

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 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = 0.93,					lowercase_ = True,					)							->					Any:






     """simple docstring"""






     a__						=mixing_coeff
     a__						=leapfrog_steps

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

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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None,					lowercase_ = False )							->					Union[str, Any]:






     """simple docstring"""






     a__						=self.tokenizer(
         lowercase_,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowercase_,					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(
           lowercase_,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowercase_,					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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					Optional[int]:






     """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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					Tuple:






     """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 				_UpperCAmelCase				(       self,					lowercase_ )							->					Any:






     """simple docstring"""






     a__						=int(lowercase_ )

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

     return alpha_prod, 1 - alpha_prod



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					)							->					Dict:






     """simple docstring"""






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

     a__,   a__						=self._get_alpha_and_beta(lowercase_ )
     a__,   a__						=self._get_alpha_and_beta(lowercase_ )

     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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					)							->					Optional[int]:






     """simple docstring"""






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

     a__,   a__						=self._get_alpha_and_beta(lowercase_ )
     a__,   a__						=self._get_alpha_and_beta(lowercase_ )

     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 				_UpperCAmelCase				(       self,					lowercase_ )							->					Dict:






     """simple docstring"""






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



   @torch.no_grad()
   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = None,					)							->					str:






     """simple docstring"""






     a__						=guidance_scale > 1.0

     a__						=image.to(device=self.device,					dtype=text_embeds.dtype )
     a__						=self.vae.encode(lowercase_ ).latent_dist.sample(lowercase_ )

     a__						=self.vae.config.scaling_factor * latent

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

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

         a__						=model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = 3.0,					lowercase_ = 50,					lowercase_ = 0.8,					lowercase_ = None,					lowercase_ = None,					lowercase_ = "pil",					)							->					Tuple:






     """simple docstring"""






     a__						=guidance_scale > 1.0

     a__						=self.image_processor.preprocess(lowercase_ )

     a__						=self._encode_prompt(lowercase_,					lowercase_,					lowercase_ )
     a__						=self._encode_prompt(lowercase_,					lowercase_,					lowercase_ )

     self.scheduler.set_timesteps(lowercase_,					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(lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )

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

         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''',					lowercase_,					standard_warn=lowercase_ )
       a__						='''np'''

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

     return image



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
   lowerCamelCase__				:			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
       from .modeling_roberta_series import RobertaSeriesModelWithTransformation
       from .pipeline_alt_diffusion import AltDiffusionPipeline
       from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
