






"""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		(							_lowerCAmelCase						):


   '''simple docstring'''

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

     '''simple docstring'''
     __A						      =			mixing_coeff
     __A						      =			leapfrog_steps

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

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					str,	_lowerCamelCase			:					Optional[str] = None,	_lowerCamelCase			:					bool = False  ):

     '''simple docstring'''
     __A						      =			self.tokenizer(
         _lowerCamelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=_lowerCamelCase,	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(
           _lowerCamelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=_lowerCamelCase,	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     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					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     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					torch.Tensor  ):

     '''simple docstring'''
     # as self.alphas_cumprod is always in cpu
     __A						      =			int(_lowerCamelCase  )

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

     return alpha_prod, 1 - alpha_prod


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	):

     '''simple docstring'''
     __A						      =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

     __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     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	):

     '''simple docstring'''
     __A						      =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

     __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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					torch.Tensor  ):

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


   @torch.no_grad()
   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Image.Image,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					torch.Tensor,	_lowerCamelCase			:					float,	_lowerCamelCase			:					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(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )

     __A						      =			self.vae.config.scaling_factor * latent

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

     for i, t in tqdm(enumerate(_lowerCamelCase  ),	total=len(_lowerCamelCase  )  ):
       __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(_lowerCamelCase,	_lowerCamelCase,	encoder_hidden_states=_lowerCamelCase  ).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=_lowerCamelCase,	model_input=_lowerCamelCase,	model_output=_lowerCamelCase,	timestep=_lowerCamelCase,	)

         __A						      =			model_input

     return coupled_latents






   @torch.no_grad()
   def __call__(     self			:					Union[str, Any],	_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",	):

     '''simple docstring'''
     __A						      =			guidance_scale > 1.0

     __A						      =			self.image_processor.preprocess(_lowerCamelCase  )

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

     self.scheduler.set_timesteps(_lowerCamelCase,	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(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase  )

     for i, t in tqdm(enumerate(_lowerCamelCase  ),	total=len(_lowerCamelCase  )  ):
       # 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(_lowerCamelCase,	_lowerCamelCase,	encoder_hidden_states=_lowerCamelCase  ).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=_lowerCamelCase,	model_input=_lowerCamelCase,	model_output=_lowerCamelCase,	timestep=_lowerCamelCase,	)

         __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''',	_lowerCamelCase,	standard_warn=_lowerCamelCase  )
       __A						      =			'''np'''

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

     return image












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




