




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		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   str=[]	)     ->   List[Any]:




 """simple docstring"""







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

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   List[Any]	)     ->   Tuple:




 """simple docstring"""







 return max(_lowerCAmelCase						,							min(_lowerCAmelCase						,							_lowerCAmelCase	)	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   [int]						,							_lowerCAmelCase							:   [int]						,							_lowerCAmelCase							:   [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 						lowerCAmelCase		(		_lowerCAmelCase							:   [int]						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   [int]	)     ->   Union[str, Any]:




 """simple docstring"""







 UpperCAmelCase__							 =						list(_lowerCAmelCase	)
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 UpperCAmelCase__							 =						clamp_rect(_lowerCAmelCase						,							[0, 0]						,							[image_size[0], image_size[1]]	)
 return rect



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Union[str, Any]	)     ->   Any:




 """simple docstring"""







 UpperCAmelCase__							 =						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(_lowerCAmelCase						,							(original_slice, 0)	)
 return result



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Optional[int]	)     ->   int:




 """simple docstring"""







 UpperCAmelCase__							 =						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
 UpperCAmelCase__							 =						tile.crop(_lowerCAmelCase	)
 return tile



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Tuple	)     ->   Union[str, Any]:




 """simple docstring"""







 UpperCAmelCase__							 =						n % d
 return n - divisor
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :AutoencoderKL ,      lowerCamelCase   :CLIPTextModel ,      lowerCamelCase   :CLIPTokenizer ,      lowerCamelCase   :UNetaDConditionModel ,      lowerCamelCase   :DDPMScheduler ,      lowerCamelCase   :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,      lowerCamelCase   :int = 350 ,      )     ->			Optional[Any]:
       super().__init__(
           vae=lowerCamelCase ,      text_encoder=lowerCamelCase ,      tokenizer=lowerCamelCase ,      unet=lowerCamelCase ,      low_res_scheduler=lowerCamelCase ,      scheduler=lowerCamelCase ,      max_noise_level=lowerCamelCase ,      )
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :int ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :int ,      **lowerCamelCase   :Optional[int]      )     ->			Union[str, Any]:
       torch.manual_seed(0      )
       UpperCAmelCase__							 =						(
           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      ),
       )
       UpperCAmelCase__							 =						add_overlap_rect(lowerCamelCase ,      lowerCamelCase ,      image.size      )
       UpperCAmelCase__							 =						image.crop(lowerCamelCase      )
       UpperCAmelCase__							 =						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       UpperCAmelCase__							 =						translated_slice_x - (original_image_slice / 2)
       UpperCAmelCase__							 =						max(0 ,      lowerCamelCase      )
       UpperCAmelCase__							 =						squeeze_tile(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
       UpperCAmelCase__							 =						to_input.size
       UpperCAmelCase__							 =						to_input.resize((tile_size, tile_size) ,      Image.BICUBIC      )
       UpperCAmelCase__							 =						super(lowerCamelCase ,      self      ).__call__(image=lowerCamelCase ,      **lowerCamelCase      ).images[0]
       UpperCAmelCase__							 =						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,      Image.BICUBIC      )
       UpperCAmelCase__							 =						unsqueeze_tile(lowerCamelCase ,      lowerCamelCase      )
       UpperCAmelCase__							 =						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,      Image.BICUBIC      )
       UpperCAmelCase__							 =						[]
       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"      )
       UpperCAmelCase__							 =						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   :Optional[int] ,      lowerCamelCase   :Union[str, List[str]] ,      lowerCamelCase   :Union[PIL.Image.Image, List[PIL.Image.Image]] ,      lowerCamelCase   :int = 75 ,      lowerCamelCase   :float = 9.0 ,      lowerCamelCase   :int = 50 ,      lowerCamelCase   :Optional[Union[str, List[str]]] = None ,      lowerCamelCase   :Optional[int] = 1 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :Optional[torch.Generator] = None ,      lowerCamelCase   :Optional[torch.FloatTensor] = None ,      lowerCamelCase   :Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 128 ,      lowerCamelCase   :int = 32 ,      lowerCamelCase   :int = 32 ,      )     ->			int:
       UpperCAmelCase__							 =						Image.new("RGB" ,      (image.size[0] * 4, image.size[1] * 4)      )
       UpperCAmelCase__							 =						math.ceil(image.size[0] / tile_size      )
       UpperCAmelCase__							 =						math.ceil(image.size[1] / tile_size      )
       UpperCAmelCase__							 =						tcx * tcy
       UpperCAmelCase__							 =						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 						lowerCAmelCase		(		)     ->   List[str]:




 """simple docstring"""







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

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

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


if __name__ == "__main__":
  main()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





