






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  __lowercase (		lowercase_					):







					'''simple docstring'''




					def __init__(			self    :     Union[str, Any]	,		UpperCamelCase_    :     AutoencoderKL	,		UpperCamelCase_    :     CLIPTextModel	,		UpperCamelCase_    :     CLIPTokenizer	,		UpperCamelCase_    :     UNetaDConditionModel	,		UpperCamelCase_    :     DDIMScheduler	,		UpperCamelCase_    :     float = 0.93	,		UpperCamelCase_    :     bool = True	,		):


									"""simple docstring"""



									__A             =		mixing_coeff
									__A             =		leapfrog_steps

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

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




					def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     str	,		UpperCamelCase_    :     Optional[str] = None	,		UpperCamelCase_    :     bool = False			):


									"""simple docstring"""



									__A             =		self.tokenizer(
									    UpperCamelCase_	,		padding="""max_length"""	,		max_length=self.tokenizer.model_max_length	,		truncation=UpperCamelCase_	,		return_tensors="""pt"""	,		)

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

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

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

													__A             =		self.tokenizer(
													    UpperCamelCase_	,		padding="""max_length"""	,		max_length=self.tokenizer.model_max_length	,		truncation=UpperCamelCase_	,		return_tensors="""pt"""	,		)

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

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

									return prompt_embeds




					def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor			):


									"""simple docstring"""



									__A             =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
									__A             =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

									return [x, y]




					def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor			):


									"""simple docstring"""



									__A             =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
									__A             =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

									return [x, y]




					def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     torch.Tensor			):


									"""simple docstring"""



									__A             =		int(UpperCamelCase_			)

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

									return alpha_prod, 1 - alpha_prod




					def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		):


									"""simple docstring"""



									__A             =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									__A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)
									__A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)

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

									__A             =		(base - b_t * model_output) / a_t

									return model_input, next_model_input.to(base.dtype			)




					def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		):


									"""simple docstring"""



									__A             =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									__A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)
									__A      ,		__A             =		self._get_alpha_and_beta(UpperCamelCase_			)

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

									return model_input, next_model_input.to(base.dtype			)




					@torch.no_grad()
					def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     torch.Tensor			):


									"""simple docstring"""



									__A             =		1 / self.vae.config.scaling_factor * latents
									__A             =		self.vae.decode(UpperCamelCase_			).sample
									__A             =		(image / 2 + 0.5).clamp(0	,		1			)
									return image




					@torch.no_grad()
					def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Image.Image	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     torch.Tensor	,		UpperCamelCase_    :     float	,		UpperCamelCase_    :     Optional[torch.Generator] = None	,		):


									"""simple docstring"""



									__A             =		guidance_scale > 1.0

									__A             =		image.to(device=self.device	,		dtype=text_embeds.dtype			)
									__A             =		self.vae.encode(UpperCamelCase_			).latent_dist.sample(UpperCamelCase_			)

									__A             =		self.vae.config.scaling_factor * latent

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

									for i, t in tqdm(enumerate(UpperCamelCase_			)	,		total=len(UpperCamelCase_			)			):
													__A             =		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             =		j ^ 1

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

																	__A             =		coupled_latents[j]
																	__A             =		coupled_latents[k]

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

																	__A             =		self.unet(UpperCamelCase_	,		UpperCamelCase_	,		encoder_hidden_states=UpperCamelCase_			).sample

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

																	__A      ,		__A             =		self.noise_step(
																	    base=UpperCamelCase_	,		model_input=UpperCamelCase_	,		model_output=UpperCamelCase_	,		timestep=UpperCamelCase_	,		)

																	__A             =		model_input

									return coupled_latents





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


									"""simple docstring"""



									__A             =		guidance_scale > 1.0

									__A             =		self.image_processor.preprocess(UpperCamelCase_			)

									__A             =		self._encode_prompt(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)
									__A             =		self._encode_prompt(UpperCamelCase_	,		UpperCamelCase_	,		UpperCamelCase_			)

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

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

									__A             =		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             =		k ^ 1

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

																	__A             =		coupled_latents[j]
																	__A             =		coupled_latents[k]

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

																	__A             =		self.unet(UpperCamelCase_	,		UpperCamelCase_	,		encoder_hidden_states=UpperCamelCase_			).sample

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

																	__A      ,		__A             =		self.denoise_step(
																	    base=UpperCamelCase_	,		model_input=UpperCamelCase_	,		model_output=UpperCamelCase_	,		timestep=UpperCamelCase_	,		)

																	__A             =		model_input

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

									# either one is fine
									__A             =		coupled_latents[0]

									if output_type not in ["latent", "pt", "np", "pil"]:
													__A             =		(
													    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             =		"""np"""

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

									return image












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     int      )  -> int:





    """simple docstring"""

    __A             =		1.0
    for i in range(1     ,			r + 1      ):
        ret *= (n + 1 - i) / i
    return round(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return choose(8 + n     ,			__lowercase      ) + choose(9 + n     ,			__lowercase      ) - 1_0



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return sum(non_bouncy_exact(__lowercase      ) for i in range(1     ,			n + 1      )      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int = 1_0_0      )  -> int:





    """simple docstring"""

    return non_bouncy_upto(__lowercase      )


if __name__ == "__main__":
  print(f"""{solution() = }""")




