





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     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_ = 0.93							,    snake_case_ = True							,    ):
         _A    =       mixing_coeff
         _A    =       leapfrog_steps

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

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




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_ = None							,    snake_case_ = False       ):
         _A    =       self.tokenizer(
             snake_case_							,    padding='max_length'							,    max_length=self.tokenizer.model_max_length							,    truncation=snake_case_							,    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(
                 snake_case_							,    padding='max_length'							,    max_length=self.tokenizer.model_max_length							,    truncation=snake_case_							,    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							,    snake_case_							,    snake_case_       ):
         _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							,    snake_case_							,    snake_case_       ):
         _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							,    snake_case_       ):
         # as self.alphas_cumprod is always in cpu
         _A    =       int(snake_case_       )

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

         return alpha_prod, 1 - alpha_prod




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    ):
         _A    =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

         _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							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    ):
         _A    =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

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




     @torch.no_grad()
     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_ = None							,    ):
         _A    =       guidance_scale > 1.0

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

         _A    =       self.vae.config.scaling_factor * latent

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

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

                 _A    =       model_input

         return coupled_latents




     @torch.no_grad()
     def __call__(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_ = 3.0							,    snake_case_ = 50							,    snake_case_ = 0.8							,    snake_case_ = None							,    snake_case_ = None							,    snake_case_ = "pil"							,    ):
         _A    =       guidance_scale > 1.0

         _A    =       self.image_processor.preprocess(snake_case_       )

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

         self.scheduler.set_timesteps(snake_case_							,    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(snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       )

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

                 _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'							,    snake_case_							,    standard_warn=snake_case_       )
             _A    =       'np'

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

         return image












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





