


"""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 _A						(						lowercase       ,    lowercase       ,    lowercase=[]		):







    """simple docstring"""
    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		) * 2_55
    a 		=np.pad(lowercase       ,    mode='''linear_ramp'''       ,    pad_width=lowercase       ,    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						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    return max(lowercase       ,    min(lowercase       ,    lowercase		)		)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """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 _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=list(lowercase		)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    a 		=clamp_rect(lowercase       ,    [0, 0]       ,    [image_size[0], image_size[1]]		)
    return rect
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    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(lowercase       ,    (original_slice, 0)		)
    return result
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    a 		=tile.crop(lowercase		)
    return tile
def _A						(						lowercase       ,    lowercase		):







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






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A = 350 ,    )					->     int:
     super().__init__(
         vae=__A ,    text_encoder=__A ,    tokenizer=__A ,    unet=__A ,    low_res_scheduler=__A ,    scheduler=__A ,    max_noise_level=__A ,    )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    **__A						)					->     Tuple:
     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(__A ,    __A ,    image.size						)
     a 		=image.crop(__A						)
     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 ,    __A						)
     a 		=squeeze_tile(__A ,    __A ,    __A ,    __A						)
     a 		=to_input.size
     a 		=to_input.resize((tile_size, tile_size) ,    Image.BICUBIC						)
     a 		=super(__A ,    self						).__call__(image=__A ,    **__A						).images[0]
     a 		=upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,    Image.BICUBIC						)
     a 		=unsqueeze_tile(__A ,    __A						)
     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=__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 ,    )					->     Optional[int]:
     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(__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 _A						(						):







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

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

    a 		=pipe(image=lowercase       ,    prompt='''Black font, white background, vector'''       ,    noise_level=40       ,    callback=lowercase		)
    final_image.save('''diffusers_library.jpg'''		)


if __name__ == "__main__":
       main()






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

