCAmelCase      :						Optional[int] = 1 ,					__UpperCAmelCase      :						float = 0.0 ,					__UpperCAmelCase      :						Optional[torch.Generator] = None ,					__UpperCAmelCase      :						Optional[torch.FloatTensor] = None ,					__UpperCAmelCase      :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,					__UpperCAmelCase      :						int = 1 ,					__UpperCAmelCase      :						int = 128 ,					__UpperCAmelCase      :						int = 32 ,					__UpperCAmelCase      :						int = 32 ,					)    ->Optional[int]:




       """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(__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 						_a    (      )							->			Dict:
 # Run a demo
 a		  =      '''stabilityai/stable-diffusion-x4-upscaler'''
 a		  =      StableDiffusionTiledUpscalePipeline.from_pretrained(a	,							revision='''fp16'''	,							torch_dtype=torch.floataa )
 a		  =      pipe.to('''cuda''' )
 a		  =      Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )

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

 a		  =      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 itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  SCREAMING_SNAKE_CASE    (datasets.BuilderConfig							):
     lowerCAmelCase					=       None




class  SCREAMING_SNAKE_CASE    (datasets.ArrowBasedBuilder							):
     lowerCAmelCase					=       PandasConfig


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.DatasetInfo(features=self.config.features)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          __A						: Dict      =       dl_manager.download_and_extract(self.config.data_files)
          if isinstance(_UpperCAmelCase						,	(str, list, tuple)):
               __A						: Union[str, Any]      =       data_files
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Optional[int]      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={'files': files})]
          __A						: Tuple      =       []
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Dict      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase						,	gen_kwargs={'files': files}))
          return splits


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __A						: List[str]      =       table_cast(_UpperCAmelCase						,	self.config.features.arrow_schema)
          return pa_table


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
               with open(_UpperCAmelCase						,	'rb') as f:
                    __A						: Optional[int]      =       pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
                    yield i, self._cast_table(_UpperCAmelCase)