import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class   lowerCAmelCase    (       _SCREAMING_SNAKE_CASE      ,		unittest.TestCase      ):
     _SCREAMING_SNAKE_CASE  :  Optional[int]		=UnCLIPPipeline
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=TEXT_TO_IMAGE_PARAMS - {
         """negative_prompt""",
         """height""",
         """width""",
         """negative_prompt_embeds""",
         """guidance_scale""",
         """prompt_embeds""",
         """cross_attention_kwargs""",
     }
     _SCREAMING_SNAKE_CASE  :  List[Any]		=TEXT_TO_IMAGE_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE  :  str		=[
         """generator""",
         """return_dict""",
         """prior_num_inference_steps""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=False



     @property
     def       a__	(       self							):
            return 32



     @property
     def       a__	(       self							):
            return 32



     @property
     def       a__	(       self							):
            return self.time_input_dim



     @property
     def       a__	(       self							):
            return self.time_input_dim * 4



     @property
     def       a__	(       self							):
            return 100



     @property
     def       a__	(       self							):
            _A=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
            return tokenizer



     @property
     def       a__	(       self							):
            torch.manual_seed(0							)
            _A=						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=1000					,   )
            return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE							)



     @property
     def       a__	(       self							):
            torch.manual_seed(0							)

            _A=						{
                'num_attention_heads': 2,
                'attention_head_dim': 12,
                'embedding_dim': self.text_embedder_hidden_size,
                'num_layers': 1,
            }

            _A=						PriorTransformer(**_SCREAMING_SNAKE_CASE							)
            return model



     @property
     def       a__	(       self							):
            torch.manual_seed(0							)

            _A=						{
                'clip_embeddings_dim': self.text_embedder_hidden_size,
                'time_embed_dim': self.time_embed_dim,
                'cross_attention_dim': self.cross_attention_dim,
            }

            _A=						UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE							)
            return model



     @property
     def       a__	(       self							):
            torch.manual_seed(0							)

            _A=						{
                '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',
            }

            _A=						UNetaDConditionModel(**_SCREAMING_SNAKE_CASE							)
            return model



     @property
     def       a__	(       self							):
            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       a__	(       self							):
            torch.manual_seed(0							)

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



     @property
     def       a__	(       self							):
            # seeded differently to get different unet than `self.dummy_super_res_first`
            torch.manual_seed(1							)

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



     def       a__	(       self							):
            _A=						self.dummy_prior
            _A=						self.dummy_decoder
            _A=						self.dummy_text_proj
            _A=						self.dummy_text_encoder
            _A=						self.dummy_tokenizer
            _A=						self.dummy_super_res_first
            _A=						self.dummy_super_res_last

            _A=						UnCLIPScheduler(
                variance_type='fixed_small_log'					,   prediction_type='sample'					,   num_train_timesteps=1000					,   clip_sample_range=5.0					,   )

            _A=						UnCLIPScheduler(
                variance_type='learned_range'					,   prediction_type='epsilon'					,   num_train_timesteps=1000					,   )

            _A=						UnCLIPScheduler(
                variance_type='fixed_small_log'					,   prediction_type='epsilon'					,   num_train_timesteps=1000					,   )

            _A=						{
                'prior': prior,
                'decoder': decoder,
                'text_proj': text_proj,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'super_res_first': super_res_first,
                'super_res_last': super_res_last,
                'prior_scheduler': prior_scheduler,
                'decoder_scheduler': decoder_scheduler,
                'super_res_scheduler': super_res_scheduler,
            }

            return components



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=0							):
            if str(_SCREAMING_SNAKE_CASE							).startswith('mps'							):
                   _A=						torch.manual_seed(_SCREAMING_SNAKE_CASE							)
            else:
                   _A=						torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(_SCREAMING_SNAKE_CASE							)
            _A=						{
                'prompt': 'horse',
                'generator': generator,
                'prior_num_inference_steps': 2,
                'decoder_num_inference_steps': 2,
                'super_res_num_inference_steps': 2,
                'output_type': 'numpy',
            }
            return inputs



     def       a__	(       self							):
            _A=						'cpu'

            _A=						self.get_dummy_components()

            _A=						self.pipeline_class(**_SCREAMING_SNAKE_CASE							)
            _A=						pipe.to(_SCREAMING_SNAKE_CASE							)

            pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

            _A=						pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)							)
            _A=						output.images

            _A=						pipe(
                **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)					,   return_dict=_SCREAMING_SNAKE_CASE					,   )[0]

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

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

            _A=						np.array(
                [
                    0.9997,
                    0.9988,
                    0.0028,
                    0.9997,
                    0.9984,
                    0.9965,
                    0.0029,
                    0.9986,
                    0.0025,
                ]							)

            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       a__	(       self							):
            _A=						torch.device('cpu'							)





            class   lowerCAmelCase    :
                 _SCREAMING_SNAKE_CASE  :  Optional[int]		=1


            _A=						self.get_dummy_components()

            _A=						self.pipeline_class(**_SCREAMING_SNAKE_CASE							)
            _A=						pipe.to(_SCREAMING_SNAKE_CASE							)

            _A=						components['prior']
            _A=						components['decoder']
            _A=						components['super_res_first']
            _A=						components['tokenizer']
            _A=						components['text_encoder']

            _A=						torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
            _A=						prior.dtype
            _A=						1

            _A=						(batch_size, prior.config.embedding_dim)
            _A=						pipe.prepare_latents(
                _SCREAMING_SNAKE_CASE					,   dtype=_SCREAMING_SNAKE_CASE					,   device=_SCREAMING_SNAKE_CASE					,   generator=_SCREAMING_SNAKE_CASE					,   latents=_SCREAMING_SNAKE_CASE					,   scheduler=DummyScheduler()							)
            _A=						(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
            _A=						pipe.prepare_latents(
                _SCREAMING_SNAKE_CASE					,   dtype=_SCREAMING_SNAKE_CASE					,   device=_SCREAMING_SNAKE_CASE					,   generator=_SCREAMING_SNAKE_CASE					,   latents=_SCREAMING_SNAKE_CASE					,   scheduler=DummyScheduler()							)

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

            pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

            _A=						'this is a prompt example'

            _A=						torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
            _A=						pipe(
                [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   prior_num_inference_steps=2					,   decoder_num_inference_steps=2					,   super_res_num_inference_steps=2					,   prior_latents=_SCREAMING_SNAKE_CASE					,   decoder_latents=_SCREAMING_SNAKE_CASE					,   super_res_latents=_SCREAMING_SNAKE_CASE					,   output_type='np'					,   )
            _A=						output.images

            _A=						tokenizer(
                _SCREAMING_SNAKE_CASE					,   padding='max_length'					,   max_length=tokenizer.model_max_length					,   return_tensors='pt'					,   )
            _A=						text_encoder(text_inputs.input_ids							)
            _A=						text_inputs.attention_mask

            _A=						torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(0							)
            _A=						pipe(
                generator=_SCREAMING_SNAKE_CASE					,   prior_num_inference_steps=2					,   decoder_num_inference_steps=2					,   super_res_num_inference_steps=2					,   prior_latents=_SCREAMING_SNAKE_CASE					,   decoder_latents=_SCREAMING_SNAKE_CASE					,   super_res_latents=_SCREAMING_SNAKE_CASE					,   text_model_output=_SCREAMING_SNAKE_CASE					,   text_attention_mask=_SCREAMING_SNAKE_CASE					,   output_type='np'					,   )[0]

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



     @skip_mps
     def       a__	(       self							):
            _A=						torch_device == 'cpu'

            self._test_attention_slicing_forward_pass(test_max_difference=_SCREAMING_SNAKE_CASE					,   expected_max_diff=0.01							)



     @skip_mps
     def       a__	(       self							):
            _A=						torch_device == 'cpu'
            _A=						True
            _A=						[
                'prior_num_inference_steps',
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

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



     def       a__	(       self							):
            _A=						[
                'prior_num_inference_steps',
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

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



     @skip_mps
     def       a__	(       self							):
            return super().test_dict_tuple_outputs_equivalent()



     @skip_mps
     def       a__	(       self							):
            return super().test_save_load_local()





     @skip_mps
     def       a__	(       self							):
            return super().test_save_load_optional_components()





@nightly
class   lowerCAmelCase    (       unittest.TestCase      ):



     def       a__	(       self							):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





     def       a__	(       self							):
            _A=						load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/unclip/karlo_v1_alpha_horse_cpu.npy'							)

            _A=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							)
            pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

            _A=						torch.manual_seed(0							)
            _A=						pipeline(
                'horse'					,   num_images_per_prompt=1					,   generator=_SCREAMING_SNAKE_CASE					,   output_type='np'					,   )

            _A=						output.images[0]

            assert image.shape == (256, 256, 3)
            assert np.abs(expected_image - image							).max() < 1E-1





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



     def       a__	(       self							):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()



     def       a__	(       self							):
            _A=						load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/unclip/karlo_v1_alpha_horse_fp16.npy'							)

            _A=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'					,   torch_dtype=torch.floataa							)
            _A=						pipeline.to(_SCREAMING_SNAKE_CASE							)
            pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

            _A=						torch.Generator(device='cpu'							).manual_seed(0							)
            _A=						pipeline(
                'horse'					,   generator=_SCREAMING_SNAKE_CASE					,   output_type='np'					,   )

            _A=						output.images[0]

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

            assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE							)





     def       a__	(       self							):
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            _A=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'					,   torch_dtype=torch.floataa							)
            _A=						pipe.to(_SCREAMING_SNAKE_CASE							)
            pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
            pipe.enable_attention_slicing()
            pipe.enable_sequential_cpu_offload()

            _A=						pipe(
                'horse'					,   num_images_per_prompt=1					,   prior_num_inference_steps=2					,   decoder_num_inference_steps=2					,   super_res_num_inference_steps=2					,   output_type='np'					,   )

            _A=						torch.cuda.max_memory_allocated()
            # make sure that less than 7 GB is allocated
            assert mem_bytes < 7 * 10**9



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Optional[int]       =					(DPMSolverMultistepScheduler,)
							_lowerCamelCase				: Optional[Any]       =					(("""num_inference_steps""", 25),)





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								    """sample_max_value""": 1.0,
								    """algorithm_type""": """dpmsolver++""",
								    """solver_type""": """midpoint""",
								    """lower_order_final""": False,
								    """lambda_min_clipped""": -float("""inf"""    ),
								    """variance_type""": None,
								}

								config.update(**_SCREAMING_SNAKE_CASE    )
								return config





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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





							def   __magic_name__      (							self    ):
								pass





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self.dummy_sample
								a_     			=   0.1 * sample
								a_     			=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

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

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

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   new_scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE    ):
								if scheduler is None:
									a_     			=   self.scheduler_classes[0]
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								return sample





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   kwargs.pop("""num_inference_steps"""			,       _SCREAMING_SNAKE_CASE    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

									a_     			=   self.dummy_sample
									a_     			=   0.1 * sample

									if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE			,       """set_timesteps"""    ):
										scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE			,       """set_timesteps"""    ):
										a_     			=   num_inference_steps

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

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

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





							def   __magic_name__      (							self    ):
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       sample_max_value=_SCREAMING_SNAKE_CASE			,       algorithm_type="""dpmsolver++"""			,       solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       )





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





							def   __magic_name__      (							self    ):
								for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
														continue
												else:
													self.check_over_configs(
													    solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												a_     			=   self.full_loop(
												    solver_order=_SCREAMING_SNAKE_CASE			,       solver_type=_SCREAMING_SNAKE_CASE			,       prediction_type=_SCREAMING_SNAKE_CASE			,       algorithm_type=_SCREAMING_SNAKE_CASE			,       )
												assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





							def   __magic_name__      (							self    ):
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
								self.check_over_configs(lambda_min_clipped=-5.1    )





							def   __magic_name__      (							self    ):
								self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE    )
								self.check_over_configs(variance_type="""learned_range"""    )





							def   __magic_name__      (							self    ):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE			,       time_step=0    )





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop()
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0.8_7			,       sample_max_value=0.5    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 1.1_3_6_4    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_2_5_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.full_loop(prediction_type="""v_prediction"""			,       use_karras_sigmas=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.2_0_9_6    ) < 1E-3





							def   __magic_name__      (							self    ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a_     			=   DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3

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

								a_     			=   self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

								assert abs(result_mean.item() - 0.3_3_0_1    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0    )
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								a_     			=   10
								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

								assert sample.dtype == torch.floataa





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

									scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
									assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps