




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 a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		UnCLIPImageVariationPipeline
						UpperCamelCase_	:       int       	=		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
						UpperCamelCase_	:       List[str]       	=		IMAGE_VARIATION_BATCH_PARAMS

						UpperCamelCase_	:       Dict       	=		[
						    'generator',
						    'return_dict',
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]
						UpperCamelCase_	:       Tuple       	=		False






						@property
						def     UpperCAmelCase_      ( self :     str				)    ->						List[Any]:
													"""simple docstring"""


													return 32






						@property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													return 32






						@property
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													return self.time_input_dim






						@property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													return self.time_input_dim * 4






						@property
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[Any]:
													"""simple docstring"""


													return 100






						@property
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


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






						@property
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						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-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													return CLIPTextModelWithProjection(lowerCamelCase__				)






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						Union[str, 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(lowerCamelCase__				)






						@property
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						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(**lowerCamelCase__				)
													return model






						@property
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""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(**lowerCamelCase__				)
													return model






						@property
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Dict:
													"""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 :     str				)    ->						Any:
													"""simple docstring"""


													torch.manual_seed(0				)

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






						@property
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													torch.manual_seed(1				)

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






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Union[str, Any]:
													"""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 :     List[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[Any]=0				,							lowerCamelCase__ :     List[str]=True				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)

													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(lowerCamelCase__				)[0]

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






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''

													__lowercase      =    self.get_dummy_components()

													__lowercase      =    self.pipeline_class(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)

													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)

													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)

													__lowercase      =    pipe(
													    **lowerCamelCase__				,							return_dict=lowerCamelCase__				,							)[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_9_9_7,
													        0.0_0_0_2,
													        0.9_9_9_7,
													        0.9_9_9_7,
													        0.9_9_6_9,
													        0.0_0_2_3,
													        0.9_9_9_7,
													        0.9_9_6_9,
													        0.9_9_7_0,
													    ]				)

													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]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''

													__lowercase      =    self.get_dummy_components()

													__lowercase      =    self.pipeline_class(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)

													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)

													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)

													__lowercase      =    pipe(
													    **lowerCamelCase__				,							return_dict=lowerCamelCase__				,							)[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_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]				)

													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				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''

													__lowercase      =    self.get_dummy_components()

													__lowercase      =    self.pipeline_class(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)

													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)
													__lowercase      =    [
													    pipeline_inputs['''image'''],
													    pipeline_inputs['''image'''],
													]

													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)
													__lowercase      =    [
													    tuple_pipeline_inputs['''image'''],
													    tuple_pipeline_inputs['''image'''],
													]

													__lowercase      =    pipe(
													    **lowerCamelCase__				,							return_dict=lowerCamelCase__				,							)[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_9_9_7,
													        0.9_9_8_9,
													        0.0_0_0_8,
													        0.0_0_2_1,
													        0.9_9_6_0,
													        0.0_0_1_8,
													        0.0_0_1_4,
													        0.0_0_0_2,
													        0.9_9_3_3,
													    ]				)

													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]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    torch.device('''cpu'''				)







													class a :
																			"""simple docstring"""

																			UpperCamelCase_	:       Any       	=		1





													__lowercase      =    self.get_dummy_components()

													__lowercase      =    self.pipeline_class(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)

													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    torch.Generator(device=lowerCamelCase__				).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(
													    lowerCamelCase__				,							dtype=lowerCamelCase__				,							device=lowerCamelCase__				,							generator=lowerCamelCase__				,							latents=lowerCamelCase__				,							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(
													    lowerCamelCase__				,							dtype=lowerCamelCase__				,							device=lowerCamelCase__				,							generator=lowerCamelCase__				,							latents=lowerCamelCase__				,							scheduler=DummyScheduler()				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				,							pil_image=lowerCamelCase__				)

													__lowercase      =    pipe(
													    **lowerCamelCase__				,							decoder_latents=lowerCamelCase__				,							super_res_latents=lowerCamelCase__				).images

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

													__lowercase      =    pipe(
													    **lowerCamelCase__				,							decoder_latents=lowerCamelCase__				,							super_res_latents=lowerCamelCase__				,							image_embeddings=lowerCamelCase__				,							).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 :     Optional[int]				)    ->						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=lowerCamelCase__				,							expected_max_diff=lowerCamelCase__				)






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[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=lowerCamelCase__				,							relax_max_difference=lowerCamelCase__				,							additional_params_copy_to_batched_inputs=lowerCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""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=lowerCamelCase__				,							additional_params_copy_to_batched_inputs=lowerCamelCase__				,							)
													else:
																				self._test_inference_batch_consistent(
																				    additional_params_copy_to_batched_inputs=lowerCamelCase__				)






						@skip_mps
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_local()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Any				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						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(lowerCamelCase__				)
													pipeline.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    torch.Generator(device='''cpu'''				).manual_seed(0				)
													__lowercase      =    pipeline(
													    lowerCamelCase__				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)

													__lowercase      =    output.images[0]

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

													assert_mean_pixel_difference(lowerCamelCase__				,							lowerCamelCase__				,							15				)












import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		(DEISMultistepScheduler,)
						UpperCamelCase_	:       Any       	=		(('num_inference_steps', 25),)






						def     UpperCAmelCase_      ( self :     List[str]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {
													    '''num_train_timesteps''': 1_000,
													    '''beta_start''': 0.0_0_0_1,
													    '''beta_end''': 0.0_2,
													    '''beta_schedule''': '''linear''',
													    '''solver_order''': 2,
													}

													config.update(**lowerCamelCase__				)
													return config






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]=0				,							**lowerCamelCase__ :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)
																				# copy over dummy past residuals
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											new_scheduler.set_timesteps(lowerCamelCase__				)
																											# copy over dummy past residuals
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase     ,     __lowercase      =    sample, sample
																				for t in range(lowerCamelCase__				,							time_step + scheduler.config.solver_order + 1				):
																											__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																											__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																											assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Any				)    ->						Tuple:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Any=0				,							**lowerCamelCase__ :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)

																				# copy over dummy past residuals (must be after setting timesteps)
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCamelCase__				)
																											__lowercase      =    scheduler_class.from_pretrained(lowerCamelCase__				)
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(lowerCamelCase__				)

																											# copy over dummy past residual (must be after setting timesteps)
																											__lowercase      =    dummy_past_residuals[: new_scheduler.config.solver_order]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int]=None				,							**lowerCamelCase__ :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													if scheduler is None:
																				__lowercase      =    self.scheduler_classes[0]
																				__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													return sample






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)

													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

																				__lowercase      =    self.dummy_sample
																				__lowercase      =    0.1 * sample

																				if num_inference_steps is not None and hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											scheduler.set_timesteps(lowerCamelCase__				)
																				elif num_inference_steps is not None and not hasattr(lowerCamelCase__				,							'''set_timesteps'''				):
																											__lowercase      =    num_inference_steps

																				# copy over dummy past residuals (must be done after set_timesteps)
																				__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

																				__lowercase      =    scheduler.timesteps[5]
																				__lowercase      =    scheduler.timesteps[6]

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

																				self.assertEqual(output_a.shape				,							sample.shape				)
																				self.assertEqual(output_a.shape				,							output_a.shape				)






						def     UpperCAmelCase_      ( self :     str				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    DEISMultistepScheduler(**self.get_scheduler_config()				)
													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

													__lowercase      =    DPMSolverSinglestepScheduler.from_config(scheduler.config				)
													__lowercase      =    DPMSolverMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    UniPCMultistepScheduler.from_config(scheduler.config				)
													__lowercase      =    DEISMultistepScheduler.from_config(scheduler.config				)

													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													for timesteps in [25, 50, 100, 999, 1_000]:
																				self.check_over_configs(num_train_timesteps=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Any:
													"""simple docstring"""


													self.check_over_configs(thresholding=lowerCamelCase__				)
													for order in [1, 2, 3]:
																				for solver_type in ["logrho"]:
																											for threshold in [0.5, 1.0, 2.0]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    thresholding=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							sample_max_value=lowerCamelCase__				,							algorithm_type='''deis'''				,							solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													for algorithm_type in ["deis"]:
																				for solver_type in ["logrho"]:
																											for order in [1, 2, 3]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									__lowercase      =    self.full_loop(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									assert not torch.isnan(lowerCamelCase__				).any(), "Samples have nan numbers"






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													self.check_over_configs(lower_order_final=lowerCamelCase__				)
													self.check_over_configs(lower_order_final=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																				self.check_over_forward(num_inference_steps=lowerCamelCase__				,							time_step=0				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop()
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop(prediction_type='''v_prediction'''				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.0_9_1				) < 1e-3






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(thresholding=lowerCamelCase__				,							dynamic_thresholding_ratio=0				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter.half()
													scheduler.set_timesteps(lowerCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

													assert sample.dtype == torch.floataa






