from __future__ import annotations

_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      -1
					_snake_case              =      -1
					_snake_case              =      [-1] * length
					_snake_case              =      None
					_snake_case              =      None







		def __repr__(			self    :				Union[str, Any]   ):






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






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





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

			_snake_case              =      []
			_snake_case              =      []

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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""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
			_snake_case              =      (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							,	lowercase__							,	node.map_left[index] - 1  )
			else:
						# go to the right subtree and map index to the right subtree
						return rank_till_index(node.right							,	lowercase__							,	index - node.map_left[index]  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""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]
			_snake_case              =      node.map_left[end] - (
			    node.map_left[start - 1] if start else 0
			)
			if num_elements_in_left_tree > index:
						return quantile(
						    node.left							,	lowercase__							,	(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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""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
			_snake_case              =      range_counting(
			    node.left							,	(node.map_left[start - 1] if start else 0)							,	node.map_left[end] - 1							,	lowercase__							,	lowercase__							,	)
			_snake_case              =      range_counting(
			    node.right							,	start - (node.map_left[start - 1] if start else 0)							,	end - node.map_left[end]							,	lowercase__							,	lowercase__							,	)
			return left + right


if __name__ == "__main__":
			import doctest

			doctest.testmod()







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				AutoencoderKL  ,					__SCREAMING_SNAKE_CASE    :				CLIPTextModel  ,					__SCREAMING_SNAKE_CASE    :				CLIPTokenizer  ,					__SCREAMING_SNAKE_CASE    :				UNetaDConditionModel  ,					__SCREAMING_SNAKE_CASE    :				DDIMScheduler  ,					__SCREAMING_SNAKE_CASE    :				float = 0.9_3  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					):






					"""simple docstring"""
					_snake_case              =      mixing_coeff
					_snake_case              =      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=__SCREAMING_SNAKE_CASE  ,					text_encoder=__SCREAMING_SNAKE_CASE  ,					tokenizer=__SCREAMING_SNAKE_CASE  ,					unet=__SCREAMING_SNAKE_CASE  ,					scheduler=__SCREAMING_SNAKE_CASE  ,					)

					_snake_case              =      2 ** (len(self.vae.config.block_out_channels   ) - 1)
					_snake_case              =      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				bool = False   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer(
					    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

					_snake_case              =      self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

					_snake_case              =      prompt_embeds.to(dtype=self.text_encoder.dtype  ,					device=self.device   )

					if do_classifier_free_guidance:
								_snake_case              =      "" if negative_prompt is None else negative_prompt

								_snake_case              =      self.tokenizer(
								    __SCREAMING_SNAKE_CASE  ,					padding="max_length"  ,					max_length=self.tokenizer.model_max_length  ,					truncation=__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"  ,					)

								_snake_case              =      self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

								_snake_case              =      torch.cat([negative_prompt_embeds, prompt_embeds]   )

					return prompt_embeds



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case              =      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case              =      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case              =      int(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					_snake_case              =      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype   )



		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					):






					"""simple docstring"""
					_snake_case              =      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )
					_snake_case       , _snake_case              =      self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					_snake_case              =      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					_snake_case              =      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype   )



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor   ):






					"""simple docstring"""
					_snake_case              =      1 / self.vae.config.scaling_factor * latents
					_snake_case              =      self.vae.decode(__SCREAMING_SNAKE_CASE   ).sample
					_snake_case              =      (image / 2 + 0.5).clamp(0  ,					1   )
					return image



		@torch.no_grad()
		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				torch.Tensor  ,					__SCREAMING_SNAKE_CASE    :				float  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      image.to(device=self.device  ,					dtype=text_embeds.dtype   )
					_snake_case              =      self.vae.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.vae.config.scaling_factor * latent

					_snake_case              =      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								_snake_case              =      self.noise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

								# j - model_input index, k - base index
								for j in range(2   ):
											_snake_case              =      j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.noise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Image.Image  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				int = 50  ,					__SCREAMING_SNAKE_CASE    :				float = 0.8  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[torch.Generator] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = "pil"  ,					):






					"""simple docstring"""
					_snake_case              =      guidance_scale > 1.0

					_snake_case              =      self.image_processor.preprocess(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      self._encode_prompt(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,					self.device   )

					_snake_case              =      num_inference_steps - int(num_inference_steps * strength   )
					_snake_case              =      self.scheduler.timesteps[t_limit:]
					_snake_case              =      fwd_timesteps.flip(0   )

					_snake_case              =      self.prepare_latents(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE   )  ,					total=len(__SCREAMING_SNAKE_CASE   )   ):
								# j - model_input index, k - base index
								for k in range(2   ):
											_snake_case              =      k ^ 1

											if self.leapfrog_steps:
														if i % 2 == 1:
																	_snake_case       , _snake_case              =      j, k

											_snake_case              =      coupled_latents[j]
											_snake_case              =      coupled_latents[k]

											_snake_case              =      torch.cat([model_input] * 2   ) if do_classifier_free_guidance else model_input

											_snake_case              =      self.unet(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					encoder_hidden_states=__SCREAMING_SNAKE_CASE   ).sample

											if do_classifier_free_guidance:
														_snake_case       , _snake_case              =      noise_pred.chunk(2   )
														_snake_case              =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_snake_case       , _snake_case              =      self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE  ,					model_input=__SCREAMING_SNAKE_CASE  ,					model_output=__SCREAMING_SNAKE_CASE  ,					timestep=__SCREAMING_SNAKE_CASE  ,					)

											_snake_case              =      model_input

								_snake_case              =      self.denoise_mixing_layer(x=coupled_latents[0]  ,					y=coupled_latents[1]   )

					# either one is fine
					_snake_case              =      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_snake_case              =      (
								    F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate("Unsupported output_type"  ,					"1.0.0"  ,					__SCREAMING_SNAKE_CASE  ,					standard_warn=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      "np"

					if output_type == "latent":
								_snake_case              =      final_latent
					else:
								_snake_case              =      self.decode_latents(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      self.image_processor.postprocess(__SCREAMING_SNAKE_CASE  ,					output_type=__SCREAMING_SNAKE_CASE   )

					return image

