
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 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = 0.93  ,						__UpperCamelCase = True  ,						):




        """simple docstring"""


        UpperCamelCase_											=							mixing_coeff
        UpperCamelCase_											=							leapfrog_steps

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

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ):




        """simple docstring"""


        UpperCamelCase_											=							self.tokenizer(
            __UpperCamelCase  ,						padding="""max_length"""  ,						max_length=self.tokenizer.model_max_length  ,						truncation=__UpperCamelCase  ,						return_tensors="""pt"""  ,						)

        UpperCamelCase_											=							self.text_encoder(text_inputs.input_ids.to(self.device  )  ).last_hidden_state

        UpperCamelCase_											=							prompt_embeds.to(dtype=self.text_encoder.dtype  ,						device=self.device  )

        if do_classifier_free_guidance:
               UpperCamelCase_											=							"""""" if negative_prompt is None else negative_prompt

               UpperCamelCase_											=							self.tokenizer(
                   __UpperCamelCase  ,						padding="""max_length"""  ,						max_length=self.tokenizer.model_max_length  ,						truncation=__UpperCamelCase  ,						return_tensors="""pt"""  ,						)

               UpperCamelCase_											=							self.text_encoder(uncond_input.input_ids.to(self.device  )  ).last_hidden_state

               UpperCamelCase_											=							torch.cat([negative_prompt_embeds, prompt_embeds]  )

        return prompt_embeds

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        UpperCamelCase_											=							self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        UpperCamelCase_											=							(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							int(__UpperCamelCase  )

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

        return alpha_prod, 1 - alpha_prod

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


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

        UpperCamelCase_							,					UpperCamelCase_											=							self._get_alpha_and_beta(__UpperCamelCase  )
        UpperCamelCase_							,					UpperCamelCase_											=							self._get_alpha_and_beta(__UpperCamelCase  )

        UpperCamelCase_											=							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        UpperCamelCase_											=							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        UpperCamelCase_											=							(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


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

        UpperCamelCase_							,					UpperCamelCase_											=							self._get_alpha_and_beta(__UpperCamelCase  )
        UpperCamelCase_							,					UpperCamelCase_											=							self._get_alpha_and_beta(__UpperCamelCase  )

        UpperCamelCase_											=							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        UpperCamelCase_											=							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
        UpperCamelCase_											=							a_t * base + b_t * model_output

        return model_input, next_model_input.to(base.dtype  )

 @torch.no_grad()
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


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

 @torch.no_grad()
 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        UpperCamelCase_											=							guidance_scale > 1.0

        UpperCamelCase_											=							image.to(device=self.device  ,						dtype=text_embeds.dtype  )
        UpperCamelCase_											=							self.vae.encode(__UpperCamelCase  ).latent_dist.sample(__UpperCamelCase  )

        UpperCamelCase_											=							self.vae.config.scaling_factor * latent

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

        for i, t in tqdm(enumerate(__UpperCamelCase  )  ,						total=len(__UpperCamelCase  )  ):
               UpperCamelCase_											=							self.noise_mixing_layer(x=coupled_latents[0]  ,						y=coupled_latents[1]  )

               # j - model_input index, k - base index
               for j in range(2  ):
                      UpperCamelCase_											=							j ^ 1

                      if self.leapfrog_steps:
                             if i % 2 == 0:
                                    UpperCamelCase_							,					UpperCamelCase_											=							j, k

                      UpperCamelCase_											=							coupled_latents[j]
                      UpperCamelCase_											=							coupled_latents[k]

                      UpperCamelCase_											=							torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

                      UpperCamelCase_											=							self.unet(__UpperCamelCase  ,						__UpperCamelCase  ,						encoder_hidden_states=__UpperCamelCase  ).sample

                      if do_classifier_free_guidance:
                             UpperCamelCase_							,					UpperCamelCase_											=							noise_pred.chunk(2  )
                             UpperCamelCase_											=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      UpperCamelCase_							,					UpperCamelCase_											=							self.noise_step(
                          base=__UpperCamelCase  ,						model_input=__UpperCamelCase  ,						model_output=__UpperCamelCase  ,						timestep=__UpperCamelCase  ,						)

                      UpperCamelCase_											=							model_input

        return coupled_latents

 @torch.no_grad()
 def __call__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = 3.0  ,						__UpperCamelCase = 5_0  ,						__UpperCamelCase = 0.8  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = "pil"  ,						):




        """simple docstring"""


        UpperCamelCase_											=							guidance_scale > 1.0

        UpperCamelCase_											=							self.image_processor.preprocess(__UpperCamelCase  )

        UpperCamelCase_											=							self._encode_prompt(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self._encode_prompt(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )

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

        UpperCamelCase_											=							num_inference_steps - int(num_inference_steps * strength  )
        UpperCamelCase_											=							self.scheduler.timesteps[t_limit:]
        UpperCamelCase_											=							fwd_timesteps.flip(0  )

        UpperCamelCase_											=							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  ):
                      UpperCamelCase_											=							k ^ 1

                      if self.leapfrog_steps:
                             if i % 2 == 1:
                                    UpperCamelCase_							,					UpperCamelCase_											=							j, k

                      UpperCamelCase_											=							coupled_latents[j]
                      UpperCamelCase_											=							coupled_latents[k]

                      UpperCamelCase_											=							torch.cat([model_input] * 2  ) if do_classifier_free_guidance else model_input

                      UpperCamelCase_											=							self.unet(__UpperCamelCase  ,						__UpperCamelCase  ,						encoder_hidden_states=__UpperCamelCase  ).sample

                      if do_classifier_free_guidance:
                             UpperCamelCase_							,					UpperCamelCase_											=							noise_pred.chunk(2  )
                             UpperCamelCase_											=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      UpperCamelCase_							,					UpperCamelCase_											=							self.denoise_step(
                          base=__UpperCamelCase  ,						model_input=__UpperCamelCase  ,						model_output=__UpperCamelCase  ,						timestep=__UpperCamelCase  ,						)

                      UpperCamelCase_											=							model_input

               UpperCamelCase_											=							self.denoise_mixing_layer(x=coupled_latents[0]  ,						y=coupled_latents[1]  )

        # either one is fine
        UpperCamelCase_											=							coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_											=							(
                   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  )
               UpperCamelCase_											=							"""np"""

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

        return image





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert np.isclose(result_sum.item()  ,						14_372_758_528.0  )
        assert np.isclose(result_mean.item()  ,						18_714_530.0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )



