




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=[]      )       ->	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      ) * 2_55
    __UpperCamelCase  		=						np.pad(lowercase_					,     mode='''linear_ramp'''					,     pad_width=lowercase_					,     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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    return max(lowercase_					,     min(lowercase_					,     lowercase_      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """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(lowercase_					,     (original_slice, 0)      )
    return result




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						n % d
    return n - divisor

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Tuple ,       snake_case					: AutoencoderKL ,       snake_case					: CLIPTextModel ,       snake_case					: CLIPTokenizer ,       snake_case					: UNetaDConditionModel ,       snake_case					: DDPMScheduler ,       snake_case					: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,       snake_case					: int = 350 ,       ):
          super().__init__(
              vae=snake_case ,       text_encoder=snake_case ,       tokenizer=snake_case ,       unet=snake_case ,       low_res_scheduler=snake_case ,       scheduler=snake_case ,       max_noise_level=snake_case ,       )







      def       snake_case	(     self					: int ,       snake_case					: Any ,       snake_case					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: Tuple ,       snake_case					: Any ,       snake_case					: Union[str, Any] ,       snake_case					: List[str] ,       **snake_case					: Dict				):
          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(snake_case ,       snake_case ,       image.size				)
          __UpperCamelCase  		=						image.crop(snake_case				)
          __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 ,       snake_case				)
          __UpperCamelCase  		=						squeeze_tile(snake_case ,       snake_case ,       snake_case ,       snake_case				)
          __UpperCamelCase  		=						to_input.size
          __UpperCamelCase  		=						to_input.resize((tile_size, tile_size) ,       Image.BICUBIC				)
          __UpperCamelCase  		=						super(snake_case ,       self				).__call__(image=snake_case ,       **snake_case				).images[0]
          __UpperCamelCase  		=						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,       Image.BICUBIC				)
          __UpperCamelCase  		=						unsqueeze_tile(snake_case ,       snake_case				)
          __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=snake_case				) ,       mode='''L''' ,       )
          final_image.paste(
              snake_case ,       (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) ,       snake_case				)







      @torch.no_grad()
      def __call__(     self					: int ,       snake_case					: Union[str, List[str]] ,       snake_case					: Union[PIL.Image.Image, List[PIL.Image.Image]] ,       snake_case					: int = 75 ,       snake_case					: float = 9.0 ,       snake_case					: int = 50 ,       snake_case					: Optional[Union[str, List[str]]] = None ,       snake_case					: Optional[int] = 1 ,       snake_case					: float = 0.0 ,       snake_case					: Optional[torch.Generator] = None ,       snake_case					: Optional[torch.FloatTensor] = None ,       snake_case					: Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,       snake_case					: int = 1 ,       snake_case					: int = 128 ,       snake_case					: int = 32 ,       snake_case					: int = 32 ,       ):
          __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(snake_case				):
              for x in range(snake_case				):
                  self._process_tile(
                      snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       snake_case ,       prompt=snake_case ,       num_inference_steps=snake_case ,       guidance_scale=snake_case ,       noise_level=snake_case ,       negative_prompt=snake_case ,       num_images_per_prompt=snake_case ,       eta=snake_case ,       generator=snake_case ,       latents=snake_case ,       )
                  current_count += 1
                  if callback is not None:
                      callback({'''progress''': current_count / total_tile_count, '''image''': final_image}				)
          return final_image







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

    """simple docstring"""


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

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

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


if __name__ == "__main__":
 main()












from __future__ import annotations

import queue

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						data
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						None







def 					__SCREAMING_SNAKE_CASE   (  )       ->	TreeNode:

    """simple docstring"""


    print('''\n********Press N to stop entering at any point of time********\n'''      )
    __UpperCamelCase  		=						input('''Enter the value of the root node: '''      ).strip().lower()
    __UpperCamelCase  		=						queue.Queue()
    __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        __UpperCamelCase  		=						F"Enter the left node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						left_node
        q.put(lowercase_      )
        __UpperCamelCase  		=						F"Enter the right node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						right_node
        q.put(lowercase_      )
    raise




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    print(node.data					,     end=''','''      )
    pre_order(node.left      )
    pre_order(node.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    in_order(node.left      )
    print(node.data					,     end=''','''      )
    in_order(node.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    post_order(node.left      )
    post_order(node.right      )
    print(node.data					,     end=''','''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        print(node_dequeued.data					,     end=''','''      )
        if node_dequeued.left:
            q.put(node_dequeued.left      )
        if node_dequeued.right:
            q.put(node_dequeued.right      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						[]
        while not q.empty():
            __UpperCamelCase  		=						q.get()
            print(node_dequeued.data					,     end=''','''      )
            if node_dequeued.left:
                list_.append(node_dequeued.left      )
            if node_dequeued.right:
                list_.append(node_dequeued.right      )
        print()
        for node in list_:
            q.put(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data					,     end=''','''      )
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        # end of while means current node doesn't have left child
        __UpperCamelCase  		=						stack.pop()
        # start to traverse its right child
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        __UpperCamelCase  		=						stack.pop()
        print(n.data					,     end=''','''      )
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase		,      __UpperCamelCase  		=						[], []
    __UpperCamelCase  		=						node
    stacka.append(lowercase_      )
    while stacka:  # to find the reversed order of post order, store it in stack2
        __UpperCamelCase  		=						stacka.pop()
        if n.left:
            stacka.append(n.left      )
        if n.right:
            stacka.append(n.right      )
        stacka.append(lowercase_      )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data					,     end=''','''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_ = ""					,     lowercase_=50					,     lowercase_="*"      )       ->	str:

    """simple docstring"""


    if not s:
        return "\n" + width * char
    __UpperCamelCase		,      __UpperCamelCase  		=						divmod(width - len(lowercase_      ) - 2					,     2      )
    return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt("Binary Tree Traversals"))

 a_   		=     build_tree()
 print(prompt("Pre Order Traversal"))
 pre_order(node)
 print(prompt() + "\n")

 print(prompt("In Order Traversal"))
 in_order(node)
 print(prompt() + "\n")

 print(prompt("Post Order Traversal"))
 post_order(node)
 print(prompt() + "\n")

 print(prompt("Level Order Traversal"))
 level_order(node)
 print(prompt() + "\n")

 print(prompt("Actual Level Order Traversal"))
 level_order_actual(node)
 print("*" * 50 + "\n")

 print(prompt("Pre Order Traversal - Iteration Version"))
 pre_order_iter(node)
 print(prompt() + "\n")

 print(prompt("In Order Traversal - Iteration Version"))
 in_order_iter(node)
 print(prompt() + "\n")

 print(prompt("Post Order Traversal - Iteration Version"))
 post_order_iter(node)
 print(prompt())






