




'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      DDIMPipeline
   _a							=      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   _a							=      PipelineTesterMixin.required_optional_params - {
       """num_images_per_prompt""",
       """latents""",
       """callback""",
       """callback_steps""",
   }
   _a							=      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   _a							=      False







   def 		UpperCamelCase__			(	self      )				->    str:
     torch.manual_seed(0      )
     __a			=					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')      ,							)
     __a			=					DDIMScheduler()
     __a			=					{'unet': unet, 'scheduler': scheduler}
     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    int:
     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					'cpu'

     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      ).images
     __a			=					image[0, -3:, -3:, -1]

     self.assertEqual(image.shape      ,							(1, 32, 32, 3)      )
     __a			=					np.array(
         [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]      )
     __a			=					np.abs(image_slice.flatten() - expected_slice      ).max()
     self.assertLessEqual(UpperCamelCase      ,							1e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     super().test_save_load_local(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     super().test_save_load_optional_components(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					'google/ddpm-cifar10-32'

     __a			=					UNetaDModel.from_pretrained(UpperCamelCase      )
     __a			=					DDIMScheduler()

     __a			=					DDIMPipeline(unet=UpperCamelCase      ,							scheduler=UpperCamelCase      )
     ddim.to(UpperCamelCase      )
     ddim.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.manual_seed(0      )
     __a			=					ddim(generator=UpperCamelCase      ,							eta=0.0      ,							output_type='numpy'      ).images

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

     assert image.shape == (1, 32, 32, 3)
     __a			=					np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]      )

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







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					'google/ddpm-ema-bedroom-256'

     __a			=					UNetaDModel.from_pretrained(UpperCamelCase      )
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      )

     __a			=					DDIMPipeline(unet=UpperCamelCase      ,							scheduler=UpperCamelCase      )
     ddpm.to(UpperCamelCase      )
     ddpm.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.manual_seed(0      )
     __a			=					ddpm(generator=UpperCamelCase      ,							output_type='numpy'      ).images

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

     assert image.shape == (1, 256, 256, 3)
     __a			=					np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]      )

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









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



