
'''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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def __init__(      self	:Dict						,					_A	:AutoencoderKL						,					_A	:CLIPTextModel						,					_A	:CLIPTokenizer						,					_A	:UNetaDConditionModel						,					_A	:DDIMScheduler						,					_A	:float = 0.93						,					_A	:bool = True						,					)					->      Optional[Any]:







       '''simple docstring'''



       __A													=      mixing_coeff
       __A													=      leapfrog_steps

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

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






 def 						lowercase_    (      self	:Tuple						,					_A	:str						,					_A	:Optional[str] = None						,					_A	:bool = False      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.tokenizer(
           _A						,					padding='max_length'						,					max_length=self.tokenizer.model_max_length						,					truncation=_A						,					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(
                 _A						,					padding='max_length'						,					max_length=self.tokenizer.model_max_length						,					truncation=_A						,					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 						lowercase_    (      self	:Any						,					_A	:torch.Tensor						,					_A	:torch.Tensor      )					->      Tuple:







       '''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 						lowercase_    (      self	:Dict						,					_A	:torch.Tensor						,					_A	:torch.Tensor      )					->      Any:







       '''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 						lowercase_    (      self	:List[str]						,					_A	:torch.Tensor      )					->      Tuple:







       '''simple docstring'''



       __A													=      int(_A      )

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

       return alpha_prod, 1 - alpha_prod






 def 						lowercase_    (      self	:Dict						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					)					->      str:







       '''simple docstring'''



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

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

       __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 						lowercase_    (      self	:Any						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					)					->      Union[str, Any]:







       '''simple docstring'''



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

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

       __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 						lowercase_    (      self	:List[Any]						,					_A	:torch.Tensor      )					->      Optional[Any]:







       '''simple docstring'''



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






 @torch.no_grad()
 def 						lowercase_    (      self	:Any						,					_A	:Image.Image						,					_A	:torch.Tensor						,					_A	:torch.Tensor						,					_A	:float						,					_A	:Optional[torch.Generator] = None						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      guidance_scale > 1.0

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

       __A													=      self.vae.config.scaling_factor * latent

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

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

                   __A													=      model_input

       return coupled_latents






 @torch.no_grad()
 def __call__(      self	: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"						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      guidance_scale > 1.0

       __A													=      self.image_processor.preprocess(_A      )

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

       self.scheduler.set_timesteps(_A						,					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(_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      ):
                   __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(_A						,					_A						,					encoder_hidden_states=_A      ).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=_A						,					model_input=_A						,					model_output=_A						,					timestep=_A						,					)

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

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

       return image








'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





def 							snake_case				(		)->      Union[str, Any]:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






