






'''simple docstring'''





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 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Optional[int]							,				_A       :						AutoencoderKL							,				_A       :						CLIPTextModel							,				_A       :						CLIPTokenizer							,				_A       :						UNetaDConditionModel							,				_A       :						DDIMScheduler							,				_A       :						float = 0.93							,				_A       :						bool = True							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   mixing_coeff
								UpperCAmelCase_     :   Union[str, Any]       =   leapfrog_steps

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

								UpperCAmelCase_     :   Optional[Any]       =   2 ** (len(self.vae.config.block_out_channels     ) - 1)
								UpperCAmelCase_     :   Optional[Any]       =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )




					def 						A     (       self       :						Optional[int]							,				_A       :						str							,				_A       :						Optional[str] = None							,				_A       :						bool = False     )  ->    Dict:
								UpperCAmelCase_     :   int       =   self.tokenizer(
								    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

								UpperCAmelCase_     :   Tuple       =   self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

								UpperCAmelCase_     :   Tuple       =   prompt_embeds.to(dtype=self.text_encoder.dtype							,				device=self.device     )

								if do_classifier_free_guidance:
											UpperCAmelCase_     :   Optional[int]       =   '''''' if negative_prompt is None else negative_prompt

											UpperCAmelCase_     :   Union[str, Any]       =   self.tokenizer(
											    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

											UpperCAmelCase_     :   List[str]       =   self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

											UpperCAmelCase_     :   Tuple       =   torch.cat([negative_prompt_embeds, prompt_embeds]     )

								return prompt_embeds




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

								return [x, y]




					def 						A     (       self       :						Union[str, Any]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCAmelCase_     :   int       =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




					def 						A     (       self       :						List[str]							,				_A       :						torch.Tensor     )  ->    List[Any]:
								# as self.alphas_cumprod is always in cpu
								UpperCAmelCase_     :   Tuple       =   int(_A     )

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

								return alpha_prod, 1 - alpha_prod




					def 						A     (       self       :						Dict							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Tuple:
								UpperCAmelCase_     :   str       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Tuple       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								UpperCAmelCase_     :   List[str]       =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )




					def 						A     (       self       :						Any							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Any       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Optional[int]       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
								UpperCAmelCase_     :   List[Any]       =   a_t * base + b_t * model_output

								return model_input, next_model_input.to(base.dtype     )




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						torch.Tensor     )  ->    List[Any]:
								UpperCAmelCase_     :   Union[str, Any]       =   1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_     :   str       =   self.vae.decode(_A     ).sample
								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								return image




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						Image.Image							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						float							,				_A       :						Optional[torch.Generator] = None							,				)  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   str       =   image.to(device=self.device							,				dtype=text_embeds.dtype     )
								UpperCAmelCase_     :   int       =   self.vae.encode(_A     ).latent_dist.sample(_A     )

								UpperCAmelCase_     :   List[str]       =   self.vae.config.scaling_factor * latent

								UpperCAmelCase_     :   Optional[int]       =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											UpperCAmelCase_     :   List[str]       =   self.noise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

											# j - model_input index, k - base index
											for j in range(2     ):
														UpperCAmelCase_     :   List[Any]       =   j ^ 1

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

														UpperCAmelCase_     :   str       =   coupled_latents[j]
														UpperCAmelCase_     :   List[Any]       =   coupled_latents[k]

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

														UpperCAmelCase_     :   Dict       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

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

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   self.noise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

								return coupled_latents





					@torch.no_grad()
					def __call__(       self       :						Any							,				_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"							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   List[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   Optional[Any]       =   self.image_processor.preprocess(_A     )

								UpperCAmelCase_     :   str       =   self._encode_prompt(_A							,				_A							,				_A     )
								UpperCAmelCase_     :   int       =   self._encode_prompt(_A							,				_A							,				_A     )

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

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

								UpperCAmelCase_     :   Optional[int]       =   self.prepare_latents(_A							,				_A							,				_A							,				_A							,				_A     )

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											# j - model_input index, k - base index
											for k in range(2     ):
														UpperCAmelCase_     :   Union[str, Any]       =   k ^ 1

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

														UpperCAmelCase_     :   Tuple       =   coupled_latents[j]
														UpperCAmelCase_     :   Optional[int]       =   coupled_latents[k]

														UpperCAmelCase_     :   Union[str, Any]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   int       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

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

														UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.denoise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

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

								# either one is fine
								UpperCAmelCase_     :   Union[str, Any]       =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
											UpperCAmelCase_     :   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'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   int       =   '''np'''

								if output_type == "latent":
											UpperCAmelCase_     :   Tuple       =   final_latent
								else:
											UpperCAmelCase_     :   Optional[int]       =   self.decode_latents(_A     )
											UpperCAmelCase_     :   str       =   self.image_processor.postprocess(_A							,				output_type=_A     )

								return image










'''simple docstring'''





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class 				snake_case__     (			UpperCamelCase):
					a_       =			1
					a_       =			2
					a_       =			3




					def __repr__(       self       :						str     )  ->    Optional[int]:
								return F"{self.__class__.__name__}.{self.name}"






def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	int	,   A			:	int						)  -> float:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   point
			return degrees(atana(y - miny	,   x - minx						)						)
def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	tuple[int, int]	,   A			:	tuple[int, int]						)  -> Direction:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   starting
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   via
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   target
			UpperCAmelCase_     :   Optional[int]       =   degrees(atana(ya - ya	,   xa - xa						)						)
			via_angle %= 3_6_0
			UpperCAmelCase_     :   Dict       =   degrees(atana(ya - ya	,   xa - xa						)						)
			target_angle %= 3_6_0
			# t-
			#  \ \
			#   \ v
			#    \|
			#     s
			# via_angle is always lower than target_angle, if direction is left.
			# If they are same, it means they are on a same line of convex hull.
			if target_angle > via_angle:
						return Direction.left
			elif target_angle == via_angle:
						return Direction.straight
			else:
						return Direction.right







def __UpperCAmelCase (			A			:	list[tuple[int, int]]						)  -> list[tuple[int, int]]:

			if len(A						) <= 2:
						# There is no convex hull
						raise ValueError('''graham_scan: argument must contain more than 3 points.'''						)
			if len(A						) == 3:
						return points
			# find the lowest and the most left point
			UpperCAmelCase_     :   List[str]       =   0
			UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   maxsize, maxsize
			for i, point in enumerate(A						):
						UpperCAmelCase_     :   str       =   point[0]
						UpperCAmelCase_     :   List[Any]       =   point[1]
						if y < miny:
									UpperCAmelCase_     :   int       =   y
									UpperCAmelCase_     :   Dict       =   x
									UpperCAmelCase_     :   Any       =   i
						if y == miny and x < minx:
									UpperCAmelCase_     :   str       =   x
									UpperCAmelCase_     :   Optional[int]       =   i

    # remove the lowest and the most left point from points for preparing for sort
			points.pop(A						)

			UpperCAmelCase_     :   Optional[Any]       =   sorted(A	,   key=lambda A						: angle_comparer(A	,   A	,   A						)						)
			# This insert actually costs complexity,
			# and you should instead add (minx, miny) into stack later.
			# I'm using insert just for easy understanding.
			sorted_points.insert(0	,   (minx, miny)						)

			UpperCAmelCase_     :   deque[tuple[int, int]]       =   deque()
			stack.append(sorted_points[0]						)
			stack.append(sorted_points[1]						)
			stack.append(sorted_points[2]						)
			# In any ways, the first 3 points line are towards left.
			# Because we sort them the angle from minx, miny.
			UpperCAmelCase_     :   Any       =   Direction.left

			for i in range(3	,   len(A						)						):
						while True:
									UpperCAmelCase_     :   Any       =   stack[-2]
									UpperCAmelCase_     :   List[Any]       =   stack[-1]
									UpperCAmelCase_     :   Any       =   sorted_points[i]
									UpperCAmelCase_     :   Tuple       =   check_direction(A	,   A	,   A						)

									if next_direction == Direction.left:
												UpperCAmelCase_     :   Optional[Any]       =   Direction.left
												break
									if next_direction == Direction.straight:
												if current_direction == Direction.left:
															# We keep current_direction as left.
															# Because if the straight line keeps as straight,
															# we want to know if this straight line is towards left.
															break
												elif current_direction == Direction.right:
															# If the straight line is towards right,
															# every previous points on those straigh line is not convex hull.
															stack.pop()
									if next_direction == Direction.right:
												stack.pop()
						stack.append(sorted_points[i]						)
			return list(A						)


