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 (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]=[]			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  size[0] - overlap_pixels * 2
    __UpperCamelCase	:	List[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
    __UpperCamelCase	:	List[str]		    =  np.ones((size_y, size_x)				,     dtype=np.uinta			) * 2_5_5
    __UpperCamelCase	:	Tuple		    =  np.pad(__snake_case				,     mode='linear_ramp'				,     pad_width=__snake_case				,     end_values=0			)

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







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	List[Any]				,     _a	:	str			):




    """simple docstring"""



    return max(__snake_case				,     min(__snake_case				,     __snake_case			)			)







def  _UpperCamelCase (     _a	:	int				,     _a	:	Any				,     _a	:	Tuple			):




    """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  _UpperCamelCase (     _a	:	Dict				,     _a	:	Optional[Any]				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  list(__snake_case			)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    __UpperCamelCase	:	Dict		    =  clamp_rect(__snake_case				,     [0, 0]				,     [image_size[0], image_size[1]]			)
    return rect







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	Dict				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	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(__snake_case				,     (original_slice, 0)			)
    return result







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  (original_image_slice * 4, 0, tile.size[0], tile.size[1])
    __UpperCamelCase	:	int		    =  tile.crop(__snake_case			)
    return tile







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	Optional[int]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  n % d
    return n - divisor






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 3_5_0     ,			):
         super().__init__(
             vae=__lowerCAmelCase     ,			text_encoder=__lowerCAmelCase     ,			tokenizer=__lowerCAmelCase     ,			unet=__lowerCAmelCase     ,			low_res_scheduler=__lowerCAmelCase     ,			scheduler=__lowerCAmelCase     ,			max_noise_level=__lowerCAmelCase     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	List[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    ),
         )
         __UpperCamelCase	:	List[Any]		    =  add_overlap_rect(__lowerCAmelCase     ,			__lowerCAmelCase     ,			image.size    )
         __UpperCamelCase	:	Union[str, Any]		    =  image.crop(__lowerCAmelCase    )
         __UpperCamelCase	:	Tuple		    =  ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         __UpperCamelCase	:	int		    =  translated_slice_x - (original_image_slice / 2)
         __UpperCamelCase	:	int		    =  max(0     ,			__lowerCAmelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  squeeze_tile(__lowerCAmelCase     ,			__lowerCAmelCase     ,			__lowerCAmelCase     ,			__lowerCAmelCase    )
         __UpperCamelCase	:	str		    =  to_input.size
         __UpperCamelCase	:	Dict		    =  to_input.resize((tile_size, tile_size)     ,			Image.BICUBIC    )
         __UpperCamelCase	:	Dict		    =  super(__lowerCAmelCase     ,			self    ).__call__(image=__lowerCAmelCase     ,			**__lowerCAmelCase    ).images[0]
         __UpperCamelCase	:	Optional[int]		    =  upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,			Image.BICUBIC    )
         __UpperCamelCase	:	Any		    =  unsqueeze_tile(__lowerCAmelCase     ,			__lowerCAmelCase    )
         __UpperCamelCase	:	List[str]		    =  upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,			Image.BICUBIC    )
         __UpperCamelCase	:	List[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'    )
         __UpperCamelCase	:	Tuple		    =  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     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 7_5     ,			_lowerCamelCase = 9.0     ,			_lowerCamelCase = 5_0     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = 0.0     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = 1_2_8     ,			_lowerCamelCase = 3_2     ,			_lowerCamelCase = 3_2     ,			):
         __UpperCamelCase	:	Optional[Any]		    =  Image.new('RGB'     ,			(image.size[0] * 4, image.size[1] * 4)    )
         __UpperCamelCase	:	str		    =  math.ceil(image.size[0] / tile_size    )
         __UpperCamelCase	:	Any		    =  math.ceil(image.size[1] / tile_size    )
         __UpperCamelCase	:	int		    =  tcx * tcy
         __UpperCamelCase	:	str		    =  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  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  'stabilityai/stable-diffusion-x4-upscaler'
    __UpperCamelCase	:	Dict		    =  StableDiffusionTiledUpscalePipeline.from_pretrained(__snake_case				,     revision='fp16'				,     torch_dtype=torch.floataa			)
    __UpperCamelCase	:	List[str]		    =  pipe.to('cuda'			)
    __UpperCamelCase	:	Dict		    =  Image.open('../../docs/source/imgs/diffusers_library.jpg'			)

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

    __UpperCamelCase	:	List[str]		    =  pipe(image=__snake_case				,     prompt='Black font, white background, vector'				,     noise_level=4_0				,     callback=__snake_case			)
    final_image.save('diffusers_library.jpg'			)


if __name__ == "__main__":
  main()




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



