"""simple docstring"""




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_a   :	List[str] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		__A							(				lowerCAmelCase__	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       if hasattr(scheduler.config       ,  """steps_offset"""						) and scheduler.config.steps_offset != 1:
         _lowerCAmelCase		: Tuple						    =  (
             F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
             F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
             """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
             """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
             """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
             """ file"""
         )
         deprecate("""steps_offset!=1"""       ,  """1.0.0"""       ,  _SCREAMING_SNAKE_CASE       ,  standard_warn=_SCREAMING_SNAKE_CASE						)
         _lowerCAmelCase		: List[str]						    =  dict(scheduler.config						)
         _lowerCAmelCase		: Optional[int]						    =  1
         _lowerCAmelCase		: Optional[int]						    =  FrozenDict(_SCREAMING_SNAKE_CASE						)

       if hasattr(scheduler.config       ,  """skip_prk_steps"""						) and scheduler.config.skip_prk_steps is False:
         _lowerCAmelCase		: int						    =  (
             F"The configuration file of this scheduler: {scheduler} has not set the configuration"
             """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
             """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
             """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
             """ Hub, it would be very nice if you could open a Pull request for the"""
             """ `scheduler/scheduler_config.json` file"""
         )
         deprecate("""skip_prk_steps not set"""       ,  """1.0.0"""       ,  _SCREAMING_SNAKE_CASE       ,  standard_warn=_SCREAMING_SNAKE_CASE						)
         _lowerCAmelCase		: Tuple						    =  dict(scheduler.config						)
         _lowerCAmelCase		: str						    =  True
         _lowerCAmelCase		: str						    =  FrozenDict(_SCREAMING_SNAKE_CASE						)

       if safety_checker is None:
         logger.warning(
             F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
             """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
             """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
             """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
             """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
             """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""						)

       self.register_modules(
           segmentation_model=_SCREAMING_SNAKE_CASE       ,  segmentation_processor=_SCREAMING_SNAKE_CASE       ,  vae=_SCREAMING_SNAKE_CASE       ,  text_encoder=_SCREAMING_SNAKE_CASE       ,  tokenizer=_SCREAMING_SNAKE_CASE       ,  unet=_SCREAMING_SNAKE_CASE       ,  scheduler=_SCREAMING_SNAKE_CASE       ,  safety_checker=_SCREAMING_SNAKE_CASE       ,  feature_extractor=_SCREAMING_SNAKE_CASE       ,  )






     def 					__A						(	self       ,  a__ = "auto"						):
       if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         _lowerCAmelCase		: Optional[Any]						    =  self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE						)






     def 					__A						(	self						):
       self.enable_attention_slicing(_SCREAMING_SNAKE_CASE						)






     def 					__A						(	self						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

       _lowerCAmelCase		: Dict						    =  torch.device("""cuda"""						)

       for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
         if cpu_offloaded_model is not None:
           cpu_offload(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE						)






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       if self.device != torch.device("""meta"""						) or not hasattr(self.unet       ,  """_hf_hook"""						):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(_SCREAMING_SNAKE_CASE       ,  """_hf_hook"""						)
             and hasattr(module._hf_hook       ,  """execution_device"""						)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device						)
       return self.device






     @torch.no_grad()
     def __call__(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 50       ,  a__ = 7.5       ,  a__ = None       ,  a__ = 1       ,  a__ = 0.0       ,  a__ = None       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  a__ = None       ,  a__ = 1       ,  **a__       ,  ):
       _lowerCAmelCase		: Any						    =  self.segmentation_processor(
           text=[text]       ,  images=[image]       ,  padding="""max_length"""       ,  return_tensors="""pt"""						).to(self.device						)
       _lowerCAmelCase		: str						    =  self.segmentation_model(**_SCREAMING_SNAKE_CASE						)
       _lowerCAmelCase		: int						    =  torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
       _lowerCAmelCase		: Any						    =  self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)[0].resize(image.size						)

       # Run inpainting pipeline with the generated mask
       _lowerCAmelCase		: Optional[int]						    =  StableDiffusionInpaintPipeline(
           vae=self.vae       ,  text_encoder=self.text_encoder       ,  tokenizer=self.tokenizer       ,  unet=self.unet       ,  scheduler=self.scheduler       ,  safety_checker=self.safety_checker       ,  feature_extractor=self.feature_extractor       ,  )
       return inpainting_pipeline(
           prompt=_SCREAMING_SNAKE_CASE       ,  image=_SCREAMING_SNAKE_CASE       ,  mask_image=_SCREAMING_SNAKE_CASE       ,  height=_SCREAMING_SNAKE_CASE       ,  width=_SCREAMING_SNAKE_CASE       ,  num_inference_steps=_SCREAMING_SNAKE_CASE       ,  guidance_scale=_SCREAMING_SNAKE_CASE       ,  negative_prompt=_SCREAMING_SNAKE_CASE       ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE       ,  eta=_SCREAMING_SNAKE_CASE       ,  generator=_SCREAMING_SNAKE_CASE       ,  latents=_SCREAMING_SNAKE_CASE       ,  output_type=_SCREAMING_SNAKE_CASE       ,  return_dict=_SCREAMING_SNAKE_CASE       ,  callback=_SCREAMING_SNAKE_CASE       ,  callback_steps=_SCREAMING_SNAKE_CASE       ,  )





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




