





"""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  __A       (  a      ,   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 _snake_case    (						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""")				,	)
								lowerCamelCase			=DDIMScheduler()
								lowerCamelCase			={"""unet""": unet, """scheduler""": scheduler}
								return components


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):




								if str(_snake_case							).startswith("""mps"""							):
												lowerCamelCase			=torch.manual_seed(_snake_case							)
								else:
												lowerCamelCase			=torch.Generator(device=_snake_case							).manual_seed(_snake_case							)
								lowerCamelCase			={
								    """batch_size""": 1,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}
								return inputs


				def _snake_case    (						self							):
								lowerCamelCase			="""cpu"""

								lowerCamelCase			=self.get_dummy_components()
								lowerCamelCase			=self.pipeline_class(**_snake_case							)
								pipe.to(_snake_case							)
								pipe.set_progress_bar_config(disable=_snake_case							)

								lowerCamelCase			=self.get_dummy_inputs(_snake_case							)
								lowerCamelCase			=pipe(**_snake_case							).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(_snake_case				,	1E-3							)


				def _snake_case    (						self							):
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)


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


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


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




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


				def _snake_case    (						self							):
								lowerCamelCase			="""google/ddpm-cifar10-32"""

								lowerCamelCase			=UNetaDModel.from_pretrained(_snake_case							)
								lowerCamelCase			=DDIMScheduler()

								lowerCamelCase			=DDIMPipeline(unet=_snake_case				,	scheduler=_snake_case							)
								ddim.to(_snake_case							)
								ddim.set_progress_bar_config(disable=_snake_case							)

								lowerCamelCase			=torch.manual_seed(0							)
								lowerCamelCase			=ddim(generator=_snake_case				,	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_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]							)

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


				def _snake_case    (						self							):
								lowerCamelCase			="""google/ddpm-ema-bedroom-256"""

								lowerCamelCase			=UNetaDModel.from_pretrained(_snake_case							)
								lowerCamelCase			=DDIMScheduler.from_pretrained(_snake_case							)

								lowerCamelCase			=DDIMPipeline(unet=_snake_case				,	scheduler=_snake_case							)
								ddpm.to(_snake_case							)
								ddpm.set_progress_bar_config(disable=_snake_case							)

								lowerCamelCase			=torch.manual_seed(0							)
								lowerCamelCase			=ddpm(generator=_snake_case				,	output_type="""numpy"""							).images

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

								assert image.shape == (1, 256, 256, 3)
								lowerCamelCase			=np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]							)

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












from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels





