from __future__ import annotations

from random import random
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__		:       List[str]       						=       TypeVar("KT")
SCREAMING_SNAKE_CASE__		:       str       						=       TypeVar("VT")

class    snake_case       (   Generic[KT, VT]    ):




  def __init__(   self  :						Union[str, Any]  ,			a_  :						KT | str = "root"  ,			a_  :						VT | None = None					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		key
       SCREAMING_SNAKE_CASE__					:					Dict      =		value
       SCREAMING_SNAKE_CASE__					:					list[Node[KT, VT]]      =		[]




  def __repr__(   self  :						Dict					)->							str:




       """simple docstring"""
       return F'''Node({self.key}: {self.value})'''




  @property
  def 						__lowercase(   self  :						List[str]					)->							int:




       """simple docstring"""
       return len(self.forward					)




class    snake_case       (   Generic[KT, VT]    ):




  def __init__(   self  :						Union[str, Any]  ,			a_  :						float = 0.5  ,			a_  :						int = 16					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Node[KT, VT]      =		Node[KT, VT]()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		0
       SCREAMING_SNAKE_CASE__					:					str      =		p
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		max_level




  def __str__(   self  :						Optional[Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		list(self					)

       if len(a_					) == 0:
            return F'''SkipList(level={self.level})'''

       SCREAMING_SNAKE_CASE__					:					str      =		max((len(str(a_					)					) for item in items)  ,			default=4					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		max(a_  ,			4					) + 4

       SCREAMING_SNAKE_CASE__					:					Dict      =		self.head
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[]

       SCREAMING_SNAKE_CASE__					:					int      =		node.forward.copy()
       lines.append(F'''[{node.key}]'''.ljust(a_  ,			'-'					) + '* ' * len(a_					)					)
       lines.append(' ' * label_size + '| ' * len(a_					)					)

       while len(node.forward					) != 0:
            SCREAMING_SNAKE_CASE__					:					int      =		node.forward[0]

            lines.append(
                F'''[{node.key}]'''.ljust(a_  ,			'-'					)
                + ' '.join(str(n.key					) if n.key == node.key else '|' for n in forwards					)					)
            lines.append(' ' * label_size + '| ' * len(a_					)					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		node.forward

       lines.append('None'.ljust(a_					) + '* ' * len(a_					)					)
       return F'''SkipList(level={self.level})\n''' + "\n".join(a_					)




  def __iter__(   self  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.head

       while len(node.forward					) != 0:
            yield node.forward[0].key
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		node.forward[0]




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		1
       while random() < self.p and level < self.max_level:
            level += 1

       return level




  def 						__lowercase(   self  :						Dict  ,			a_  :						Optional[int]					)->							tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		[]

       SCREAMING_SNAKE_CASE__					:					Any      =		self.head

       for i in reversed(range(self.level					)					):
            # i < node.level - When node level is lesser than `i` decrement `i`.
            # node.forward[i].key < key - Jumping to node with key value higher
            #                             or equal to searched key would result
            #                             in skipping searched key.
            while i < node.level and node.forward[i].key < key:
                 SCREAMING_SNAKE_CASE__					:					str      =		node.forward[i]
            # Each leftmost node (relative to searched node) will potentially have to
            # be updated.
            update_vector.append(a_					)

       update_vector.reverse()  # Note that we were inserting values in reverse order.

       # len(node.forward) != 0 - If current node doesn't contain any further
       #                          references then searched key is not present.
       # node.forward[0].key == key - Next node key should be equal to search key
       #                              if key is present.
       if len(node.forward					) != 0 and node.forward[0].key == key:
            return node.forward[0], update_vector
       else:
            return None, update_vector




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						KT					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self._locate_node(a_					)

       if node is not None:
            for i, update_node in enumerate(a_					):
                 # Remove or replace all references to removed node.
                 if update_node.level > i and update_node.forward[i].key == key:
                      if node.level > i:
                           SCREAMING_SNAKE_CASE__					:					List[Any]      =		node.forward[i]
                      else:
                           SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		update_node.forward[:i]




  def 						__lowercase(   self  :						Any  ,			a_  :						KT  ,			a_  :						VT					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		self._locate_node(a_					)
       if node is not None:
            SCREAMING_SNAKE_CASE__					:					str      =		value
       else:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.random_level()

            if level > self.level:
                 # After level increase we have to add additional nodes to head.
                 for _ in range(self.level - 1  ,			a_					):
                      update_vector.append(self.head					)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		level

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		Node(a_  ,			a_					)

            for i, update_node in enumerate(update_vector[:level]					):
                 # Change references to pass through new node.
                 if update_node.level > i:
                      new_node.forward.append(update_node.forward[i]					)

                 if update_node.level < i + 1:
                      update_node.forward.append(a_					)
                 else:
                      SCREAMING_SNAKE_CASE__					:					Optional[int]      =		new_node




  def 						__lowercase(   self  :						Any  ,			a_  :						VT					)->							VT | None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self._locate_node(a_					)

       if node is not None:
            return node.value

       return None







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		SkipList()
     skip_list.insert('Key1'  ,	3			)
     skip_list.insert('Key2'  ,	12			)
     skip_list.insert('Key3'  ,	41			)
     skip_list.insert('Key4'  ,	-19			)

     SCREAMING_SNAKE_CASE__					:					Tuple      =		skip_list.head
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{}
     while node.level != 0:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		node.forward[0]
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		node.value

     assert len(lowercase__			) == 4
     assert all_values["Key1"] == 3
     assert all_values["Key2"] == 12
     assert all_values["Key3"] == 41
     assert all_values["Key4"] == -19







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		SkipList()
     skip_list.insert('Key1'  ,	10			)
     skip_list.insert('Key1'  ,	12			)

     skip_list.insert('Key5'  ,	7			)
     skip_list.insert('Key7'  ,	10			)
     skip_list.insert('Key10'  ,	5			)

     skip_list.insert('Key7'  ,	7			)
     skip_list.insert('Key5'  ,	5			)
     skip_list.insert('Key10'  ,	10			)

     SCREAMING_SNAKE_CASE__					:					Dict      =		skip_list.head
     SCREAMING_SNAKE_CASE__					:					Any      =		{}
     while node.level != 0:
          SCREAMING_SNAKE_CASE__					:					int      =		node.forward[0]
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		node.value

     if len(lowercase__			) != 4:
          print()
     assert len(lowercase__			) == 4
     assert all_values["Key1"] == 12
     assert all_values["Key7"] == 7
     assert all_values["Key5"] == 5
     assert all_values["Key10"] == 10







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		SkipList()
     assert skip_list.find('Some key'			) is None







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		SkipList()

     skip_list.insert('Key2'  ,	20			)
     assert skip_list.find('Key2'			) == 20

     skip_list.insert('Some Key'  ,	10			)
     skip_list.insert('Key2'  ,	8			)
     skip_list.insert('V'  ,	13			)

     assert skip_list.find('Y'			) is None
     assert skip_list.find('Key2'			) == 8
     assert skip_list.find('Some Key'			) == 10
     assert skip_list.find('V'			) == 13







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		SkipList()
     skip_list.delete('Some key'			)

     assert len(skip_list.head.forward			) == 0







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[Any]      =		SkipList()

     skip_list.insert('Key1'  ,	12			)
     skip_list.insert('V'  ,	13			)
     skip_list.insert('X'  ,	14			)
     skip_list.insert('Key2'  ,	15			)

     skip_list.delete('V'			)
     skip_list.delete('Key2'			)

     assert skip_list.find('V'			) is None
     assert skip_list.find('Key2'			) is None







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Dict      =		SkipList()

     skip_list.insert('Key1'  ,	12			)
     skip_list.insert('V'  ,	13			)
     skip_list.insert('X'  ,	14			)
     skip_list.insert('Key2'  ,	15			)

     skip_list.delete('V'			)
     assert skip_list.find('V'			) is None
     assert skip_list.find('X'			) == 14
     assert skip_list.find('Key1'			) == 12
     assert skip_list.find('Key2'			) == 15

     skip_list.delete('X'			)
     assert skip_list.find('V'			) is None
     assert skip_list.find('X'			) is None
     assert skip_list.find('Key1'			) == 12
     assert skip_list.find('Key2'			) == 15

     skip_list.delete('Key1'			)
     assert skip_list.find('V'			) is None
     assert skip_list.find('X'			) is None
     assert skip_list.find('Key1'			) is None
     assert skip_list.find('Key2'			) == 15

     skip_list.delete('Key2'			)
     assert skip_list.find('V'			) is None
     assert skip_list.find('X'			) is None
     assert skip_list.find('Key1'			) is None
     assert skip_list.find('Key2'			) is None







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		SkipList()

     skip_list.insert('Key1'  ,	12			)
     skip_list.insert('V'  ,	13			)
     skip_list.insert('X'  ,	1_42			)
     skip_list.insert('Key2'  ,	15			)

     skip_list.delete('X'			)

     def traverse_keys(lowercase__	:      Any			):
          yield node.key
          for forward_node in node.forward:
               yield from traverse_keys(lowercase__			)

     assert len(set(traverse_keys(skip_list.head			)			)			) == 4







def      _a					(   ):







     '''simple docstring'''


     def is_sorted(lowercase__	:      int			):
          return all(next_item >= item for item, next_item in zip(lowercase__  ,	lst[1:]			)			)

     SCREAMING_SNAKE_CASE__					:					Dict      =		SkipList()
     for i in range(10			):
          skip_list.insert(lowercase__  ,	lowercase__			)
     assert is_sorted(list(lowercase__			)			)
     skip_list.delete(5			)
     skip_list.delete(8			)
     skip_list.delete(2			)
     assert is_sorted(list(lowercase__			)			)
     skip_list.insert(-12  ,	-12			)
     skip_list.insert(77  ,	77			)
     assert is_sorted(list(lowercase__			)			)







def      _a					(   ):







     '''simple docstring'''


     for _ in range(1_00			):
          # Repeat test 100 times due to the probabilistic nature of skip list
          # random values == random bugs
          test_insert()
          test_insert_overrides_existing_value()

          test_searching_empty_list_returns_none()
          test_search()

          test_deleting_item_from_empty_list_do_nothing()
          test_deleted_items_are_not_founded_by_find_method()
          test_delete_removes_only_given_key()
          test_delete_doesnt_leave_dead_nodes()

          test_iter_always_yields_sorted_values()







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		SkipList()
     skip_list.insert(2  ,	'2'			)
     skip_list.insert(4  ,	'4'			)
     skip_list.insert(6  ,	'4'			)
     skip_list.insert(4  ,	'5'			)
     skip_list.insert(8  ,	'4'			)
     skip_list.insert(9  ,	'4'			)

     skip_list.delete(4			)

     print(lowercase__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()








'''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


_UpperCamelCase											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    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=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    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(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_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 :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					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=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






