





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 						lowerCamelCase       (__lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   DDIMPipeline
							UpperCAmelCase_      						=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							UpperCAmelCase_      						=   PipelineTesterMixin.required_optional_params - {
							    "num_images_per_prompt",
							    "latents",
							    "callback",
							    "callback_steps",
							}
							UpperCAmelCase_      						=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					Optional[int]						)				->			List[str]:





														"""simple docstring"""
														torch.manual_seed(0						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						UNetaDModel(
														    block_out_channels=(3_2, 6_4),			layers_per_block=2,			sample_size=3_2,			in_channels=3,			out_channels=3,			down_block_types=("DownBlock2D", "AttnDownBlock2D"),			up_block_types=("AttnUpBlock2D", "UpBlock2D"),			)
														SCREAMING_SNAKE_CASE__       :      int     =						DDIMScheduler()
														SCREAMING_SNAKE_CASE__       :      str     =						{"unet": unet, "scheduler": scheduler}
														return components






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					str=0						)				->			Tuple:





														"""simple docstring"""
														if str(_UpperCAmelCase						).startswith("mps"						):
																					SCREAMING_SNAKE_CASE__       :      int     =						torch.manual_seed(_UpperCAmelCase						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{
														    "batch_size": 1,
														    "generator": generator,
														    "num_inference_steps": 2,
														    "output_type": "numpy",
														}
														return inputs






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"cpu"

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_dummy_components()
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.pipeline_class(**_UpperCAmelCase						)
														pipe.to(_UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_dummy_inputs(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						pipe(**_UpperCAmelCase						).images
														SCREAMING_SNAKE_CASE__       :      Tuple     =						image[0, -3:, -3:, -1]

														self.assertEqual(image.shape,			(1, 3_2, 3_2, 3)						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						np.array(
														    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						np.abs(image_slice.flatten() - expected_slice						).max()
														self.assertLessEqual(_UpperCAmelCase,			1E-3						)






							def  A_     ( self     :					Tuple						)				->			str:





														"""simple docstring"""
														super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														super().test_save_load_local(expected_max_difference=3E-3						)






							def  A_     ( self     :					Dict						)				->			str:





														"""simple docstring"""
														super().test_save_load_optional_components(expected_max_difference=3E-3						)






							def  A_     ( self     :					Tuple						)				->			Dict:





														"""simple docstring"""
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)


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







							"""simple docstring"""




							def  A_     ( self     :					List[Any]						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						"google/ddpm-cifar10-32"

														SCREAMING_SNAKE_CASE__       :      Dict     =						UNetaDModel.from_pretrained(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						DDIMScheduler()

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						DDIMPipeline(unet=_UpperCAmelCase,			scheduler=_UpperCAmelCase						)
														ddim.to(_UpperCAmelCase						)
														ddim.set_progress_bar_config(disable=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.manual_seed(0						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						ddim(generator=_UpperCAmelCase,			eta=0.0,			output_type="numpy"						).images

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

														assert image.shape == (1, 3_2, 3_2, 3)
														SCREAMING_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  A_     ( self     :					List[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"google/ddpm-ema-bedroom-256"

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						UNetaDModel.from_pretrained(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						DDIMScheduler.from_pretrained(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						DDIMPipeline(unet=_UpperCAmelCase,			scheduler=_UpperCAmelCase						)
														ddpm.to(_UpperCAmelCase						)
														ddpm.set_progress_bar_config(disable=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      str     =						torch.manual_seed(0						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						ddpm(generator=_UpperCAmelCase,			output_type="numpy"						).images

														SCREAMING_SNAKE_CASE__       :      int     =						image[0, -3:, -3:, -1]

														assert image.shape == (1, 2_5_6, 2_5_6, 3)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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













import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    "split_dict"			,				[
        SplitDict(),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42			,				dataset_name="my_dataset"       )}       ),
        SplitDict({"train": SplitInfo(name="train"			,				num_bytes=13_37			,				num_examples=42       )}       ),
        SplitDict({"train": SplitInfo()}       ),
    ]			,				)
def _a (  SCREAMING_SNAKE_CASE__      :  SplitDict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						split_dict._to_yaml_list()
							assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE__       )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														SCREAMING_SNAKE_CASE__       :      str     =						None
														# the split name of split_dict takes over the name of the split info object
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						split_name
							assert split_dict == reloaded







@pytest.mark.parametrize(
    "split_info"			,				[SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE__       ), SplitInfo(dataset_name="my_dataset"       )]       )
def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						asdict(SplitDict({"train": split_info}       )       )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name






