


"""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 			__snake_case   (lowerCamelCase			,		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   __a   (     self:    List[str]   ):
										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""")     ,							)
										__lowerCamelCase          =      DDIMScheduler()
										__lowerCamelCase          =      {"""unet""": unet, """scheduler""": scheduler}
										return components




							def   __a   (     self:    Optional[Any]     ,							A_:    int     ,							A_:    Union[str, Any]=0   ):
										if str(A_   ).startswith("""mps"""   ):
													__lowerCamelCase          =      torch.manual_seed(A_   )
										else:
													__lowerCamelCase          =      torch.Generator(device=A_   ).manual_seed(A_   )
										__lowerCamelCase          =      {
										    """batch_size""": 1,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """output_type""": """numpy""",
										}
										return inputs




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      """cpu"""

										__lowerCamelCase          =      self.get_dummy_components()
										__lowerCamelCase          =      self.pipeline_class(**A_   )
										pipe.to(A_   )
										pipe.set_progress_bar_config(disable=A_   )

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

										self.assertEqual(image.shape     ,							(1, 32, 32, 3)   )
										__lowerCamelCase          =      np.array(
										    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]   )
										__lowerCamelCase          =      np.abs(image_slice.flatten() - expected_slice   ).max()
										self.assertLessEqual(A_     ,							1E-3   )




							def   __a   (     self:    List[Any]   ):
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )




							def   __a   (     self:    Dict   ):
										super().test_save_load_local(expected_max_difference=3E-3   )




							def   __a   (     self:    int   ):
										super().test_save_load_optional_components(expected_max_difference=3E-3   )




							def   __a   (     self:    str   ):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )




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




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      """google/ddpm-cifar10-32"""

										__lowerCamelCase          =      UNetaDModel.from_pretrained(A_   )
										__lowerCamelCase          =      DDIMScheduler()

										__lowerCamelCase          =      DDIMPipeline(unet=A_     ,							scheduler=A_   )
										ddim.to(A_   )
										ddim.set_progress_bar_config(disable=A_   )

										__lowerCamelCase          =      torch.manual_seed(0   )
										__lowerCamelCase          =      ddim(generator=A_     ,							eta=0.0     ,							output_type="""numpy"""   ).images

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

										assert image.shape == (1, 32, 32, 3)
										__lowerCamelCase          =      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   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      """google/ddpm-ema-bedroom-256"""

										__lowerCamelCase          =      UNetaDModel.from_pretrained(A_   )
										__lowerCamelCase          =      DDIMScheduler.from_pretrained(A_   )

										__lowerCamelCase          =      DDIMPipeline(unet=A_     ,							scheduler=A_   )
										ddpm.to(A_   )
										ddpm.set_progress_bar_config(disable=A_   )

										__lowerCamelCase          =      torch.manual_seed(0   )
										__lowerCamelCase          =      ddpm(generator=A_     ,							output_type="""numpy"""   ).images

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

										assert image.shape == (1, 2_56, 2_56, 3)
										__lowerCamelCase          =      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__magic_name__						: Tuple    =				{}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: str    =				['MLukeTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__magic_name__						: int    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




