






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Union[str, Any]      ):
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.dummy_uncond_unet
       SCREAMING_SNAKE_CASE       :      Tuple							=    PNDMScheduler()

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    PNDMPipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       pndm.to(UpperCAmelCase_      )
       pndm.set_progress_bar_config(disable=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      int							=    pndm(generator=UpperCAmelCase_					, num_inference_steps=20					, output_type="numpy"      ).images

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    pndm(generator=UpperCAmelCase_					, num_inference_steps=20					, output_type="numpy"					, return_dict=UpperCAmelCase_      )[0]

       SCREAMING_SNAKE_CASE       :      Dict							=    image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    np.array([1.0, 1.0, 0.0, 1.0, 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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    "google/ddpm-cifar10-32"

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    UNetaDModel.from_pretrained(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    PNDMScheduler()

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    PNDMPipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       pndm.to(UpperCAmelCase_      )
       pndm.set_progress_bar_config(disable=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    pndm(generator=UpperCAmelCase_					, output_type="numpy"      ).images

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE       :      Tuple							=    np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

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














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


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .multicontrolnet import MultiControlNetModel
   from .pipeline_controlnet import StableDiffusionControlNetPipeline
   from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
   from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
   from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






