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_    ):

       def __init__(							self					:     int			,			_lowerCAmelCase					:     AutoencoderKL			,			_lowerCAmelCase					:     CLIPTextModel			,			_lowerCAmelCase					:     CLIPTokenizer			,			_lowerCAmelCase					:     UNetaDConditionModel			,			_lowerCAmelCase					:     DDIMScheduler			,			_lowerCAmelCase					:     float = 0.93			,			_lowerCAmelCase					:     bool = True			,			):
           __snake_case       :				List[str] 			=    mixing_coeff
           __snake_case       :				Optional[int] 			=    leapfrog_steps

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

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

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Optional[str] = None			,			_lowerCAmelCase					:     bool = False			):
           __snake_case       :				Any 			=    self.tokenizer(
               UpperCAmelCase_			,			padding="""max_length"""			,			max_length=self.tokenizer.model_max_length			,			truncation=UpperCAmelCase_			,			return_tensors="""pt"""			,			)

           __snake_case       :				Optional[int] 			=    self.text_encoder(text_inputs.input_ids.to(self.device			)			).last_hidden_state

           __snake_case       :				Any 			=    prompt_embeds.to(dtype=self.text_encoder.dtype			,			device=self.device			)

           if do_classifier_free_guidance:
               __snake_case       :				Union[str, Any] 			=    '''''' if negative_prompt is None else negative_prompt

               __snake_case       :				int 			=    self.tokenizer(
                   UpperCAmelCase_			,			padding="""max_length"""			,			max_length=self.tokenizer.model_max_length			,			truncation=UpperCAmelCase_			,			return_tensors="""pt"""			,			)

               __snake_case       :				int 			=    self.text_encoder(uncond_input.input_ids.to(self.device			)			).last_hidden_state

               __snake_case       :				Optional[Any] 			=    torch.cat([negative_prompt_embeds, prompt_embeds]			)

           return prompt_embeds

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			):
           __snake_case       :				str 			=    self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           __snake_case       :				Tuple 			=    self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			):
           __snake_case       :				int 			=    (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           __snake_case       :				Any 			=    (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]

       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     torch.Tensor			):
           __snake_case       :				List[str] 			=    int(UpperCAmelCase_			)

           __snake_case       :				Optional[int] 			=    self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

           return alpha_prod, 1 - alpha_prod

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			):
           __snake_case       :				Tuple 			=    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           __snake_case       :				List[str] 			=    self._get_alpha_and_beta(UpperCAmelCase_			)
           __snake_case       :				Tuple 			=    self._get_alpha_and_beta(UpperCAmelCase_			)

           __snake_case       :				Any 			=    (alpha_prod_t_prev / alpha_prod_t) ** 0.5
           __snake_case       :				List[Any] 			=    -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

           __snake_case       :				Tuple 			=    (base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype			)

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			):
           __snake_case       :				List[str] 			=    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           __snake_case       :				Optional[Any] 			=    self._get_alpha_and_beta(UpperCAmelCase_			)
           __snake_case       :				List[str] 			=    self._get_alpha_and_beta(UpperCAmelCase_			)

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

           return model_input, next_model_input.to(base.dtype			)

       @torch.no_grad()
       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     torch.Tensor			):
           __snake_case       :				Optional[int] 			=    1 / self.vae.config.scaling_factor * latents
           __snake_case       :				Any 			=    self.vae.decode(UpperCAmelCase_			).sample
           __snake_case       :				Dict 			=    (image / 2 + 0.5).clamp(0			,			1			)
           return image

       @torch.no_grad()
       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     Image.Image			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     torch.Tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     Optional[torch.Generator] = None			,			):
           __snake_case       :				Any 			=    guidance_scale > 1.0

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

           __snake_case       :				Optional[int] 			=    self.vae.config.scaling_factor * latent

           __snake_case       :				Tuple 			=    [latent.clone(), latent.clone()]

           for i, t in tqdm(enumerate(UpperCAmelCase_			)			,			total=len(UpperCAmelCase_			)			):
               __snake_case       :				Dict 			=    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       :				List[str] 			=    j ^ 1

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

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

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

                   __snake_case       :				Union[str, Any] 			=    self.unet(UpperCAmelCase_			,			UpperCAmelCase_			,			encoder_hidden_states=UpperCAmelCase_			).sample

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

                   __snake_case       :				int 			=    self.noise_step(
                       base=UpperCAmelCase_			,			model_input=UpperCAmelCase_			,			model_output=UpperCAmelCase_			,			timestep=UpperCAmelCase_			,			)

                   __snake_case       :				List[Any] 			=    model_input

           return coupled_latents





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

           __snake_case       :				str 			=    self.image_processor.preprocess(UpperCAmelCase_			)

           __snake_case       :				Optional[Any] 			=    self._encode_prompt(UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			)
           __snake_case       :				List[str] 			=    self._encode_prompt(UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			)

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

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

           __snake_case       :				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			):
                   __snake_case       :				Any 			=    k ^ 1

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

                   __snake_case       :				Dict 			=    coupled_latents[j]
                   __snake_case       :				str 			=    coupled_latents[k]

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

                   __snake_case       :				Optional[Any] 			=    self.unet(UpperCAmelCase_			,			UpperCAmelCase_			,			encoder_hidden_states=UpperCAmelCase_			).sample

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

                   __snake_case       :				str 			=    self.denoise_step(
                       base=UpperCAmelCase_			,			model_input=UpperCAmelCase_			,			model_output=UpperCAmelCase_			,			timestep=UpperCAmelCase_			,			)

                   __snake_case       :				str 			=    model_input

               __snake_case       :				Optional[int] 			=    self.denoise_mixing_layer(x=coupled_latents[0]			,			y=coupled_latents[1]			)

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

           if output_type not in ["latent", "pt", "np", "pil"]:
               __snake_case       :				Dict 			=    (
                   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_			)
               __snake_case       :				str 			=    '''np'''

           if output_type == "latent":
               __snake_case       :				List[Any] 			=    final_latent
           else:
               __snake_case       :				int 			=    self.decode_latents(UpperCAmelCase_			)
               __snake_case       :				str 			=    self.image_processor.postprocess(UpperCAmelCase_			,			output_type=UpperCAmelCase_			)

           return image








import math
from timeit import timeit


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Tuple     					=	abs(_UpperCamelCase			)
				while True:
								_UpperCAmelCase						:  str     					=	n // 10
								digits += 1
								if n == 0:
												break
				return digits


def 	_A	(   _UpperCamelCase			):
				return 1 if n == 0 else math.floor(math.log(abs(_UpperCamelCase			)      ,					10			) + 1			)


def 	_A	(   _UpperCamelCase			):
				return len(str(abs(_UpperCamelCase			)			)			)


def 	_A	(   ):
				from collections.abc import Callable

				def benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			) -> None:
								_UpperCAmelCase						:  List[str]     					=	F'''{func.__name__}({value})'''
								_UpperCAmelCase						:  Any     					=	timeit(F'''__main__.{call}'''      ,					setup='''import __main__'''			)
								print(F'''{call}: {func(_UpperCamelCase			)} -- {timing} seconds'''			)

				for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
								for func in (num_digits, num_digits_fast, num_digits_faster):
												benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			)
								print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
