"""simple docstring"""



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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







			'''simple docstring'''
			_UpperCamelCase     :	List[Any]				    =							['safety_checker', 'feature_extractor']





			def __init__( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_ = True      ,			):







										'''simple docstring'''


										super().__init__()
										if hasattr(scheduler.config      ,			"""steps_offset"""       ) and scheduler.config.steps_offset != 1:
																	A__					:    Dict														= (
																	    F'''The configuration file of this scheduler: {scheduler} is outdated.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"""      ,			snake_case_      ,			standard_warn=snake_case_       )
																	A__					:    List[Any]														= dict(scheduler.config       )
																	A__					:    Any														= 1
																	A__					:    List[Any]														= FrozenDict(snake_case_       )

										self.register_modules(
										    vae=snake_case_      ,			text_encoder=snake_case_      ,			tokenizer=snake_case_      ,			unet=snake_case_      ,			scheduler=snake_case_      ,			safety_checker=snake_case_      ,			feature_extractor=snake_case_      ,			)
										self.register_to_config(requires_safety_checker=snake_case_       )





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return {k: getattr(self      ,			snake_case_       ) for k in self.config.keys() if not k.startswith("""_"""       )}





			def 		lowerCamelCase				( self      ,			snake_case_ = "auto"       ):







										'''simple docstring'''


										if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	A__					:    List[Any]														= self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self.enable_attention_slicing(snake_case_       )





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_ = 0.8      ,			snake_case_ = 50      ,			snake_case_ = 7.5      ,			snake_case_ = None      ,			snake_case_ = 1      ,			snake_case_ = 0.0      ,			snake_case_ = None      ,			snake_case_ = "pil"      ,			snake_case_ = True      ,			snake_case_ = None      ,			snake_case_ = 1      ,			):







										'''simple docstring'''


										return StableDiffusionInpaintPipelineLegacy(**self.components       )(
										    prompt=snake_case_      ,			image=snake_case_      ,			mask_image=snake_case_      ,			strength=snake_case_      ,			num_inference_steps=snake_case_      ,			guidance_scale=snake_case_      ,			negative_prompt=snake_case_      ,			num_images_per_prompt=snake_case_      ,			eta=snake_case_      ,			generator=snake_case_      ,			output_type=snake_case_      ,			return_dict=snake_case_      ,			callback=snake_case_      ,			)





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_ = 0.8      ,			snake_case_ = 50      ,			snake_case_ = 7.5      ,			snake_case_ = None      ,			snake_case_ = 1      ,			snake_case_ = 0.0      ,			snake_case_ = None      ,			snake_case_ = "pil"      ,			snake_case_ = True      ,			snake_case_ = None      ,			snake_case_ = 1      ,			**snake_case_      ,			):







										'''simple docstring'''


										return StableDiffusionImgaImgPipeline(**self.components       )(
										    prompt=snake_case_      ,			image=snake_case_      ,			strength=snake_case_      ,			num_inference_steps=snake_case_      ,			guidance_scale=snake_case_      ,			negative_prompt=snake_case_      ,			num_images_per_prompt=snake_case_      ,			eta=snake_case_      ,			generator=snake_case_      ,			output_type=snake_case_      ,			return_dict=snake_case_      ,			callback=snake_case_      ,			callback_steps=snake_case_      ,			)





			@torch.no_grad()
			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = 512      ,			snake_case_ = 512      ,			snake_case_ = 50      ,			snake_case_ = 7.5      ,			snake_case_ = None      ,			snake_case_ = 1      ,			snake_case_ = 0.0      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = "pil"      ,			snake_case_ = True      ,			snake_case_ = None      ,			snake_case_ = 1      ,			):







										'''simple docstring'''


										return StableDiffusionPipeline(**self.components       )(
										    prompt=snake_case_      ,			height=snake_case_      ,			width=snake_case_      ,			num_inference_steps=snake_case_      ,			guidance_scale=snake_case_      ,			negative_prompt=snake_case_      ,			num_images_per_prompt=snake_case_      ,			eta=snake_case_      ,			generator=snake_case_      ,			latents=snake_case_      ,			output_type=snake_case_      ,			return_dict=snake_case_      ,			callback=snake_case_      ,			callback_steps=snake_case_      ,			)






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase         =		TypeVar("T", bound=bool)
class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

										if random.choice([True, False]       ):
																	A__							,       A__					:    Any														= roota.right, roota.left

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

										if data:
																	for item in data:
																								self.insert(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if not self._root:
																	raise IndexError("""Can't get top element for the empty heap."""       )
										return self._root.value





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





