






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


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





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				["safety_checker", "feature_extractor"]

	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = True	,   ):
							"""simple docstring"""







							super().__init__()
							if hasattr(scheduler.config	,   'steps_offset'   ) and scheduler.config.steps_offset != 1:
													_UpperCAmelCase     =      (
													    F"""The configuration file of this scheduler: {scheduler} is outdated.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'	,   UpperCAmelCase	,   standard_warn=UpperCAmelCase   )
													_UpperCAmelCase     =      dict(scheduler.config   )
													_UpperCAmelCase     =      1
													_UpperCAmelCase     =      FrozenDict(UpperCAmelCase   )

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

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase = "auto"   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self.enable_attention_slicing(UpperCAmelCase   )

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = 0.8	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   ):
							"""simple docstring"""







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

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = 0.8	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







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

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = 512	,   UpperCAmelCase = 512	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   ):
							"""simple docstring"""







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








from __future__ import annotations

_a				     =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      [-1] * length
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None

	def __repr__(							self   ):
							"""simple docstring"""







							return F"""Node(min_value={self.minn} max_value={self.maxx})"""



def __A   (				__lowerCAmelCase     )->  Node | None:





						"""simple docstring"""



						_UpperCAmelCase     =      Node(len(__lowerCAmelCase     )     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      min(__lowerCAmelCase     ), max(__lowerCAmelCase     )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						_UpperCAmelCase     =      (root.minn + root.maxx) // 2

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []

						for index, num in enumerate(__lowerCAmelCase     ):
												if num <= pivot:
																		left_arr.append(__lowerCAmelCase     )
												else:
																		right_arr.append(__lowerCAmelCase     )
												_UpperCAmelCase     =      len(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						return root


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						_UpperCAmelCase     =      (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left			,		__lowerCAmelCase			,		node.map_left[index] - 1     )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right			,		__lowerCAmelCase			,		index - node.map_left[index]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if start > end:
												return 0
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		start - 1     )
						return rank_till_end - rank_before_start


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						_UpperCAmelCase     =      node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left			,		__lowerCAmelCase			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
						else:
												return quantile(
												    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						_UpperCAmelCase     =      range_counting(
						    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						_UpperCAmelCase     =      range_counting(
						    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()
