


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class        UpperCamelCase__   (	lowercase_							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   UnCLIPImageVariationPipeline
					__SCREAMING_SNAKE_CASE       :  Tuple						=   IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
					__SCREAMING_SNAKE_CASE       :  Dict						=   IMAGE_VARIATION_BATCH_PARAMS

					__SCREAMING_SNAKE_CASE       :  Dict						=   [
					    '''generator''',
					    '''return_dict''',
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   False

					@property
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return 32

					@property
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										return 32

					@property
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										return self.time_input_dim

					@property
					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										return self.time_input_dim * 4

					@property
					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										return 100

					@property
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
										return tokenizer

					@property
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										torch.manual_seed(0       )
										lowercase_  							=			CLIPTextConfig(
										    bos_token_id=0							,					eos_token_id=2							,					hidden_size=self.text_embedder_hidden_size							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1_000							,					)
										return CLIPTextModelWithProjection(UpperCamelCase__       )

					@property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										torch.manual_seed(0       )
										lowercase_  							=			CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size							,					projection_dim=self.text_embedder_hidden_size							,					num_hidden_layers=5							,					num_attention_heads=4							,					image_size=32							,					intermediate_size=37							,					patch_size=1							,					)
										return CLIPVisionModelWithProjection(UpperCamelCase__       )

					@property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										torch.manual_seed(0       )

										lowercase_  							=			{
										    """clip_embeddings_dim""": self.text_embedder_hidden_size,
										    """time_embed_dim""": self.time_embed_dim,
										    """cross_attention_dim""": self.cross_attention_dim,
										}

										lowercase_  							=			UnCLIPTextProjModel(**UpperCamelCase__       )
										return model

					@property
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										torch.manual_seed(0       )

										lowercase_  							=			{
										    """sample_size""": 32,
										    # RGB in channels
										    """in_channels""": 3,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 6,
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": """identity""",
										}

										lowercase_  							=			UNetaDConditionModel(**UpperCamelCase__       )
										return model

					@property
					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										return {
										    "sample_size": 64,
										    "layers_per_block": 1,
										    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
										    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
										    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    "in_channels": 6,
										    "out_channels": 3,
										}

					@property
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										torch.manual_seed(0       )

										lowercase_  							=			UNetaDModel(**self.dummy_super_res_kwargs       )
										return model

					@property
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										torch.manual_seed(1       )

										lowercase_  							=			UNetaDModel(**self.dummy_super_res_kwargs       )
										return model

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			self.dummy_decoder
										lowercase_  							=			self.dummy_text_proj
										lowercase_  							=			self.dummy_text_encoder
										lowercase_  							=			self.dummy_tokenizer
										lowercase_  							=			self.dummy_super_res_first
										lowercase_  							=			self.dummy_super_res_last

										lowercase_  							=			UnCLIPScheduler(
										    variance_type="""learned_range"""							,					prediction_type="""epsilon"""							,					num_train_timesteps=1_000							,					)

										lowercase_  							=			UnCLIPScheduler(
										    variance_type="""fixed_small_log"""							,					prediction_type="""epsilon"""							,					num_train_timesteps=1_000							,					)

										lowercase_  							=			CLIPImageProcessor(crop_size=32							,					size=32       )

										lowercase_  							=			self.dummy_image_encoder

										return {
										    "decoder": decoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "text_proj": text_proj,
										    "feature_extractor": feature_extractor,
										    "image_encoder": image_encoder,
										    "super_res_first": super_res_first,
										    "super_res_last": super_res_last,
										    "decoder_scheduler": decoder_scheduler,
										    "super_res_scheduler": super_res_scheduler,
										}

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   str=0							,					UpperCamelCase__       :   Dict=True       ):






										'''simple docstring'''






										lowercase_  							=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )

										if pil_image:
															lowercase_  							=			input_image * 0.5 + 0.5
															lowercase_  							=			input_image.clamp(0							,					1       )
															lowercase_  							=			input_image.cpu().permute(0							,					2							,					3							,					1       ).float().numpy()
															lowercase_  							=			DiffusionPipeline.numpy_to_pil(UpperCamelCase__       )[0]

										return {
										    "image": input_image,
										    "generator": generator,
										    "decoder_num_inference_steps": 2,
										    "super_res_num_inference_steps": 2,
										    "output_type": "np",
										}

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()

										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										lowercase_  							=			pipe.to(UpperCamelCase__       )

										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )

										lowercase_  							=			pipe(**UpperCamelCase__       )
										lowercase_  							=			output.images

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )

										lowercase_  							=			pipe(
										    **UpperCamelCase__							,					return_dict=UpperCamelCase__							,					)[0]

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

										assert image.shape == (1, 64, 64, 3)

										lowercase_  							=			np.array(
										    [
										        0.9_997,
										        0.0_002,
										        0.9_997,
										        0.9_997,
										        0.9_969,
										        0.0_023,
										        0.9_997,
										        0.9_969,
										        0.9_970,
										    ]       )

										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   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()

										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										lowercase_  							=			pipe.to(UpperCamelCase__       )

										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )

										lowercase_  							=			pipe(**UpperCamelCase__       )
										lowercase_  							=			output.images

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )

										lowercase_  							=			pipe(
										    **UpperCamelCase__							,					return_dict=UpperCamelCase__							,					)[0]

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

										assert image.shape == (1, 64, 64, 3)

										lowercase_  							=			np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]       )

										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   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""

										lowercase_  							=			self.get_dummy_components()

										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										lowercase_  							=			pipe.to(UpperCamelCase__       )

										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )
										lowercase_  							=			[
										    pipeline_inputs["""image"""],
										    pipeline_inputs["""image"""],
										]

										lowercase_  							=			pipe(**UpperCamelCase__       )
										lowercase_  							=			output.images

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )
										lowercase_  							=			[
										    tuple_pipeline_inputs["""image"""],
										    tuple_pipeline_inputs["""image"""],
										]

										lowercase_  							=			pipe(
										    **UpperCamelCase__							,					return_dict=UpperCamelCase__							,					)[0]

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

										assert image.shape == (2, 64, 64, 3)

										lowercase_  							=			np.array(
										    [
										        0.9_997,
										        0.9_989,
										        0.0_008,
										        0.0_021,
										        0.9_960,
										        0.0_018,
										        0.0_014,
										        0.0_002,
										        0.9_933,
										    ]       )

										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   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			torch.device("""cpu"""       )







										class        UpperCamelCase__   :
															__SCREAMING_SNAKE_CASE       :  str						=   1



										lowercase_  							=			self.get_dummy_components()

										lowercase_  							=			self.pipeline_class(**UpperCamelCase__       )
										lowercase_  							=			pipe.to(UpperCamelCase__       )

										pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(0       )
										lowercase_  							=			pipe.decoder.dtype
										lowercase_  							=			1

										lowercase_  							=			(
										    batch_size,
										    pipe.decoder.config.in_channels,
										    pipe.decoder.config.sample_size,
										    pipe.decoder.config.sample_size,
										)
										lowercase_  							=			pipe.prepare_latents(
										    UpperCamelCase__							,					dtype=UpperCamelCase__							,					device=UpperCamelCase__							,					generator=UpperCamelCase__							,					latents=UpperCamelCase__							,					scheduler=DummyScheduler()       )

										lowercase_  							=			(
										    batch_size,
										    pipe.super_res_first.config.in_channels // 2,
										    pipe.super_res_first.config.sample_size,
										    pipe.super_res_first.config.sample_size,
										)
										lowercase_  							=			pipe.prepare_latents(
										    UpperCamelCase__							,					dtype=UpperCamelCase__							,					device=UpperCamelCase__							,					generator=UpperCamelCase__							,					latents=UpperCamelCase__							,					scheduler=DummyScheduler()       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )

										lowercase_  							=			pipe(
										    **UpperCamelCase__							,					decoder_latents=UpperCamelCase__							,					super_res_latents=UpperCamelCase__       ).images

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__							,					pil_image=UpperCamelCase__       )
										# Don't pass image, instead pass embedding
										lowercase_  							=			pipeline_inputs.pop("""image"""       )
										lowercase_  							=			pipe.image_encoder(UpperCamelCase__       ).image_embeds

										lowercase_  							=			pipe(
										    **UpperCamelCase__							,					decoder_latents=UpperCamelCase__							,					super_res_latents=UpperCamelCase__							,					image_embeddings=UpperCamelCase__							,					).images

										# make sure passing text embeddings manually is identical
										assert np.abs(img_out_a - img_out_a       ).max() < 1e-4

					@skip_mps
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			torch_device == """cpu"""

										# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
										lowercase_  							=			1e-2

										self._test_attention_slicing_forward_pass(
										    test_max_difference=UpperCamelCase__							,					expected_max_diff=UpperCamelCase__       )

					@skip_mps
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			torch_device == """cpu"""
										lowercase_  							=			True
										lowercase_  							=			[
										    """decoder_num_inference_steps""",
										    """super_res_num_inference_steps""",
										]

										self._test_inference_batch_single_identical(
										    test_max_difference=UpperCamelCase__							,					relax_max_difference=UpperCamelCase__							,					additional_params_copy_to_batched_inputs=UpperCamelCase__							,					)

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			[
										    """decoder_num_inference_steps""",
										    """super_res_num_inference_steps""",
										]

										if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															lowercase_  							=			[2, 3]
															self._test_inference_batch_consistent(
															    batch_sizes=UpperCamelCase__							,					additional_params_copy_to_batched_inputs=UpperCamelCase__							,					)
										else:
															self._test_inference_batch_consistent(
															    additional_params_copy_to_batched_inputs=UpperCamelCase__       )

					@skip_mps
					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return super().test_dict_tuple_outputs_equivalent()

					@skip_mps
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										return super().test_save_load_local()







					@skip_mps
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										return super().test_save_load_optional_components()







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

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""       )
										lowercase_  							=			load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""       )

										lowercase_  							=			UnCLIPImageVariationPipeline.from_pretrained(
										    """kakaobrain/karlo-v1-alpha-image-variations"""							,					torch_dtype=torch.floataa       )
										lowercase_  							=			pipeline.to(UpperCamelCase__       )
										pipeline.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			torch.Generator(device="""cpu"""       ).manual_seed(0       )
										lowercase_  							=			pipeline(
										    UpperCamelCase__							,					generator=UpperCamelCase__							,					output_type="""np"""							,					)

										lowercase_  							=			output.images[0]

										assert image.shape == (256, 256, 3)

										assert_mean_pixel_difference(UpperCamelCase__							,					UpperCamelCase__							,					15       )






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


