



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Any=[]							)      ->						str:







 """simple docstring"""
 lowerCAmelCase   =     size[0] - overlap_pixels * 2
 lowerCAmelCase   =     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
 lowerCAmelCase   =     np.ones((size_y, size_x)							,		dtype=np.uinta							) * 255
 lowerCAmelCase   =     np.pad(_SCREAMING_SNAKE_CASE							,		mode="""linear_ramp"""							,		pad_width=_SCREAMING_SNAKE_CASE							,		end_values=0							)

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						str:







 """simple docstring"""
 return max(_SCREAMING_SNAKE_CASE							,		min(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						[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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						[int]							)      ->						Optional[Any]:







 """simple docstring"""
 lowerCAmelCase   =     list(_SCREAMING_SNAKE_CASE							)
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 lowerCAmelCase   =     clamp_rect(_SCREAMING_SNAKE_CASE							,		[0, 0]							,		[image_size[0], image_size[1]]							)
 return rect






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						Dict:







 """simple docstring"""
 lowerCAmelCase   =     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(_SCREAMING_SNAKE_CASE							,		(original_slice, 0)							)
 return result






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						str:







 """simple docstring"""
 lowerCAmelCase   =     (original_image_slice * 4, 0, tile.size[0], tile.size[1])
 lowerCAmelCase   =     tile.crop(_SCREAMING_SNAKE_CASE							)
 return tile






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Any							)      ->						str:







 """simple docstring"""
 lowerCAmelCase   =     n % d
 return n - divisor

class __snake_case(				_lowerCAmelCase ):


      '''simple docstring'''



      def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_ = 350     ,	)     ->     List[str]:
       super().__init__(
           vae=A_     ,	text_encoder=A_     ,	tokenizer=A_     ,	unet=A_     ,	low_res_scheduler=A_     ,	scheduler=A_     ,	max_noise_level=A_     ,	)


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




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







def 		_snake_case		( )      ->						Union[str, Any]:







 """simple docstring"""
 lowerCAmelCase   =     """stabilityai/stable-diffusion-x4-upscaler"""
 lowerCAmelCase   =     StableDiffusionTiledUpscalePipeline.from_pretrained(_SCREAMING_SNAKE_CASE							,		revision="""fp16"""							,		torch_dtype=torch.floataa							)
 lowerCAmelCase   =     pipe.to("""cuda"""							)
 lowerCAmelCase   =     Image.open("""../../docs/source/imgs/diffusers_library.jpg"""							)

 def callback(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
  print(f'progress: {obj["progress"]:.4f}'							)
  obj["image"].save("""diffusers_library_progress.jpg"""							)

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


if __name__ == "__main__":
    main()




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)