



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


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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		UpperCAmelCase    (    snake_case__					):
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  None






def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int  ,					_SCREAMING_SNAKE_CASE :						Tuple=0.999  ,					_SCREAMING_SNAKE_CASE :						Dict="cosine"  ,					)->Optional[Any]:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_SCREAMING_SNAKE_CASE :						Optional[Any]					):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_SCREAMING_SNAKE_CASE :						Dict					):
                  return math.exp(t * -1_2.0					)

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

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





class 		UpperCAmelCase    (    snake_case__							,snake_case__					):
   SCREAMING_SNAKE_CASE__					      =  [e.name for e in KarrasDiffusionSchedulers]
   SCREAMING_SNAKE_CASE__					      =  1





   @register_to_config
   def __init__(						self							,  _lowerCAmelCase = 1_000							,  _lowerCAmelCase = 0.0_001							,  _lowerCAmelCase = 0.02							,  _lowerCAmelCase = "linear"							,  _lowerCAmelCase = None							,  _lowerCAmelCase = "epsilon"							,  _lowerCAmelCase = "linear"							,  _lowerCAmelCase = False							,  _lowerCAmelCase = "linspace"							,  _lowerCAmelCase = 0							,  ):
         if trained_betas is not None:
               _lowerCAmelCase			      =	torch.tensor(__snake_case							,  dtype=torch.floataa      )
         elif beta_schedule == "linear":
               _lowerCAmelCase			      =	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.
               _lowerCAmelCase			      =	(
                   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
               _lowerCAmelCase			      =	betas_for_alpha_bar(__snake_case      )
         else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''      )

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

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

         # setable values
         _lowerCAmelCase			      =	None
         _lowerCAmelCase			      =	np.linspace(0							,  num_train_timesteps - 1							,  __snake_case							,  dtype=__snake_case      )[::-1].copy()
         _lowerCAmelCase			      =	torch.from_numpy(__snake_case      )
         _lowerCAmelCase			      =	False
         _lowerCAmelCase			      =	use_karras_sigmas





   @property
   def 						__lowerCAmelCase				(						self      ):
         # 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							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         if isinstance(__snake_case							,  torch.Tensor      ):
               _lowerCAmelCase			      =	timestep.to(self.timesteps.device      )
         _lowerCAmelCase			      =	(self.timesteps == timestep).nonzero().item()
         _lowerCAmelCase			      =	self.sigmas[step_index]

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

         _lowerCAmelCase			      =	True
         return sample





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase = None      ):
         _lowerCAmelCase			      =	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":
               _lowerCAmelCase			      =	np.linspace(0							,  self.config.num_train_timesteps - 1							,  __snake_case							,  dtype=__snake_case      )[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               _lowerCAmelCase			      =	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
               _lowerCAmelCase			      =	(np.arange(0							,  __snake_case      ) * step_ratio).round()[::-1].copy().astype(__snake_case      )
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               _lowerCAmelCase			      =	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
               _lowerCAmelCase			      =	(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\'.'''      )

         _lowerCAmelCase			      =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
         _lowerCAmelCase			      =	np.log(__snake_case      )

         if self.config.interpolation_type == "linear":
               _lowerCAmelCase			      =	np.interp(__snake_case							,  np.arange(0							,  len(__snake_case      )      )							,  __snake_case      )
         elif self.config.interpolation_type == "log_linear":
               _lowerCAmelCase			      =	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:
               _lowerCAmelCase			      =	self._convert_to_karras(in_sigmas=__snake_case							,  num_inference_steps=self.num_inference_steps      )
               _lowerCAmelCase			      =	np.array([self._sigma_to_t(__snake_case							,  __snake_case      ) for sigma in sigmas]      )

         _lowerCAmelCase			      =	np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
         _lowerCAmelCase			      =	torch.from_numpy(__snake_case      ).to(device=__snake_case      )
         if str(__snake_case      ).startswith('''mps'''      ):
               # mps does not support float64
               _lowerCAmelCase			      =	torch.from_numpy(__snake_case      ).to(__snake_case							,  dtype=torch.floataa      )
         else:
               _lowerCAmelCase			      =	torch.from_numpy(__snake_case      ).to(device=__snake_case      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	np.log(__snake_case      )

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

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

         _lowerCAmelCase			      =	log_sigmas[low_idx]
         _lowerCAmelCase			      =	log_sigmas[high_idx]

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

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	in_sigmas[-1].item()
         _lowerCAmelCase			      =	in_sigmas[0].item()

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase = 0.0							,  _lowerCAmelCase = 0.0							,  _lowerCAmelCase = float('''inf'''      )							,  _lowerCAmelCase = 1.0							,  _lowerCAmelCase = None							,  _lowerCAmelCase = 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      ):
               _lowerCAmelCase			      =	timestep.to(self.timesteps.device      )

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

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

         _lowerCAmelCase			      =	randn_tensor(
             model_output.shape							,  dtype=model_output.dtype							,  device=model_output.device							,  generator=__snake_case      )

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

         if gamma > 0:
               _lowerCAmelCase			      =	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":
               _lowerCAmelCase			      =	model_output
         elif self.config.prediction_type == "epsilon":
               _lowerCAmelCase			      =	sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _lowerCAmelCase			      =	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
         _lowerCAmelCase			      =	(sample - pred_original_sample) / sigma_hat

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

         _lowerCAmelCase			      =	sample + derivative * dt

         if not return_dict:
               return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=__snake_case							,  pred_original_sample=__snake_case      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	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
               _lowerCAmelCase			      =	self.timesteps.to(original_samples.device							,  dtype=torch.floataa      )
               _lowerCAmelCase			      =	timesteps.to(original_samples.device							,  dtype=torch.floataa      )
         else:
               _lowerCAmelCase			      =	self.timesteps.to(original_samples.device      )
               _lowerCAmelCase			      =	timesteps.to(original_samples.device      )

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

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

         _lowerCAmelCase			      =	original_samples + noise * sigma
         return noisy_samples





   def __len__(						self      ):
         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__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= 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_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   List[str]							= {
           '''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					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= 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]						):





       '''simple docstring'''
       return super().test_save_load_local()





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





       '''simple docstring'''
       return super().test_inference_batch_single_identical()





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return super().test_save_load_optional_components()





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       return super().test_attention_slicing_forward_pass()





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






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def        _lowerCamelCase			(						self					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''						)
       UpperCAmelCase_				:   int							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''						)
       UpperCAmelCase_				:   List[Any]							= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''						)

       UpperCAmelCase_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

       UpperCAmelCase_				:   Optional[int]							= torch.manual_seed(0						)
       UpperCAmelCase_				:   List[str]							= 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_				:   Tuple							= output.images[0]

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