



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 		UpperCAmelCase						(						unittest.TestCase				):





   @property
   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(0			)
         _lowerCAmelCase 			=  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''')       ,     )
         return model





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.dummy_uncond_unet
         _lowerCAmelCase 			=  ScoreSdeVeScheduler()

         _lowerCAmelCase 			=  ScoreSdeVePipeline(unet=_A       ,     scheduler=_A			)
         sde_ve.to(_A			)
         sde_ve.set_progress_bar_config(disable=_A			)

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  sde_ve(num_inference_steps=2       ,     output_type='''numpy'''       ,     generator=_A			).images

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  sde_ve(num_inference_steps=2       ,     output_type='''numpy'''       ,     generator=_A       ,     return_dict=_A			)[
             0
         ]

         _lowerCAmelCase 			=  image[0, -3:, -3:, -1]
         _lowerCAmelCase 			=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _lowerCAmelCase 			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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






@slow
@require_torch
class 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''google/ncsnpp-church-256'''
         _lowerCAmelCase 			=  UNetaDModel.from_pretrained(_A			)

         _lowerCAmelCase 			=  ScoreSdeVeScheduler.from_pretrained(_A			)

         _lowerCAmelCase 			=  ScoreSdeVePipeline(unet=_A       ,     scheduler=_A			)
         sde_ve.to(_A			)
         sde_ve.set_progress_bar_config(disable=_A			)

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  sde_ve(num_inference_steps=10       ,     output_type='''numpy'''       ,     generator=_A			).images

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

         assert image.shape == (1, 256, 256, 3)

         _lowerCAmelCase 			=  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]			)

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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
    from .camera import create_pan_cameras
    from .pipeline_shap_e import ShapEPipeline
    from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
    from .renderer import (
        BoundingBoxVolume,
        ImportanceRaySampler,
        MLPNeRFModelOutput,
        MLPNeRSTFModel,
        ShapEParamsProjModel,
        ShapERenderer,
        StratifiedRaySampler,
        VoidNeRFModel,
    )