


'''simple docstring'''



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 SchedulerMixin


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



@dataclass
class       a							(						_a			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					torch.FloatTensor



class       a							(						_a      ,    _a			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					List[str]				  =  1

     @register_to_config
     def __init__(							self	:						Dict				,					snake_case	:						int = 40				,					snake_case	:						float = 0.002				,					snake_case	:						float = 80.0				,					snake_case	:						float = 0.5				,					snake_case	:						float = 1.0				,					snake_case	:						float = 7.0				,					snake_case	:						bool = True				,					)		->				List[str]:
          __UpperCAmelCase      :					int    				=     sigma_max

          __UpperCAmelCase      :					Any    				=     np.linspace(0				,					1				,					_snake_case     )
          __UpperCAmelCase      :					str    				=     self._convert_to_karras(_snake_case     )
          __UpperCAmelCase      :					Tuple    				=     self.sigma_to_t(_snake_case     )

          # setable values
          __UpperCAmelCase      :					List[str]    				=     None
          __UpperCAmelCase      :					Union[str, Any]    				=     torch.from_numpy(_snake_case     )
          __UpperCAmelCase      :					int    				=     torch.from_numpy(_snake_case     )
          __UpperCAmelCase      :					str    				=     False
          __UpperCAmelCase      :					List[Any]    				=     False

     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						List[Any]				,					snake_case	:						Tuple=None     )		->				List[str]:


          if schedule_timesteps is None:
               __UpperCAmelCase      :					List[Any]    				=     self.timesteps

          __UpperCAmelCase      :					Any    				=     (schedule_timesteps == timestep).nonzero()
          return indices.item()

     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						Union[float, torch.FloatTensor]     )		->				Optional[int]:


          if isinstance(_snake_case				,					torch.Tensor     ):
               __UpperCAmelCase      :					List[str]    				=     timestep.to(self.timesteps.device     )
          __UpperCAmelCase      :					Tuple    				=     self.index_for_timestep(_snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.sigmas[step_idx]

          __UpperCAmelCase      :					List[str]    				=     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

          __UpperCAmelCase      :					Optional[int]    				=     True
          return sample

     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						Union[float, np.ndarray]     )		->				Union[str, Any]:


          if not isinstance(_snake_case				,					np.ndarray     ):
               __UpperCAmelCase      :					int    				=     np.array(_snake_case				,					dtype=np.floataa     )

          __UpperCAmelCase      :					Tuple    				=     1000 * 0.25 * np.log(sigmas + 1E-44     )

          return timesteps

     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						Optional[int] = None				,					snake_case	:						Union[str, torch.device] = None				,					snake_case	:						Optional[List[int]] = None				,					)		->				Tuple:


          if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''     )

          if num_inference_steps is not None and timesteps is not None:
               raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''     )

          # Follow DDPMScheduler custom timesteps logic
          if timesteps is not None:
               for i in range(1				,					len(_snake_case     )     ):
                    if timesteps[i] >= timesteps[i - 1]:
                         raise ValueError('''`timesteps` must be in descending order.'''     )

               if timesteps[0] >= self.config.num_train_timesteps:
                    raise ValueError(
                        f'`timesteps` must start before `self.config.train_timesteps`:'
                        f' {self.config.num_train_timesteps}.'     )

               __UpperCAmelCase      :					Tuple    				=     np.array(_snake_case				,					dtype=np.intaa     )
               __UpperCAmelCase      :					List[str]    				=     True
          else:
               if num_inference_steps > self.config.num_train_timesteps:
                    raise ValueError(
                        f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                        f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                        f' maximal {self.config.num_train_timesteps} timesteps.'     )

               __UpperCAmelCase      :					Optional[int]    				=     num_inference_steps

               __UpperCAmelCase      :					Optional[int]    				=     self.config.num_train_timesteps // self.num_inference_steps
               __UpperCAmelCase      :					Tuple    				=     (np.arange(0				,					_snake_case     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
               __UpperCAmelCase      :					Any    				=     False

          # Map timesteps to Karras sigmas directly for multistep sampling
          # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
          __UpperCAmelCase      :					Union[str, Any]    				=     self.config.num_train_timesteps
          __UpperCAmelCase      :					List[str]    				=     timesteps[::-1].copy()
          __UpperCAmelCase      :					Tuple    				=     ramp / (num_train_timesteps - 1)
          __UpperCAmelCase      :					List[Any]    				=     self._convert_to_karras(_snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.sigma_to_t(_snake_case     )

          __UpperCAmelCase      :					Optional[Any]    				=     np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
          __UpperCAmelCase      :					Dict    				=     torch.from_numpy(_snake_case     ).to(device=_snake_case     )

          if str(_snake_case     ).startswith('''mps'''     ):
               # mps does not support float64
               __UpperCAmelCase      :					Dict    				=     torch.from_numpy(_snake_case     ).to(_snake_case				,					dtype=torch.floataa     )
          else:
               __UpperCAmelCase      :					Any    				=     torch.from_numpy(_snake_case     ).to(device=_snake_case     )

     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						List[str]     )		->				Dict:
          __UpperCAmelCase      :					Optional[int]    				=     self.config.sigma_min
          __UpperCAmelCase      :					Any    				=     self.config.sigma_max

          __UpperCAmelCase      :					Dict    				=     self.config.rho
          __UpperCAmelCase      :					Any    				=     sigma_min ** (1 / rho)
          __UpperCAmelCase      :					Any    				=     sigma_max ** (1 / rho)
          __UpperCAmelCase      :					str    				=     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas

     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						Any     )		->				Any:
          __UpperCAmelCase      :					Dict    				=     self.config.sigma_data

          __UpperCAmelCase      :					Union[str, Any]    				=     sigma_data**2 / (sigma**2 + sigma_data**2)
          __UpperCAmelCase      :					Optional[Any]    				=     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out

     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						str     )		->				Any:
          __UpperCAmelCase      :					int    				=     self.config.sigma_min
          __UpperCAmelCase      :					Any    				=     self.config.sigma_data

          __UpperCAmelCase      :					Any    				=     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
          __UpperCAmelCase      :					List[Any]    				=     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out

     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						Union[float, torch.FloatTensor]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						Optional[torch.Generator] = None				,					snake_case	:						bool = True				,					)		->				str:


          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'''
                       f' `{self.__class__}.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     ):
               __UpperCAmelCase      :					Tuple    				=     timestep.to(self.timesteps.device     )

          __UpperCAmelCase      :					Optional[Any]    				=     self.config.sigma_min
          __UpperCAmelCase      :					List[Any]    				=     self.config.sigma_max

          __UpperCAmelCase      :					Optional[Any]    				=     self.index_for_timestep(_snake_case     )

          # sigma_next corresponds to next_t in original implementation
          __UpperCAmelCase      :					int    				=     self.sigmas[step_index]
          if step_index + 1 < self.config.num_train_timesteps:
               __UpperCAmelCase      :					Optional[int]    				=     self.sigmas[step_index + 1]
          else:
               # Set sigma_next to sigma_min
               __UpperCAmelCase      :					Optional[Any]    				=     self.sigmas[-1]

          # Get scalings for boundary conditions
          __UpperCAmelCase				,  __UpperCAmelCase      :					Optional[Any]    				=     self.get_scalings_for_boundary_condition(_snake_case     )

          # 1. Denoise model output using boundary conditions
          __UpperCAmelCase      :					List[Any]    				=     c_out * model_output + c_skip * sample
          if self.config.clip_denoised:
               __UpperCAmelCase      :					int    				=     denoised.clamp(-1				,					1     )

          # 2. Sample z ~ N(0, s_noise^2 * I)
          # Noise is not used for onestep sampling.
          if len(self.timesteps     ) > 1:
               __UpperCAmelCase      :					str    				=     randn_tensor(
                   model_output.shape				,					dtype=model_output.dtype				,					device=model_output.device				,					generator=_snake_case     )
          else:
               __UpperCAmelCase      :					int    				=     torch.zeros_like(_snake_case     )
          __UpperCAmelCase      :					Dict    				=     noise * self.config.s_noise

          __UpperCAmelCase      :					Optional[Any]    				=     sigma_next.clamp(min=_snake_case				,					max=_snake_case     )

          # 3. Return noisy sample
          # tau = sigma_hat, eps = sigma_min
          __UpperCAmelCase      :					Union[str, Any]    				=     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

          if not return_dict:
               return (prev_sample,)

          return CMStochasticIterativeSchedulerOutput(prev_sample=_snake_case     )

     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor				,					)		->				Tuple:
          __UpperCAmelCase      :					Union[str, Any]    				=     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
               __UpperCAmelCase      :					List[str]    				=     self.timesteps.to(original_samples.device				,					dtype=torch.floataa     )
               __UpperCAmelCase      :					List[Any]    				=     timesteps.to(original_samples.device				,					dtype=torch.floataa     )
          else:
               __UpperCAmelCase      :					List[Any]    				=     self.timesteps.to(original_samples.device     )
               __UpperCAmelCase      :					Union[str, Any]    				=     timesteps.to(original_samples.device     )

          __UpperCAmelCase      :					Tuple    				=     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          __UpperCAmelCase      :					Optional[Any]    				=     sigmas[step_indices].flatten()
          while len(sigma.shape     ) < len(original_samples.shape     ):
               __UpperCAmelCase      :					Union[str, Any]    				=     sigma.unsqueeze(-1     )

          __UpperCAmelCase      :					Union[str, Any]    				=     original_samples + noise * sigma
          return noisy_samples






     def __len__(							self	:						Optional[Any]     )		->				Dict:
          return self.config.num_train_timesteps





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       str		=    PaintByExamplePipeline
   UpperCAmelCase__    :       Tuple		=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCAmelCase__    :       Any		=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCAmelCase__    :       Union[str, Any]		=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_		=				UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=9   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        UpperCAmelCase_		=				PNDMScheduler(skip_prk_steps=_snake_case)
        torch.manual_seed(0)
        UpperCAmelCase_		=				AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        torch.manual_seed(0)
        UpperCAmelCase_		=				CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        UpperCAmelCase_		=				PaintByExampleImageEncoder(_snake_case   ,		proj_size=32)
        UpperCAmelCase_		=				CLIPImageProcessor(crop_size=32   ,		size=32)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				np.array(image.convert('''RGB'''))
        UpperCAmelCase_		=				image[None].transpose(0   ,		3   ,		1   ,		2)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
        return image







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Dict="cpu"   ,		_snake_case						:		int=0):

        """simple docstring"""


        UpperCAmelCase_		=				floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((32, 32))

        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_		=				torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_		=				torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_		=				{
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**_snake_case)
        UpperCAmelCase_		=				pipe.to('''cpu''')
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				self.get_dummy_inputs()
        UpperCAmelCase_		=				pipe(**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_		=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				'''cpu'''
        UpperCAmelCase_		=				self.get_dummy_inputs()
        inputs.pop('''mask_image''')
        UpperCAmelCase_		=				self.convert_to_pt(inputs.pop('''image'''))
        UpperCAmelCase_		=				image.clamp(0   ,		1) / 2

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**self.get_dummy_components())
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				pipe(image=_snake_case   ,		mask_image=mask_image[:, 0]   ,		**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				mask_image.cpu().permute(0   ,		2   ,		3   ,		1)[0]

        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')

        UpperCAmelCase_		=				pipe(**self.get_dummy_inputs())
        UpperCAmelCase_		=				output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class __snake_case     (     unittest.TestCase						):







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''')

        UpperCAmelCase_		=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(321)
        UpperCAmelCase_		=				pipe(
            image=_snake_case   ,		mask_image=_snake_case   ,		example_image=_snake_case   ,		generator=_snake_case   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_		=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






