





"""simple docstring"""


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)
							snake_case__			:							Tuple										=       (ScoreSdeVeScheduler,)
							snake_case__			:							Optional[int]										=       ()

							@property
							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			4
								snake_case__      							=			3
								snake_case__      							=			8
								snake_case__      							=			8

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

								return sample

							@property
							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			4
								snake_case__      							=			3
								snake_case__      							=			8
								snake_case__      							=			8

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

								return sample

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

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

								return model

							def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			{
								    "num_train_timesteps": 2000,
								    "snr": 0.15,
								    "sigma_min": 0.01,
								    "sigma_max": 1348,
								    "sampling_eps": 1e-5,
								}

								config.update(**__SCREAMING_SNAKE_CASE       )
								return config

							def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=0				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

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

									snake_case__      							=			self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
									snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__SCREAMING_SNAKE_CASE       )
										snake_case__      							=			scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )

									snake_case__      							=			scheduler.step_pred(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample
									snake_case__      							=			new_scheduler.step_pred(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample

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

									snake_case__      							=			scheduler.step_correct(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample
									snake_case__      							=			new_scheduler.step_correct(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample

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

							def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=0				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )
								kwargs.update(__SCREAMING_SNAKE_CASE       )

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

									snake_case__      							=			self.get_scheduler_config()
									snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__SCREAMING_SNAKE_CASE       )
										snake_case__      							=			scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )

									snake_case__      							=			scheduler.step_pred(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample
									snake_case__      							=			new_scheduler.step_pred(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample

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

									snake_case__      							=			scheduler.step_correct(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample
									snake_case__      							=			new_scheduler.step_correct(
									    __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample

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

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								for timesteps in [10, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]				,   [1, 100, 1000]       ):
									self.check_over_configs(sigma_min=__SCREAMING_SNAKE_CASE				,   sigma_max=__SCREAMING_SNAKE_CASE       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

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

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

								snake_case__      							=			self.scheduler_classes[0]
								snake_case__      							=			self.get_scheduler_config()
								snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

								snake_case__      							=			3

								snake_case__      							=			self.dummy_model()
								snake_case__      							=			self.dummy_sample_deter

								scheduler.set_sigmas(__SCREAMING_SNAKE_CASE       )
								scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
								snake_case__      							=			torch.manual_seed(0       )

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

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

									with torch.no_grad():
										snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

									snake_case__      							=			scheduler.step_pred(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )
									snake_case__      							=			output.prev_sample, output.prev_sample_mean

								snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
								snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

								assert np.isclose(result_sum.item()				,   143_7275_8528.0       )
								assert np.isclose(result_mean.item()				,   1871_4530.0       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

								snake_case__      							=			kwargs.pop("num_inference_steps"				,   __SCREAMING_SNAKE_CASE       )

								for scheduler_class in self.scheduler_classes:
									snake_case__      							=			self.get_scheduler_config()
									snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

									snake_case__      							=			self.dummy_sample
									snake_case__      							=			0.1 * sample

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

									snake_case__      							=			scheduler.step_pred(__SCREAMING_SNAKE_CASE				,   0				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample
									snake_case__      							=			scheduler.step_pred(__SCREAMING_SNAKE_CASE				,   1				,   __SCREAMING_SNAKE_CASE				,   generator=torch.manual_seed(0       )				,   **__SCREAMING_SNAKE_CASE       ).prev_sample

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








"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

        self.register_modules(vae=__SCREAMING_SNAKE_CASE				,   text_encoder=__SCREAMING_SNAKE_CASE				,   tokenizer=__SCREAMING_SNAKE_CASE				,   unet=__SCREAMING_SNAKE_CASE				,   scheduler=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

        snake_case__      							=			self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )

       def __call__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 0.3				,   __SCREAMING_SNAKE_CASE = 0.6				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 42				,   __SCREAMING_SNAKE_CASE = 50				,   __SCREAMING_SNAKE_CASE = 7.5				,   ):






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

        torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,   ).to(self.device       )

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

        snake_case__      							=			torch.cat([latents] * 2       )

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

        with torch.no_grad():
         snake_case__      							=			self.unet(
             __SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

        snake_case__							,				snake_case__      							=			pred.chunk(2       )
        snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
         if i > tmax:
          if i < tmin:  # layout generation phase
           snake_case__      							=			self.scheduler.add_noise(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=__SCREAMING_SNAKE_CASE				,   )

           snake_case__      							=			(mix_factor * latents) + (
               1 - mix_factor
           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
           snake_case__      							=			torch.cat([input] * 2       )

          else:  # content generation phase
           snake_case__      							=			torch.cat([latents] * 2       )

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

          with torch.no_grad():
           snake_case__      							=			self.unet(
               __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   encoder_hidden_states=__SCREAMING_SNAKE_CASE				,   ).sample

          snake_case__							,				snake_case__      							=			pred.chunk(2       )
          snake_case__      							=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

