




'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				_snake_case	(			unittest.TestCase							):




			@property
			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										torch.manual_seed(0							)
										snake_case_			      =   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							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   "cpu"
										snake_case_			      =   self.dummy_uncond_unet
										snake_case_			      =   DDPMScheduler()

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.Generator(device=a__							).manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							).images

										snake_case_			      =   torch.Generator(device=a__							).manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"       ,							return_dict=a__							)[0]

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

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   np.array(
										    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_0_0, 1.199e-03, 2.648e-04, 5.101e-04]							)

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




			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   self.dummy_uncond_unet
										snake_case_			      =   DDPMScheduler(prediction_type="sample"							)

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							).images

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							num_inference_steps=2       ,							output_type="numpy"							)[0]

										snake_case_			      =   image[0, -3:, -3:, -1]
										snake_case_			      =   image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   1e-2 if torch_device != "mps" else 3e-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance




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




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   "google/ddpm-cifar10-32"

										snake_case_			      =   UNetaDModel.from_pretrained(a__							)
										snake_case_			      =   DDPMScheduler.from_pretrained(a__							)

										snake_case_			      =   DDPMPipeline(unet=a__       ,							scheduler=a__							)
										ddpm.to(a__							)
										ddpm.set_progress_bar_config(disable=a__							)

										snake_case_			      =   torch.manual_seed(0							)
										snake_case_			      =   ddpm(generator=a__       ,							output_type="numpy"							).images

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

										assert image.shape == (1, 32, 32, 3)
										snake_case_			      =   np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]							)
										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				[
		    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MobileViTV2ForImageClassification",
		    "MobileViTV2ForSemanticSegmentation",
		    "MobileViTV2Model",
		    "MobileViTV2PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileViTVaConfig,
		    MobileViTVaOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTVaForImageClassification,
				    MobileViTVaForSemanticSegmentation,
				    MobileViTVaModel,
				    MobileViTVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							:     Any   	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




