





'''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 							UpperCAmelCase_  (						__lowerCamelCase : Tuple     ,__lowerCamelCase : int     ,__lowerCamelCase : Any=[]			):
   lowercase_      :Optional[Any]         =			size[0] - overlap_pixels * 2
   lowercase_      :str         =			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_      :str         =			np.ones((size_y, size_x)     ,dtype=np.uinta			) * 2_55
   lowercase_      :Optional[Any]         =			np.pad(__lowerCamelCase     ,mode="linear_ramp"     ,pad_width=__lowerCamelCase     ,end_values=0			)

   if "l" in remove_borders:
      lowercase_      :List[Any]         =			mask[:, overlap_pixels : mask.shape[1]]
   if "r" in remove_borders:
      lowercase_      :Union[str, Any]         =			mask[:, 0 : mask.shape[1] - overlap_pixels]
   if "t" in remove_borders:
      lowercase_      :Optional[int]         =			mask[overlap_pixels : mask.shape[0], :]
   if "b" in remove_borders:
      lowercase_      :int         =			mask[0 : mask.shape[0] - overlap_pixels, :]
   return mask
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : List[Any]			):
   return max(__lowerCamelCase     ,min(__lowerCamelCase     ,__lowerCamelCase			)			)
def 							UpperCAmelCase_  (						__lowerCamelCase : [int]     ,__lowerCamelCase : [int]     ,__lowerCamelCase : [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 							UpperCAmelCase_  (						__lowerCamelCase : [int]     ,__lowerCamelCase : int     ,__lowerCamelCase : [int]			):
   lowercase_      :Dict         =			list(__lowerCamelCase			)
   rect[0] -= overlap
   rect[1] -= overlap
   rect[2] += overlap
   rect[3] += overlap
   lowercase_      :Dict         =			clamp_rect(__lowerCamelCase     ,[0, 0]     ,[image_size[0], image_size[1]]			)
   return rect
def 							UpperCAmelCase_  (						__lowerCamelCase : Any     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : str     ,__lowerCamelCase : Optional[Any]			):
   lowercase_      :Optional[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(__lowerCamelCase     ,(original_slice, 0)			)
   return result
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : int			):
   lowercase_      :Optional[int]         =			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
   lowercase_      :int         =			tile.crop(__lowerCamelCase			)
   return tile
def 							UpperCAmelCase_  (						__lowerCamelCase : int     ,__lowerCamelCase : Tuple			):
   lowercase_      :Union[str, Any]         =			n % d
   return n - divisor
class 				a_       (    _lowerCAmelCase							):





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





        """simple docstring"""




        super().__init__(
            vae=lowercase     ,				text_encoder=lowercase     ,				tokenizer=lowercase     ,				unet=lowercase     ,				low_res_scheduler=lowercase     ,				scheduler=lowercase     ,				max_noise_level=lowercase     ,				)





     def 							lowercase__				(			self		: Any     ,				lowercase		: Union[str, Any]     ,				lowercase		: Any     ,				lowercase		: Dict     ,				lowercase		: Tuple     ,				lowercase		: Dict     ,				lowercase		: Any     ,				lowercase		: str     ,				**lowercase		: Any						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_      :Union[str, Any]         =			(
            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(lowercase     ,				lowercase     ,				image.size						)
        lowercase_      :Dict         =			image.crop(lowercase						)
        lowercase_      :Any         =			((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
        lowercase_      :Optional[int]         =			translated_slice_x - (original_image_slice / 2)
        lowercase_      :Optional[int]         =			max(0     ,				lowercase						)
        lowercase_      :Optional[Any]         =			squeeze_tile(lowercase     ,				lowercase     ,				lowercase     ,				lowercase						)
        lowercase_      :Tuple         =			to_input.size
        lowercase_      :List[Any]         =			to_input.resize((tile_size, tile_size)     ,				Image.BICUBIC						)
        lowercase_      :Any         =			super(lowercase     ,				self						).__call__(image=lowercase     ,				**lowercase						).images[0]
        lowercase_      :Optional[int]         =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,				Image.BICUBIC						)
        lowercase_      :List[str]         =			unsqueeze_tile(lowercase     ,				lowercase						)
        lowercase_      :List[Any]         =			upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,				Image.BICUBIC						)
        lowercase_      :Union[str, Any]         =			[]
        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_      :Any         =			Image.fromarray(
            make_transparency_mask(
                (upscaled_tile.size[0], upscaled_tile.size[1])     ,				tile_border * 4     ,				remove_borders=lowercase						)     ,				mode="L"     ,				)
        final_image.paste(
            lowercase     ,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)     ,				lowercase						)







     @torch.no_grad()
     def __call__(			self		: Optional[int]     ,				lowercase		: Union[str, List[str]]     ,				lowercase		: Union[PIL.Image.Image, List[PIL.Image.Image]]     ,				lowercase		: int = 75     ,				lowercase		: float = 9.0     ,				lowercase		: int = 50     ,				lowercase		: Optional[Union[str, List[str]]] = None     ,				lowercase		: Optional[int] = 1     ,				lowercase		: float = 0.0     ,				lowercase		: Optional[torch.Generator] = None     ,				lowercase		: Optional[torch.FloatTensor] = None     ,				lowercase		: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,				lowercase		: int = 1     ,				lowercase		: int = 128     ,				lowercase		: int = 32     ,				lowercase		: int = 32     ,				):





        """simple docstring"""




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







def 							UpperCAmelCase_  (						):
   # Run a demo
   lowercase_      :Dict         =			"stabilityai/stable-diffusion-x4-upscaler"
   lowercase_      :Any         =			StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCamelCase     ,revision="fp16"     ,torch_dtype=torch.floataa			)
   lowercase_      :List[Any]         =			pipe.to("cuda"			)
   lowercase_      :int         =			Image.open("../../docs/source/imgs/diffusers_library.jpg"			)

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

   lowercase_      :Tuple         =			pipe(image=__lowerCamelCase     ,prompt="Black font, white background, vector"     ,noise_level=40     ,callback=__lowerCamelCase			)
   final_image.save("diffusers_library.jpg"			)


if __name__ == "__main__":
 main()









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase :					int 							={
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Any 							=['''CLIPTokenizerFast''']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					str 							=['''CLIPFeatureExtractor''']
 lowerCAmelCase :					Optional[int] 							=['''CLIPImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Any 							=[
     '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CLIPModel''',
     '''CLIPPreTrainedModel''',
     '''CLIPTextModel''',
     '''CLIPTextModelWithProjection''',
     '''CLIPVisionModel''',
     '''CLIPVisionModelWithProjection''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Union[str, Any] 							=[
     '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCLIPModel''',
     '''TFCLIPPreTrainedModel''',
     '''TFCLIPTextModel''',
     '''TFCLIPVisionModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					List[Any] 							=[
     '''FlaxCLIPModel''',
     '''FlaxCLIPPreTrainedModel''',
     '''FlaxCLIPTextModel''',
     '''FlaxCLIPTextPreTrainedModel''',
     '''FlaxCLIPVisionModel''',
     '''FlaxCLIPVisionPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_clip import (
     CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CLIPConfig,
     CLIPOnnxConfig,
     CLIPTextConfig,
     CLIPVisionConfig,
 )
 from .processing_clip import CLIPProcessor
 from .tokenization_clip import CLIPTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_clip_fast import CLIPTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clip import CLIPFeatureExtractor
  from .image_processing_clip import CLIPImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_clip import (
      CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      CLIPModel,
      CLIPPreTrainedModel,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPVisionModel,
      CLIPVisionModelWithProjection,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_clip import (
      TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCLIPModel,
      TFCLIPPreTrainedModel,
      TFCLIPTextModel,
      TFCLIPVisionModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_clip import (
      FlaxCLIPModel,
      FlaxCLIPPreTrainedModel,
      FlaxCLIPTextModel,
      FlaxCLIPTextPreTrainedModel,
      FlaxCLIPVisionModel,
      FlaxCLIPVisionPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase :					int 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


