



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 				lowerCAmelCase__	(						__lowerCamelCase    ):







					def __init__(							self   :  str      ,		_A   :  AutoencoderKL      ,		_A   :  CLIPTextModel      ,		_A   :  CLIPTokenizer      ,		_A   :  UNetaDConditionModel      ,		_A   :  DDIMScheduler      ,		_A   :  float = 0.93      ,		_A   :  bool = True      ,		):
											A__						:				str         =			mixing_coeff
											A__						:				List[str]         =			leapfrog_steps

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

											A__						:				List[str]         =			2 ** (len(self.vae.config.block_out_channels) - 1)
											A__						:				str         =			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)







					def 			_lowercase      (							self   :  int      ,		_A   :  str      ,		_A   :  Optional[str] = None      ,		_A   :  bool = False):
											A__						:				Optional[int]         =			self.tokenizer(
											    UpperCamelCase_      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)

											A__						:				Union[str, Any]         =			self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

											A__						:				Optional[Any]         =			prompt_embeds.to(dtype=self.text_encoder.dtype      ,		device=self.device)

											if do_classifier_free_guidance:
																	A__						:				Dict         =			"" if negative_prompt is None else negative_prompt

																	A__						:				str         =			self.tokenizer(
																	    UpperCamelCase_      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors="pt"      ,		)

																	A__						:				Optional[Any]         =			self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

																	A__						:				Union[str, Any]         =			torch.cat([negative_prompt_embeds, prompt_embeds])

											return prompt_embeds







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor):
											A__						:				Union[str, Any]         =			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
											A__						:				Union[str, Any]         =			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

											return [x, y]







					def 			_lowercase      (							self   :  str      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor):
											A__						:				int         =			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
											A__						:				List[Any]         =			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

											return [x, y]







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  torch.Tensor):
											# as self.alphas_cumprod is always in cpu
											A__						:				Optional[int]         =			int(UpperCamelCase_)

											A__						:				str         =			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

											return alpha_prod, 1 - alpha_prod







					def 			_lowercase      (							self   :  str      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		):
											A__						:				Optional[Any]         =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											A__     ,							A__						:				int         =			self._get_alpha_and_beta(UpperCamelCase_)
											A__     ,							A__						:				str         =			self._get_alpha_and_beta(UpperCamelCase_)

											A__						:				Union[str, Any]         =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											A__						:				int         =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

											A__						:				Dict         =			(base - b_t * model_output) / a_t

											return model_input, next_model_input.to(base.dtype)







					def 			_lowercase      (							self   :  Optional[Any]      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		):
											A__						:				Optional[int]         =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											A__     ,							A__						:				Tuple         =			self._get_alpha_and_beta(UpperCamelCase_)
											A__     ,							A__						:				str         =			self._get_alpha_and_beta(UpperCamelCase_)

											A__						:				str         =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											A__						:				Union[str, Any]         =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
											A__						:				List[str]         =			a_t * base + b_t * model_output

											return model_input, next_model_input.to(base.dtype)







					@torch.no_grad()
					def 			_lowercase      (							self   :  List[str]      ,		_A   :  torch.Tensor):
											A__						:				str         =			1 / self.vae.config.scaling_factor * latents
											A__						:				int         =			self.vae.decode(UpperCamelCase_).sample
											A__						:				str         =			(image / 2 + 0.5).clamp(0      ,		1)
											return image







					@torch.no_grad()
					def 			_lowercase      (							self   :  List[str]      ,		_A   :  Image.Image      ,		_A   :  torch.Tensor      ,		_A   :  torch.Tensor      ,		_A   :  float      ,		_A   :  Optional[torch.Generator] = None      ,		):
											A__						:				str         =			guidance_scale > 1.0

											A__						:				List[str]         =			image.to(device=self.device      ,		dtype=text_embeds.dtype)
											A__						:				Dict         =			self.vae.encode(UpperCamelCase_).latent_dist.sample(UpperCamelCase_)

											A__						:				int         =			self.vae.config.scaling_factor * latent

											A__						:				int         =			[latent.clone(), latent.clone()]

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

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

																							if self.leapfrog_steps:
																													if i % 2 == 0:
																																			A__     ,							A__						:				List[str]         =			j, k

																							A__						:				List[Any]         =			coupled_latents[j]
																							A__						:				Optional[int]         =			coupled_latents[k]

																							A__						:				Optional[Any]         =			torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																							A__						:				Any         =			self.unet(UpperCamelCase_      ,		UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_).sample

																							if do_classifier_free_guidance:
																													A__     ,							A__						:				Optional[Any]         =			noise_pred.chunk(2)
																													A__						:				List[str]         =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																							A__     ,							A__						:				Optional[Any]         =			self.noise_step(
																							    base=UpperCamelCase_      ,		model_input=UpperCamelCase_      ,		model_output=UpperCamelCase_      ,		timestep=UpperCamelCase_      ,		)

																							A__						:				int         =			model_input

											return coupled_latents







					@torch.no_grad()
					def __call__(							self   :  List[str]      ,		_A   :  str      ,		_A   :  str      ,		_A   :  Image.Image      ,		_A   :  float = 3.0      ,		_A   :  int = 50      ,		_A   :  float = 0.8      ,		_A   :  Optional[str] = None      ,		_A   :  Optional[torch.Generator] = None      ,		_A   :  Optional[str] = "pil"      ,		):
											A__						:				Optional[int]         =			guidance_scale > 1.0

											A__						:				str         =			self.image_processor.preprocess(UpperCamelCase_)

											A__						:				Optional[int]         =			self._encode_prompt(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_)
											A__						:				Tuple         =			self._encode_prompt(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_)

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

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

											A__						:				Dict         =			self.prepare_latents(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_)

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

																							if self.leapfrog_steps:
																													if i % 2 == 1:
																																			A__     ,							A__						:				Optional[Any]         =			j, k

																							A__						:				int         =			coupled_latents[j]
																							A__						:				Optional[int]         =			coupled_latents[k]

																							A__						:				str         =			torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																							A__						:				str         =			self.unet(UpperCamelCase_      ,		UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_).sample

																							if do_classifier_free_guidance:
																													A__     ,							A__						:				Tuple         =			noise_pred.chunk(2)
																													A__						:				List[str]         =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																							A__     ,							A__						:				Optional[int]         =			self.denoise_step(
																							    base=UpperCamelCase_      ,		model_input=UpperCamelCase_      ,		model_output=UpperCamelCase_      ,		timestep=UpperCamelCase_      ,		)

																							A__						:				Dict         =			model_input

																	A__						:				str         =			self.denoise_mixing_layer(x=coupled_latents[0]      ,		y=coupled_latents[1])

											# either one is fine
											A__						:				Optional[Any]         =			coupled_latents[0]

											if output_type not in ["latent", "pt", "np", "pil"]:
																	A__						:				str         =			(
																	    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"      ,		UpperCamelCase_      ,		standard_warn=UpperCamelCase_)
																	A__						:				Optional[int]         =			"np"

											if output_type == "latent":
																	A__						:				List[Any]         =			final_latent
											else:
																	A__						:				Tuple         =			self.decode_latents(UpperCamelCase_)
																	A__						:				List[str]         =			self.image_processor.postprocess(UpperCamelCase_      ,		output_type=UpperCamelCase_)

											return image





from __future__ import annotations

from collections.abc import Iterable





class        lowerCamelCase__:
      def __init__(							self:					Dict       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					str  ):
             __lowerCamelCase,  __lowerCamelCase  =		float(UpperCamelCase_  ), float(UpperCamelCase_  )
      def __eq__(							self:					List[Any]       ,			UpperCamelCase_:					List[str]  ):
             return self.x == other.x and self.y == other.y
      def __ne__(							self:					Tuple       ,			UpperCamelCase_:					Dict  ):
             return not self == other
      def __gt__(							self:					List[Any]       ,			UpperCamelCase_:					List[Any]  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y > other.y
             return False
      def __lt__(							self:					str       ,			UpperCamelCase_:					int  ):
             return not self > other
      def __ge__(							self:					Dict       ,			UpperCamelCase_:					Any  ):
             if self.x > other.x:
                    return True
             elif self.x == other.x:
                    return self.y >= other.y
             return False
      def __le__(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[Any]  ):
             if self.x < other.x:
                    return True
             elif self.x == other.x:
                    return self.y <= other.y
             return False
      def __repr__(							self:					Union[str, Any]  ):
             return F'({self.x}, {self.y})'




      def __hash__(							self:					List[Any]  ):
             return hash(self.x  )




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]] | Iterable[list[float]]    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       if list_of_tuples:
              for p in list_of_tuples:
                     if isinstance(A__    ,     A__      ):
                            points.append(A__      )
                     else:
                            try:
                                   points.append(Point(p[0]    ,     p[1]      )      )
                            except (IndexError, TypeError):
                                   print(
                                       f'Ignoring deformed point {p}. All points'
                                       """ must have at least 2 coordinates."""      )
       return points




def    lowerCamelCase__			(		A__			:   list[Point] | list[list[float]]      ):





       '''simple docstring'''



       if not hasattr(A__    ,     """__iter__"""      ):
              __lowerCamelCase  =		f'Expecting an iterable object but got an non-iterable type {points}'
              raise ValueError(A__      )

       if not points:
              __lowerCamelCase  =		f'Expecting a list of points but got {points}'
              raise ValueError(A__      )

       return _construct_points(A__      )




def    lowerCamelCase__			(		A__			:   Point    ,     A__			:   Point    ,     A__			:   Point      ):





       '''simple docstring'''



       __lowerCamelCase  =		(a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
       return det




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )
       __lowerCamelCase  =		set()

       for i in range(n - 1      ):
              for j in range(i + 1    ,     A__      ):
                     __lowerCamelCase  =		__lowerCamelCase  =		False
                     __lowerCamelCase  =		True
                     for k in range(A__      ):
                            if k != i and k != j:
                                   __lowerCamelCase  =		_det(points[i]    ,     points[j]    ,     points[k]      )

                                   if det_k > 0:
                                          __lowerCamelCase  =		True
                                   elif det_k < 0:
                                          __lowerCamelCase  =		True
                                   else:
                                          # point[i], point[j], point[k] all lie on a straight line
                                          # if point[k] is to the left of point[i] or it's to the
                                          # right of point[j], then point[i], point[j] cannot be
                                          # part of the convex hull of A
                                          if points[k] < points[i] or points[k] > points[j]:
                                                 __lowerCamelCase  =		False
                                                 break

                            if points_left_of_ij and points_right_of_ij:
                                   __lowerCamelCase  =		False
                                   break

                     if ij_part_of_convex_hull:
                            convex_set.update([points[i], points[j]]      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       # divide all the points into an upper hull and a lower hull
       # the left most point and the right most point are definitely
       # members of the convex hull by definition.
       # use these two anchors to divide all the points into two hulls,
       # an upper hull and a lower hull.

       # all points to the left (above) the line joining the extreme points belong to the
       # upper hull
       # all points to the right (below) the line joining the extreme points below to the
       # lower hull
       # ignore all points on the line joining the extreme points since they cannot be
       # part of the convex hull

       __lowerCamelCase  =		points[0]
       __lowerCamelCase  =		points[n - 1]

       __lowerCamelCase  =		{left_most_point, right_most_point}
       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]

       for i in range(1    ,     n - 1      ):
              __lowerCamelCase  =		_det(A__    ,     A__    ,     points[i]      )

              if det > 0:
                     upper_hull.append(points[i]      )
              elif det < 0:
                     lower_hull.append(points[i]      )

       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
       _construct_hull(A__    ,     A__    ,     A__    ,     A__      )

       return sorted(A__      )




def    lowerCamelCase__			(		A__			:   list[Point]    ,     A__			:   Point    ,     A__			:   Point    ,     A__			:   set[Point]      ):





       '''simple docstring'''



       if points:
              __lowerCamelCase  =		None
              __lowerCamelCase  =		float("""-inf"""      )
              __lowerCamelCase  =		[]

              for p in points:
                     __lowerCamelCase  =		_det(A__    ,     A__    ,     A__      )

                     if det > 0:
                            candidate_points.append(A__      )

                            if det > extreme_point_distance:
                                   __lowerCamelCase  =		det
                                   __lowerCamelCase  =		p

              if extreme_point:
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )
                     convex_set.add(A__      )
                     _construct_hull(A__    ,     A__    ,     A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   list[Point]      ):





       '''simple docstring'''



       __lowerCamelCase  =		sorted(_validate_input(A__      )      )
       __lowerCamelCase  =		len(A__      )

       __lowerCamelCase  =		points[:2]
       for i in range(2    ,     A__      ):
              __lowerCamelCase  =		_det(convex_hull[1]    ,     convex_hull[0]    ,     points[i]      )
              if det > 0:
                     convex_hull.insert(0    ,     points[i]      )
                     break
              elif det < 0:
                     convex_hull.append(points[i]      )
                     break
              else:
                     __lowerCamelCase  =		points[i]
       i += 1

       for j in range(A__    ,     A__      ):
              if (
                  _det(convex_hull[0]    ,     convex_hull[-1]    ,     points[j]      ) > 0
                  and _det(convex_hull[-1]    ,     convex_hull[0]    ,     points[1]      ) < 0
              ):
                     # The point lies within the convex hull
                     continue

              convex_hull.insert(0    ,     points[j]      )
              convex_hull.append(points[j]      )
              while _det(convex_hull[0]    ,     convex_hull[1]    ,     convex_hull[2]      ) >= 0:
                     del convex_hull[1]
              while _det(convex_hull[-1]    ,     convex_hull[-2]    ,     convex_hull[-3]      ) <= 0:
                     del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
       return sorted(convex_hull[1:] if len(A__      ) > 3 else convex_hull      )




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		[
           (0, 3),
           (2, 2),
           (1, 1),
           (2, 1),
           (3, 0),
           (0, 0),
           (3, 3),
           (2, -1),
           (2, -4),
           (1, -3),
       ]
       # the convex set of points is
       # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
       __lowerCamelCase  =		convex_hull_bf(A__      )

       __lowerCamelCase  =		convex_hull_recursive(A__      )
       assert results_bf == results_recursive

       __lowerCamelCase  =		convex_hull_melkman(A__      )
       assert results_bf == results_melkman

       print(A__      )


if __name__ == "__main__":
       main()

