



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



			def 				__lowercase					(			self							:				int		)							->					List[Any]:
									torch.manual_seed(0		)
									snake_case   :			Dict						    = 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")   ,	)
									snake_case   :			Any						    = DDIMScheduler()
									snake_case   :			Tuple						    = {"unet": unet, "scheduler": scheduler}
									return components



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				List[str]   ,	_lowercase							:				int=0		)							->					List[str]:
									if str(_lowercase		).startswith("mps"		):
															snake_case   :			Union[str, Any]						    = torch.manual_seed(_lowercase		)
									else:
															snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(_lowercase		)
									snake_case   :			Tuple						    = {
									    "batch_size": 1,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}
									return inputs



			def 				__lowercase					(			self							:				int		)							->					str:
									snake_case   :			Union[str, Any]						    = "cpu"

									snake_case   :			Optional[Any]						    = self.get_dummy_components()
									snake_case   :			Dict						    = self.pipeline_class(**_lowercase		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[int]						    = pipe(**_lowercase		).images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1]

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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)



			def 				__lowercase					(			self							:				Dict		)							->					List[str]:
									super().test_save_load_local(expected_max_difference=3E-3		)



			def 				__lowercase					(			self							:				str		)							->					Dict:
									super().test_save_load_optional_components(expected_max_difference=3E-3		)



			def 				__lowercase					(			self							:				int		)							->					int:
									super().test_inference_batch_single_identical(expected_max_diff=3E-3		)



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



			def 				__lowercase					(			self							:				List[str]		)							->					Union[str, Any]:
									snake_case   :			Any						    = "google/ddpm-cifar10-32"

									snake_case   :			List[Any]						    = UNetaDModel.from_pretrained(_lowercase		)
									snake_case   :			Dict						    = DDIMScheduler()

									snake_case   :			Tuple						    = DDIMPipeline(unet=_lowercase   ,	scheduler=_lowercase		)
									ddim.to(_lowercase		)
									ddim.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[int]						    = torch.manual_seed(0		)
									snake_case   :			Tuple						    = ddim(generator=_lowercase   ,	eta=0.0   ,	output_type="numpy"		).images

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									snake_case   :			Dict						    = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]		)

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



			def 				__lowercase					(			self							:				Tuple		)							->					str:
									snake_case   :			Dict						    = "google/ddpm-ema-bedroom-256"

									snake_case   :			Any						    = UNetaDModel.from_pretrained(_lowercase		)
									snake_case   :			str						    = DDIMScheduler.from_pretrained(_lowercase		)

									snake_case   :			Optional[int]						    = DDIMPipeline(unet=_lowercase   ,	scheduler=_lowercase		)
									ddpm.to(_lowercase		)
									ddpm.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Any						    = torch.manual_seed(0		)
									snake_case   :			Any						    = ddpm(generator=_lowercase   ,	output_type="numpy"		).images

									snake_case   :			List[str]						    = image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									snake_case   :			List[Any]						    = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]		)

									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


A		=						{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['GPTSw3Tokenizer']


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

else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



