


"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							UNetaDModel
    UpperCAmelCase	:							KarrasVeScheduler






    def __init__( self    :   str    ,			__UpperCAmelCase    :   UNetaDModel    ,			__UpperCAmelCase    :   KarrasVeScheduler):
      super().__init__()
      self.register_modules(unet=__UpperCAmelCase    ,			scheduler=__UpperCAmelCase)






    @torch.no_grad()
    def __call__( self    :   List[str]    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 50    ,			__UpperCAmelCase    :   Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,			__UpperCAmelCase    :   Optional[str] = "pil"    ,			__UpperCAmelCase    :   bool = True    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      a						:		List[Any]											=      self.unet.config.sample_size
      a						:		Tuple											=      (batch_size, 3, img_size, img_size)

      a						:		str											=      self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      a						:		Any											=      randn_tensor(__UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			device=self.device) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__UpperCAmelCase)

      for t in self.progress_bar(self.scheduler.timesteps):
        # here sigma_t == t_i from the paper
        a						:		List[str]											=      self.scheduler.schedule[t]
        a						:		str											=      self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        a							,		a						:		Optional[Any]											=      self.scheduler.add_noise_to_input(__UpperCAmelCase    ,			__UpperCAmelCase    ,			generator=__UpperCAmelCase)

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        a						:		Union[str, Any]											=      (sigma_hat / 2) * model((sample_hat + 1) / 2    ,			sigma_hat / 2).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        a						:		Union[str, Any]											=      self.scheduler.step(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

        if sigma_prev != 0:
          # 6. Apply 2nd order correction
          # The model inputs and output are adjusted by following eq. (213) in [1].
          a						:		Optional[int]											=      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,			sigma_prev / 2).sample
          a						:		Optional[int]											=      self.scheduler.step_correct(
              __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			step_output.prev_sample    ,			step_output["derivative"]    ,			)
        a						:		List[str]											=      step_output.prev_sample

      a						:		Union[str, Any]											=      (sample / 2 + 0.5).clamp(0    ,			1)
      a						:		Dict											=      sample.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
      if output_type == "pil":
        a						:		Union[str, Any]											=      self.numpy_to_pil(__UpperCAmelCase)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase)










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







  print("\n********Press N to stop entering at any point of time********\n"	)
  a						:		int											=      input("Enter the value of the root node: "	).strip().lower()
  a						:		queue.Queue											=      queue.Queue()
  a						:		Tuple											=      TreeNode(int(A_	)	)
  q.put(A_	)
  while not q.empty():
    a						:		Union[str, Any]											=      q.get()
    a						:		Optional[int]											=      F'''Enter the left node of {node_found.data}: '''
    a						:		Union[str, Any]											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		List[str]											=      TreeNode(int(A_	)	)
    a						:		Any											=      left_node
    q.put(A_	)
    a						:		Dict											=      F'''Enter the right node of {node_found.data}: '''
    a						:		str											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		Any											=      TreeNode(int(A_	)	)
    a						:		str											=      right_node
    q.put(A_	)
  raise


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      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 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		Any											=      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(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		int											=      node
  while n or stack:
    while n:  # start from root node, find its left child
      print(n.data  ,       end=","	)
      stack.append(A_	)
      a						:		Tuple											=      n.left
    # end of while means current node doesn't have left child
    a						:		Optional[Any]											=      stack.pop()
    # start to traverse its right child
    a						:		str											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







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


if __name__ == "__main__":
 import doctest

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

 __lowercase    =				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())






