

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase								=logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				None







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_=0.999,   lowerCAmelCase_="cosine",   ):





     """simple docstring"""







     if alpha_transform_type == "cosine":

          def alpha_bar_fn(lowerCAmelCase_      ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(lowerCAmelCase_      ):
               return math.exp(t * -12.0      )

     else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'      )

     SCREAMING_SNAKE_CASE										=[]
     for i in range(lowerCAmelCase_      ):
          SCREAMING_SNAKE_CASE										=i / num_diffusion_timesteps
          SCREAMING_SNAKE_CASE										=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(lowerCAmelCase_      ) / alpha_bar_fn(lowerCAmelCase_      ),   lowerCAmelCase_      )      )
     return torch.tensor(lowerCAmelCase_,   dtype=torch.floataa      )



class 		a_	(							lowerCamelCase_			,      lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				[e.name for e in KarrasDiffusionSchedulers]
      __UpperCAmelCase      		=				1
      @register_to_config
      def __init__(				self					:							List[Any]      ,snake_case					:							int = 1000      ,snake_case					:							float = 0.0_001      ,snake_case					:							float = 0.02      ,snake_case					:							str = "linear"      ,snake_case					:							Optional[Union[np.ndarray, List[float]]] = None      ,snake_case					:							str = "epsilon"      ,snake_case					:							str = "linear"      ,snake_case					:							Optional[bool] = False      ,snake_case					:							str = "linspace"      ,snake_case					:							int = 0      ,):
           if trained_betas is not None:
                SCREAMING_SNAKE_CASE										=torch.tensor(snake_case      ,dtype=torch.floataa    )
           elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE										=torch.linspace(snake_case      ,snake_case      ,snake_case      ,dtype=torch.floataa    )
           elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                SCREAMING_SNAKE_CASE										=(
                    torch.linspace(beta_start**0.5      ,beta_end**0.5      ,snake_case      ,dtype=torch.floataa    ) ** 2
                )
           elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE										=betas_for_alpha_bar(snake_case    )
           else:
                raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

           SCREAMING_SNAKE_CASE										=1.0 - self.betas
           SCREAMING_SNAKE_CASE										=torch.cumprod(self.alphas      ,dim=0    )

           SCREAMING_SNAKE_CASE										=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
           SCREAMING_SNAKE_CASE										=np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    )

           # setable values
           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=np.linspace(0      ,num_train_timesteps - 1      ,snake_case      ,dtype=snake_case    )[::-1].copy()
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    )
           SCREAMING_SNAKE_CASE										=False
           SCREAMING_SNAKE_CASE										=use_karras_sigmas
      @property
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							torch.FloatTensor      ,snake_case					:							Union[float, torch.FloatTensor]    ):
           if isinstance(snake_case      ,torch.Tensor    ):
                SCREAMING_SNAKE_CASE										=timestep.to(self.timesteps.device    )
           SCREAMING_SNAKE_CASE										=(self.timesteps == timestep).nonzero().item()
           SCREAMING_SNAKE_CASE										=self.sigmas[step_index]

           SCREAMING_SNAKE_CASE										=sample / ((sigma**2 + 1) ** 0.5)

           SCREAMING_SNAKE_CASE										=True
           return sample
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							int      ,snake_case					:							Union[str, torch.device] = None    ):
           SCREAMING_SNAKE_CASE										=num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                SCREAMING_SNAKE_CASE										=np.linspace(0      ,self.config.num_train_timesteps - 1      ,snake_case      ,dtype=snake_case    )[
                    ::-1
                ].copy()
           elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE										=self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE										=(np.arange(0      ,snake_case    ) * step_ratio).round()[::-1].copy().astype(snake_case    )
                timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE										=self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE										=(np.arange(self.config.num_train_timesteps      ,0      ,-step_ratio    )).round().copy().astype(snake_case    )
                timesteps -= 1
           else:
                raise ValueError(
                    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'    )

           SCREAMING_SNAKE_CASE										=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
           SCREAMING_SNAKE_CASE										=np.log(snake_case    )

           if self.config.interpolation_type == "linear":
                SCREAMING_SNAKE_CASE										=np.interp(snake_case      ,np.arange(0      ,len(snake_case    )    )      ,snake_case    )
           elif self.config.interpolation_type == "log_linear":
                SCREAMING_SNAKE_CASE										=torch.linspace(np.log(sigmas[-1]    )      ,np.log(sigmas[0]    )      ,num_inference_steps + 1    ).exp()
           else:
                raise ValueError(
                    f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                    ' \'linear\' or \'log_linear\''    )

           if self.use_karras_sigmas:
                SCREAMING_SNAKE_CASE										=self._convert_to_karras(in_sigmas=snake_case      ,num_inference_steps=self.num_inference_steps    )
                SCREAMING_SNAKE_CASE										=np.array([self._sigma_to_t(snake_case      ,snake_case    ) for sigma in sigmas]    )

           SCREAMING_SNAKE_CASE										=np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case    )
           if str(snake_case    ).startswith('mps'    ):
                # mps does not support float64
                SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(snake_case      ,dtype=torch.floataa    )
           else:
                SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]    ):
           # get log sigma
           SCREAMING_SNAKE_CASE										=np.log(snake_case    )

           # get distribution
           SCREAMING_SNAKE_CASE										=log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           SCREAMING_SNAKE_CASE										=np.cumsum((dists >= 0)      ,axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
           SCREAMING_SNAKE_CASE										=low_idx + 1

           SCREAMING_SNAKE_CASE										=log_sigmas[low_idx]
           SCREAMING_SNAKE_CASE										=log_sigmas[high_idx]

           # interpolate sigmas
           SCREAMING_SNAKE_CASE										=(low - log_sigma) / (low - high)
           SCREAMING_SNAKE_CASE										=np.clip(snake_case      ,0      ,1    )

           # transform interpolation to time range
           SCREAMING_SNAKE_CASE										=(1 - w) * low_idx + w * high_idx
           SCREAMING_SNAKE_CASE										=t.reshape(sigma.shape    )
           return t
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							torch.FloatTensor      ,snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=in_sigmas[-1].item()
           SCREAMING_SNAKE_CASE										=in_sigmas[0].item()

           SCREAMING_SNAKE_CASE										=7.0  # 7.0 is the value used in the paper
           SCREAMING_SNAKE_CASE										=np.linspace(0      ,1      ,snake_case    )
           SCREAMING_SNAKE_CASE										=sigma_min ** (1 / rho)
           SCREAMING_SNAKE_CASE										=sigma_max ** (1 / rho)
           SCREAMING_SNAKE_CASE										=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							torch.FloatTensor      ,snake_case					:							Union[float, torch.FloatTensor]      ,snake_case					:							torch.FloatTensor      ,snake_case					:							float = 0.0      ,snake_case					:							float = 0.0      ,snake_case					:							float = float('inf'    )      ,snake_case					:							float = 1.0      ,snake_case					:							Optional[torch.Generator] = None      ,snake_case					:							bool = True      ,):

           if (
               isinstance(snake_case      ,snake_case    )
               or isinstance(snake_case      ,torch.IntTensor    )
               or isinstance(snake_case      ,torch.LongTensor    )
           ):
                raise ValueError(
                    (
                        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                        ' one of the `scheduler.timesteps` as a timestep.'
                    )      ,)

           if not self.is_scale_input_called:
                logger.warning(
                    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                    'See `StableDiffusionPipeline` for a usage example.'    )

           if isinstance(snake_case      ,torch.Tensor    ):
                SCREAMING_SNAKE_CASE										=timestep.to(self.timesteps.device    )

           SCREAMING_SNAKE_CASE										=(self.timesteps == timestep).nonzero().item()
           SCREAMING_SNAKE_CASE										=self.sigmas[step_index]

           SCREAMING_SNAKE_CASE										=min(s_churn / (len(self.sigmas    ) - 1)      ,2**0.5 - 1    ) if s_tmin <= sigma <= s_tmax else 0.0

           SCREAMING_SNAKE_CASE										=randn_tensor(
               model_output.shape      ,dtype=model_output.dtype      ,device=model_output.device      ,generator=snake_case    )

           SCREAMING_SNAKE_CASE										=noise * s_noise
           SCREAMING_SNAKE_CASE										=sigma * (gamma + 1)

           if gamma > 0:
                SCREAMING_SNAKE_CASE										=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           # NOTE: "original_sample" should not be an expected prediction_type but is left in for
           # backwards compatibility
           if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
                SCREAMING_SNAKE_CASE										=model_output
           elif self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE										=sample - sigma_hat * model_output
           elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                SCREAMING_SNAKE_CASE										=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           else:
                raise ValueError(
                    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'    )

           # 2. Convert to an ODE derivative
           SCREAMING_SNAKE_CASE										=(sample - pred_original_sample) / sigma_hat

           SCREAMING_SNAKE_CASE										=self.sigmas[step_index + 1] - sigma_hat

           SCREAMING_SNAKE_CASE										=sample + derivative * dt

           if not return_dict:
                return (prev_sample,)

           return EulerDiscreteSchedulerOutput(prev_sample=snake_case      ,pred_original_sample=snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							torch.FloatTensor      ,snake_case					:							torch.FloatTensor      ,snake_case					:							torch.FloatTensor      ,):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           SCREAMING_SNAKE_CASE										=self.sigmas.to(device=original_samples.device      ,dtype=original_samples.dtype    )
           if original_samples.device.type == "mps" and torch.is_floating_point(snake_case    ):
                # mps does not support float64
                SCREAMING_SNAKE_CASE										=self.timesteps.to(original_samples.device      ,dtype=torch.floataa    )
                SCREAMING_SNAKE_CASE										=timesteps.to(original_samples.device      ,dtype=torch.floataa    )
           else:
                SCREAMING_SNAKE_CASE										=self.timesteps.to(original_samples.device    )
                SCREAMING_SNAKE_CASE										=timesteps.to(original_samples.device    )

           SCREAMING_SNAKE_CASE										=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

           SCREAMING_SNAKE_CASE										=sigmas[step_indices].flatten()
           while len(sigma.shape    ) < len(original_samples.shape    ):
                SCREAMING_SNAKE_CASE										=sigma.unsqueeze(-1    )

           SCREAMING_SNAKE_CASE										=original_samples + noise * sigma
           return noisy_samples


      def __len__(				self					:							Any    ):
           return self.config.num_train_timesteps




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										=self.get_dummy_inputs(snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe(**snake_case    ).images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

