





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

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



def      __lowerCamelCase   (							a_     :		str ,   a_     :		int ,   a_     :		List[str]							)			->      str:
      return max(a_ ,   min(a_ ,   a_							)							)



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



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		List[Any] ,   a_     :		Tuple ,   a_     :		Optional[Any]							)			->      Tuple:
      __SCREAMING_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(a_ ,   (original_slice, 0)							)
      return result



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



def      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		int							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  n % d
      return n - divisor






class 					_SCREAMING_SNAKE_CASE(   A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 3_50		,)			->      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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)			->      int:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE   :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							),
             )
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  add_overlap_rect(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,image.size							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  image.crop(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
             __SCREAMING_SNAKE_CASE   :int						=  translated_slice_x - (original_image_slice / 2)
             __SCREAMING_SNAKE_CASE   :int						=  max(0		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  squeeze_tile(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  to_input.size
             __SCREAMING_SNAKE_CASE   :List[str]						=  to_input.resize((tile_size, tile_size)		,Image.BICUBIC							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  super(SCREAMING_SNAKE_CASE__		,self							).__call__(image=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							).images[0]
             __SCREAMING_SNAKE_CASE   :Any						=  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,Image.BICUBIC							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  unsqueeze_tile(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,Image.BICUBIC							)
             __SCREAMING_SNAKE_CASE   :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'''							)
             __SCREAMING_SNAKE_CASE   :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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 75		,SCREAMING_SNAKE_CASE__ = 9.0		,SCREAMING_SNAKE_CASE__ = 50		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 32		,)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  Image.new('''RGB'''		,(image.size[0] * 4, image.size[1] * 4)							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  math.ceil(image.size[0] / tile_size							)
             __SCREAMING_SNAKE_CASE   :Dict						=  math.ceil(image.size[1] / tile_size							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  tcx * tcy
             __SCREAMING_SNAKE_CASE   :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      __lowerCamelCase   (							)			->      Any:
      # Run a demo
      __SCREAMING_SNAKE_CASE   :str						=  '''stabilityai/stable-diffusion-x4-upscaler'''
      __SCREAMING_SNAKE_CASE   :Any						=  StableDiffusionTiledUpscalePipeline.from_pretrained(a_ ,   revision='''fp16''' ,   torch_dtype=torch.floataa							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  pipe.to('''cuda'''							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  Image.open('''../../docs/source/imgs/diffusers_library.jpg'''							)

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

      __SCREAMING_SNAKE_CASE   :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"""
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


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      str							=  '''beit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=81_92		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11]		,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6]		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0.4		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2_55		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Any						=  vocab_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :str						=  layer_norm_eps

             __SCREAMING_SNAKE_CASE   :int						=  image_size
             __SCREAMING_SNAKE_CASE   :Tuple						=  patch_size
             __SCREAMING_SNAKE_CASE   :Any						=  num_channels
             __SCREAMING_SNAKE_CASE   :Any						=  use_mask_token
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_absolute_position_embeddings
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_relative_position_bias
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  use_shared_relative_position_bias
             __SCREAMING_SNAKE_CASE   :List[str]						=  layer_scale_init_value
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  drop_path_rate
             __SCREAMING_SNAKE_CASE   :str						=  use_mean_pooling
             # decode head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE   :Dict						=  out_indices
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  pool_scales
             # auxiliary head attributes (semantic segmentation)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  use_auxiliary_head
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  auxiliary_loss_weight
             __SCREAMING_SNAKE_CASE   :Dict						=  auxiliary_channels
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  auxiliary_num_convs
             __SCREAMING_SNAKE_CASE   :List[str]						=  auxiliary_concat_input
             __SCREAMING_SNAKE_CASE   :List[Any]						=  semantic_loss_ignore_index






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  version.parse('''1.11'''  )




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





             """simple docstring"""






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




       @property
       def     _UpperCamelCase     (						self							)			->      float:





             """simple docstring"""






             return 1E-4