




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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	_lowerCamelCase  (     _A				):

      """simple docstring"""

      lowerCAmelCase__      =   42
      lowerCAmelCase__      =   None







def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_=0.999   ,       lowercase_="cosine"   ,       )    ->		List[Any]:

    """simple docstring"""


    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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


class 	_lowerCamelCase  (     _A							,     _A				):

      """simple docstring"""

      lowerCAmelCase__      =   [e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__      =   1







      @register_to_config
      def __init__(       self   :	Tuple	,    snake_case   :	int = 1000	,    snake_case   :	float = 0.00_01	,    snake_case   :	float = 0.02	,    snake_case   :	str = "linear"	,    snake_case   :	Optional[Union[np.ndarray, List[float]]] = None	,    snake_case   :	Optional[bool] = False	,    snake_case   :	str = "epsilon"	,    snake_case   :	str = "linspace"	,    snake_case   :	int = 0	,    ):






          if trained_betas is not None:
              __UpperCamelCase											=  torch.tensor(UpperCamelCase__	,    dtype=torch.floataa			)
          elif beta_schedule == "linear":
              __UpperCamelCase											=  torch.linspace(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.floataa			)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __UpperCamelCase											=  (
                  torch.linspace(beta_start**0.5	,    beta_end**0.5	,    UpperCamelCase__	,    dtype=torch.floataa			) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __UpperCamelCase											=  betas_for_alpha_bar(UpperCamelCase__			)
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

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

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

          # setable values
          __UpperCamelCase											=  None
          __UpperCamelCase											=  use_karras_sigmas
          self.set_timesteps(UpperCamelCase__	,    UpperCamelCase__			)
          __UpperCamelCase											=  []
          __UpperCamelCase											=  False







      @property
      def  snake_case		(       self   :	Tuple			):






          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5







      def  snake_case		(       self   :	Dict	,    snake_case   :	torch.FloatTensor	,    snake_case   :	Union[float, torch.FloatTensor]			):






          if isinstance(UpperCamelCase__	,    torch.Tensor			):
              __UpperCamelCase											=  timestep.to(self.timesteps.device			)
          __UpperCamelCase											=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase											=  self.sigmas[step_index]
          __UpperCamelCase											=  sample / ((sigma**2 + 1) ** 0.5)
          __UpperCamelCase											=  True
          return sample







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	int	,    snake_case   :	Optional[int]	,    snake_case   :	Any			):






          def lms_derivative(snake_case   :	Tuple			):
              __UpperCamelCase											=  1.0
              for k in range(UpperCamelCase__			):
                  if current_order == k:
                      continue
                  prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
              return prod

          __UpperCamelCase											=  integrate.quad(UpperCamelCase__	,    self.sigmas[t]	,    self.sigmas[t + 1]	,    epsrel=1E-4			)[0]

          return integrated_coeff







      def  snake_case		(       self   :	Optional[Any]	,    snake_case   :	int	,    snake_case   :	Union[str, torch.device] = None			):
          __UpperCamelCase											=  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":
              __UpperCamelCase											=  np.linspace(0	,    self.config.num_train_timesteps - 1	,    UpperCamelCase__	,    dtype=UpperCamelCase__			)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              __UpperCamelCase											=  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
              __UpperCamelCase											=  (np.arange(0	,    UpperCamelCase__			) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__			)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              __UpperCamelCase											=  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
              __UpperCamelCase											=  (np.arange(self.config.num_train_timesteps	,    0	,    -step_ratio			)).round().copy().astype(UpperCamelCase__			)
              timesteps -= 1
          else:
              raise ValueError(
                  F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."			)

          __UpperCamelCase											=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          __UpperCamelCase											=  np.log(UpperCamelCase__			)
          __UpperCamelCase											=  np.interp(UpperCamelCase__	,    np.arange(0	,    len(UpperCamelCase__			)			)	,    UpperCamelCase__			)

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

          __UpperCamelCase											=  np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)

          __UpperCamelCase											=  torch.from_numpy(UpperCamelCase__			).to(device=UpperCamelCase__			)
          if str(UpperCamelCase__			).startswith('''mps'''			):
              # mps does not support float64
              __UpperCamelCase											=  torch.from_numpy(UpperCamelCase__			).to(UpperCamelCase__	,    dtype=torch.floataa			)
          else:
              __UpperCamelCase											=  torch.from_numpy(UpperCamelCase__			).to(device=UpperCamelCase__			)

          __UpperCamelCase											=  []







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Dict	,    snake_case   :	List[Any]			):
          __UpperCamelCase											=  np.log(UpperCamelCase__			)

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

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

          __UpperCamelCase											=  log_sigmas[low_idx]
          __UpperCamelCase											=  log_sigmas[high_idx]

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

          # transform interpolation to time range
          __UpperCamelCase											=  (1 - w) * low_idx + w * high_idx
          __UpperCamelCase											=  t.reshape(sigma.shape			)
          return t







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	torch.FloatTensor			):
          __UpperCamelCase											=  in_sigmas[-1].item()
          __UpperCamelCase											=  in_sigmas[0].item()

          __UpperCamelCase											=  7.0  # 7.0 is the value used in the paper
          __UpperCamelCase											=  np.linspace(0	,    1	,    self.num_inference_steps			)
          __UpperCamelCase											=  sigma_min ** (1 / rho)
          __UpperCamelCase											=  sigma_max ** (1 / rho)
          __UpperCamelCase											=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas







      def  snake_case		(       self   :	List[str]	,    snake_case   :	torch.FloatTensor	,    snake_case   :	Union[float, torch.FloatTensor]	,    snake_case   :	torch.FloatTensor	,    snake_case   :	int = 4	,    snake_case   :	bool = True	,    ):






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

          if isinstance(UpperCamelCase__	,    torch.Tensor			):
              __UpperCamelCase											=  timestep.to(self.timesteps.device			)
          __UpperCamelCase											=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase											=  self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase											=  sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase											=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              __UpperCamelCase											=  model_output
          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
          __UpperCamelCase											=  (sample - pred_original_sample) / sigma
          self.derivatives.append(UpperCamelCase__			)
          if len(self.derivatives			) > order:
              self.derivatives.pop(0			)

          # 3. Compute linear multistep coefficients
          __UpperCamelCase											=  min(step_index + 1	,    UpperCamelCase__			)
          __UpperCamelCase											=  [self.get_lms_coefficient(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__			) for curr_order in range(UpperCamelCase__			)]

          # 4. Compute previous sample based on the derivatives path
          __UpperCamelCase											=  sample + sum(
              coeff * derivative for coeff, derivative in zip(UpperCamelCase__	,    reversed(self.derivatives			)			)			)

          if not return_dict:
              return (prev_sample,)

          return LMSDiscreteSchedulerOutput(prev_sample=UpperCamelCase__	,    pred_original_sample=UpperCamelCase__			)







      def  snake_case		(       self   :	Tuple	,    snake_case   :	torch.FloatTensor	,    snake_case   :	torch.FloatTensor	,    snake_case   :	torch.FloatTensor	,    ):
          __UpperCamelCase											=  self.sigmas.to(device=original_samples.device	,    dtype=original_samples.dtype			)
          if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__			):
              # mps does not support float64
              __UpperCamelCase											=  self.timesteps.to(original_samples.device	,    dtype=torch.floataa			)
              __UpperCamelCase											=  timesteps.to(original_samples.device	,    dtype=torch.floataa			)
          else:
              __UpperCamelCase											=  self.timesteps.to(original_samples.device			)
              __UpperCamelCase											=  timesteps.to(original_samples.device			)

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

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

          __UpperCamelCase											=  original_samples + noise * sigma
          return noisy_samples







      def __len__(       self   :	str			):
          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 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   RePaintPipeline
      lowerCAmelCase__     :	Optional[int]      =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      lowerCAmelCase__     :	Tuple      =   PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      lowerCAmelCase__     :	List[str]      =   IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__     :	Dict      =   False







      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  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''')	,    )
          __UpperCamelCase											=  RePaintScheduler()
          __UpperCamelCase											=  {'''unet''': unet, '''scheduler''': scheduler}
          return components







      def  snake_case		(       self   :	int	,    snake_case   :	str	,    snake_case   :	List[Any]=0			):
          if str(snake_case			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(snake_case			)
          else:
              __UpperCamelCase											=  torch.Generator(device=snake_case			).manual_seed(snake_case			)
          __UpperCamelCase											=  np.random.RandomState(snake_case			).standard_normal((1, 3, 32, 32)			)
          __UpperCamelCase											=  torch.from_numpy(snake_case			).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  (image > 0).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  {
              '''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  snake_case		(       self   :	str			):
          __UpperCamelCase											=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase											=  self.get_dummy_components()
          __UpperCamelCase											=  RePaintPipeline(**snake_case			)
          __UpperCamelCase											=  sd_pipe.to(snake_case			)
          sd_pipe.set_progress_bar_config(disable=snake_case			)

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

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3







      @skip_mps
      def  snake_case		(       self   :	str			):
          return super().test_save_load_local()







      @unittest.skip('''non-deterministic pipeline'''			)
      def  snake_case		(       self   :	Any			):
          return super().test_inference_batch_single_identical()







      @skip_mps
      def  snake_case		(       self   :	List[Any]			):
          return super().test_dict_tuple_outputs_equivalent()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_save_load_optional_components()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_attention_slicing_forward_pass()







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

      """simple docstring"""

      def  snake_case		(       self   :	Dict			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







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

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

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

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  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'''	,    )
          __UpperCamelCase											=  output.images[0]

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






