
'''simple docstring'''






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    MODEL_MAPPING,
			    BeitForImageClassification,
			    BeitForMaskedImageModeling,
			    BeitForSemanticSegmentation,
			    BeitModel,
			)
			from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			import PIL
			from PIL import Image

			from transformers import BeitImageProcessor




class     __snake_case		:
					def __init__(     self,    A,    A=100,    A=13,    A=30,    A=2,    A=3,    A=True,    A=True,    A=32,    A=4,    A=4,    A=37,    A="gelu",    A=0.1,    A=0.1,    A=10,    A=0.02,    A=3,    A=None,    A=[0, 1, 2, 3],    ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= parent
									lowerCamelCase					:			str									= 100
									lowerCamelCase					:			Tuple									= batch_size
									lowerCamelCase					:			Tuple									= image_size
									lowerCamelCase					:			Any									= patch_size
									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			int									= is_training
									lowerCamelCase					:			List[str]									= use_labels
									lowerCamelCase					:			Optional[int]									= hidden_size
									lowerCamelCase					:			str									= num_hidden_layers
									lowerCamelCase					:			Tuple									= num_attention_heads
									lowerCamelCase					:			Optional[Any]									= intermediate_size
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Optional[int]									= hidden_dropout_prob
									lowerCamelCase					:			str									= attention_probs_dropout_prob
									lowerCamelCase					:			Optional[Any]									= type_sequence_label_size
									lowerCamelCase					:			int									= initializer_range
									lowerCamelCase					:			List[Any]									= scope
									lowerCamelCase					:			Dict									= out_indices
									lowerCamelCase					:			List[str]									= num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowerCamelCase					:			Tuple									= (image_size // patch_size) ** 2
									lowerCamelCase					:			List[str]									= num_patches + 1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									lowerCamelCase					:			Dict									= None
									lowerCamelCase					:			Union[str, Any]									= None
									if self.use_labels:
													lowerCamelCase					:			Optional[int]									= ids_tensor([self.batch_size],    self.type_sequence_label_size      )
													lowerCamelCase					:			Tuple									= ids_tensor([self.batch_size, self.image_size, self.image_size],    self.num_labels      )

									lowerCamelCase					:			str									= self.get_config()

									return config, pixel_values, labels, pixel_labels
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return BeitConfig(
									    vocab_size=self.vocab_size,    image_size=self.image_size,    patch_size=self.patch_size,    num_channels=self.num_channels,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    intermediate_size=self.intermediate_size,    hidden_act=self.hidden_act,    hidden_dropout_prob=self.hidden_dropout_prob,    attention_probs_dropout_prob=self.attention_probs_dropout_prob,    is_decoder=A,    initializer_range=self.initializer_range,    out_indices=self.out_indices,    )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= BeitModel(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			Union[str, Any]									= model(A      )
									self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BeitForMaskedImageModeling(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			List[Any]									= model(A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length - 1, self.vocab_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.type_sequence_label_size
									lowerCamelCase					:			List[Any]									= BeitForImageClassification(A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			Any									= model(A,    labels=A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									lowerCamelCase					:			Dict									= 1
									lowerCamelCase					:			List[Any]									= BeitForImageClassification(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			List[Any]									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									lowerCamelCase					:			str									= model(A,    labels=A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.num_labels
									lowerCamelCase					:			int									= BeitForSemanticSegmentation(A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			str									= model(A      )
									self.parent.assertEqual(
									    result.logits.shape,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
									lowerCamelCase					:			Dict									= model(A,    labels=A      )
									self.parent.assertEqual(
									    result.logits.shape,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.prepare_config_and_inputs()
									lowerCamelCase					:			Union[str, Any]									= config_and_inputs
									lowerCamelCase					:			Optional[int]									= {'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(
					    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					_lowerCAmelCase			     =	(
					    {
					        '''feature-extraction''': BeitModel,
					        '''image-classification''': BeitForImageClassification,
					        '''image-segmentation''': BeitForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= BeitModelTester(self      )
									lowerCamelCase					:			Dict									= ConfigTester(self,    config_class=A,    has_text_modality=A,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					@unittest.skip(reason='BEiT does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@require_torch_multi_gpu
					@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			Dict									= model_class(A      )
													self.assertIsInstance(model.get_input_embeddings(),    (nn.Module)      )
													lowerCamelCase					:			Any									= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(A,    nn.Linear      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[Any]									= model_class(A      )
													lowerCamelCase					:			str									= inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase					:			Optional[Any]									= [*signature.parameters.keys()]

													lowerCamelCase					:			Dict									= ['pixel_values']
													self.assertListEqual(arg_names[:1],    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.model_tester.is_training:
													return

									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs_for_common()
									lowerCamelCase					:			List[str]									= True

									for model_class in self.all_model_classes:
													# we don't test BeitForMaskedImageModeling
													if model_class in [*get_values(A      ), BeitForMaskedImageModeling]:
																	continue

													lowerCamelCase					:			Optional[int]									= model_class(A      )
													model.to(A      )
													model.train()
													lowerCamelCase					:			Tuple									= self._prepare_for_class(A,    A,    return_labels=A      )
													lowerCamelCase					:			int									= model(**A      ).loss
													loss.backward()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
													return

									lowerCamelCase					:			Dict									= False
									lowerCamelCase					:			List[str]									= True

									for model_class in self.all_model_classes:
													# we don't test BeitForMaskedImageModeling
													if (
													    model_class in [*get_values(A      ), BeitForMaskedImageModeling]
													    or not model_class.supports_gradient_checkpointing
													):
																	continue

													lowerCamelCase					:			Dict									= model_class(A      )
													model.gradient_checkpointing_enable()
													model.to(A      )
													model.train()
													lowerCamelCase					:			int									= self._prepare_for_class(A,    A,    return_labels=A      )
													lowerCamelCase					:			Optional[int]									= model(**A      ).loss
													loss.backward()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.model_tester.prepare_config_and_inputs_for_common()

									lowerCamelCase					:			Optional[Any]									= _config_zero_init(A      )
									for model_class in self.all_model_classes:
													lowerCamelCase					:			List[Any]									= model_class(config=A      )
													for name, param in model.named_parameters():
																	# we skip lambda parameters as these require special initial values
																	# determined by config.layer_scale_init_value
																	if "lambda" in name:
																					continue
																	if param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item(),    [0.0, 1.0],    msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',    )




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			int									= BeitModel.from_pretrained(A      )
													self.assertIsNotNone(A      )





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Dict									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
				return image




@require_torch
@require_vision
class     __snake_case		(   unittest.TestCase):
					@cached_property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      ).to(A      )

									lowerCamelCase					:			Union[str, Any]									= self.default_image_processor
									lowerCamelCase					:			Dict									= prepare_img()
									lowerCamelCase					:			Tuple									= image_processor(images=A,    return_tensors='pt'      ).pixel_values.to(A      )

									# prepare bool_masked_pos
									lowerCamelCase					:			Dict									= torch.ones((1, 196),    dtype=torch.bool      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			str									= model(pixel_values=A,    bool_masked_pos=A      )
									lowerCamelCase					:			Optional[int]									= outputs.logits

									# verify the logits
									lowerCamelCase					:			Optional[int]									= torch.Size((1, 196, 8192)      )
									self.assertEqual(logits.shape,    A      )

									lowerCamelCase					:			Optional[int]									= torch.tensor(
									    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]      ).to(A      )

									self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3],    A,    atol=1e-2      )      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      ).to(A      )

									lowerCamelCase					:			str									= self.default_image_processor
									lowerCamelCase					:			Dict									= prepare_img()
									lowerCamelCase					:			List[str]									= image_processor(images=A,    return_tensors='pt'      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			Any									= model(**A      )
									lowerCamelCase					:			Dict									= outputs.logits

									# verify the logits
									lowerCamelCase					:			Tuple									= torch.Size((1, 1000)      )
									self.assertEqual(logits.shape,    A      )

									lowerCamelCase					:			Tuple									= torch.tensor([-1.2385, -1.0987, -1.0108]      ).to(A      )

									self.assertTrue(torch.allclose(logits[0, :3],    A,    atol=1e-4      )      )

									lowerCamelCase					:			List[str]									= 281
									self.assertEqual(logits.argmax(-1      ).item(),    A      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      ).to(
									    A      )

									lowerCamelCase					:			List[Any]									= self.default_image_processor
									lowerCamelCase					:			Dict									= prepare_img()
									lowerCamelCase					:			str									= image_processor(images=A,    return_tensors='pt'      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			List[str]									= model(**A      )
									lowerCamelCase					:			Optional[Any]									= outputs.logits

									# verify the logits
									lowerCamelCase					:			int									= torch.Size((1, 2_1841)      )
									self.assertEqual(logits.shape,    A      )

									lowerCamelCase					:			List[Any]									= torch.tensor([1.6881, -0.2787, 0.5901]      ).to(A      )

									self.assertTrue(torch.allclose(logits[0, :3],    A,    atol=1e-4      )      )

									lowerCamelCase					:			Union[str, Any]									= 2396
									self.assertEqual(logits.argmax(-1      ).item(),    A      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'      )
									lowerCamelCase					:			List[str]									= model.to(A      )

									lowerCamelCase					:			Dict									= BeitImageProcessor(do_resize=A,    size=640,    do_center_crop=A      )

									lowerCamelCase					:			Optional[Any]									= load_dataset('hf-internal-testing/fixtures_ade20k',    split='test'      )
									lowerCamelCase					:			Any									= Image.open(ds[0]['file']      )
									lowerCamelCase					:			Any									= image_processor(images=A,    return_tensors='pt'      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			Optional[int]									= model(**A      )
									lowerCamelCase					:			List[str]									= outputs.logits

									# verify the logits
									lowerCamelCase					:			int									= torch.Size((1, 150, 160, 160)      )
									self.assertEqual(logits.shape,    A      )

									lowerCamelCase					:			List[str]									= version.parse(PIL.__version__      ) < version.parse('9.0.0'      )

									if is_pillow_less_than_a:
													lowerCamelCase					:			Union[str, Any]									= torch.tensor(
													    [
													        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
													        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
													        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
													    ],    device=A,    )
									else:
													lowerCamelCase					:			Dict									= torch.tensor(
													    [
													        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
													        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
													        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
													    ],    device=A,    )

									self.assertTrue(torch.allclose(logits[0, :3, :3, :3],    A,    atol=1e-4      )      )




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'      )
									lowerCamelCase					:			Dict									= model.to(A      )

									lowerCamelCase					:			List[str]									= BeitImageProcessor(do_resize=A,    size=640,    do_center_crop=A      )

									lowerCamelCase					:			List[Any]									= load_dataset('hf-internal-testing/fixtures_ade20k',    split='test'      )
									lowerCamelCase					:			str									= Image.open(ds[0]['file']      )
									lowerCamelCase					:			List[str]									= image_processor(images=A,    return_tensors='pt'      ).to(A      )

									# forward pass
									with torch.no_grad():
													lowerCamelCase					:			str									= model(**A      )

									lowerCamelCase					:			List[Any]									= outputs.logits.detach().cpu()

									lowerCamelCase					:			str									= image_processor.post_process_semantic_segmentation(outputs=A,    target_sizes=[(500, 300)]      )
									lowerCamelCase					:			Optional[int]									= torch.Size((500, 300)      )
									self.assertEqual(segmentation[0].shape,    A      )

									lowerCamelCase					:			Any									= image_processor.post_process_semantic_segmentation(outputs=A      )
									lowerCamelCase					:			Optional[int]									= torch.Size((160, 160)      )
									self.assertEqual(segmentation[0].shape,    A      )


'''simple docstring'''






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	(DPMSolverSinglestepScheduler,)
					_lowerCAmelCase			     =	(('''num_inference_steps''', 25),)
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lambda_min_clipped': -float('inf'      ),
									    'variance_type': None,
									}

									config.update(**A      )
									return config
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			Optional[Any]									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			Dict									= 0.1 * sample
									lowerCamelCase					:			Dict									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Dict									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )
													# copy over dummy past residuals
													lowerCamelCase					:			str									= dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(A      )
																	lowerCamelCase					:			List[Any]									= scheduler_class.from_pretrained(A      )
																	new_scheduler.set_timesteps(A      )
																	# copy over dummy past residuals
																	lowerCamelCase					:			Optional[int]									= dummy_past_residuals[: new_scheduler.config.solver_order]

													lowerCamelCase			, lowerCamelCase					:			Optional[int]									= sample, sample
													for t in range(A,    time_step + scheduler.config.solver_order + 1      ):
																	lowerCamelCase					:			Dict									= scheduler.step(A,    A,    A,    **A      ).prev_sample
																	lowerCamelCase					:			Optional[int]									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

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

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self,    A=0,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= dict(self.forward_default_kwargs      )
									lowerCamelCase					:			str									= kwargs.pop('num_inference_steps',    A      )
									lowerCamelCase					:			Union[str, Any]									= self.dummy_sample
									lowerCamelCase					:			List[str]									= 0.1 * sample
									lowerCamelCase					:			List[str]									= [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
													lowerCamelCase					:			Tuple									= self.get_scheduler_config()
													lowerCamelCase					:			Optional[Any]									= scheduler_class(**A      )
													scheduler.set_timesteps(A      )

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

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

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

													lowerCamelCase					:			int									= scheduler.step(A,    A,    A,    **A      ).prev_sample
													lowerCamelCase					:			Dict									= new_scheduler.step(A,    A,    A,    **A      ).prev_sample

													assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
					def     UpperCAmelCase_							(     self,    A=None,    **A      ):

									"""simple docstring"""


									if scheduler is None:
													lowerCamelCase					:			Any									= self.scheduler_classes[0]
													lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
													lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Optional[Any]									= self.get_scheduler_config(**A      )
									lowerCamelCase					:			Optional[int]									= scheduler_class(**A      )

									lowerCamelCase					:			Any									= 10
									lowerCamelCase					:			Dict									= self.dummy_model()
									lowerCamelCase					:			Any									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			Dict									= model(A,    A      )
													lowerCamelCase					:			List[str]									= scheduler.step(A,    A,    A      ).prev_sample

									return sample
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			Dict									= 50
									lowerCamelCase					:			Tuple									= self.dummy_model()
									lowerCamelCase					:			Optional[int]									= self.dummy_sample_deter
									scheduler.set_timesteps(A      )

									# make sure that the first t is uneven
									for i, t in enumerate(scheduler.timesteps[3:]      ):
													lowerCamelCase					:			Any									= model(A,    A      )
													lowerCamelCase					:			Optional[int]									= scheduler.step(A,    A,    A      ).prev_sample

									lowerCamelCase					:			Any									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2574      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Dict									= DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3

									lowerCamelCase					:			Dict									= DEISMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Any									= UniPCMultistepScheduler.from_config(scheduler.config      )
									lowerCamelCase					:			Optional[Any]									= DPMSolverSinglestepScheduler.from_config(scheduler.config      )

									lowerCamelCase					:			str									= self.full_loop(scheduler=A      )
									lowerCamelCase					:			Optional[int]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(thresholding=A      )
									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=A,    prediction_type=A,    sample_max_value=A,    algorithm_type='dpmsolver++',    solver_order=A,    solver_type=A,    )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																	for order in [1, 2, 3]:
																					for prediction_type in ["epsilon", "sample"]:
																									self.check_over_configs(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									lowerCamelCase					:			Optional[Any]									= self.full_loop(
																									    solver_order=A,    solver_type=A,    prediction_type=A,    algorithm_type=A,    )
																									assert not torch.isnan(A      ).any(), "Samples have nan numbers"
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lower_order_final=A      )
									self.check_over_configs(lower_order_final=A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
									self.check_over_configs(lambda_min_clipped=-5.1      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.check_over_configs(variance_type=A      )
									self.check_over_configs(variance_type='learned_range'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop()
									lowerCamelCase					:			str									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2791      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.full_loop(use_karras_sigmas=A      )
									lowerCamelCase					:			Tuple									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.2248      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction'      )
									lowerCamelCase					:			Dict									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.1453      ) < 1e-3
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.full_loop(prediction_type='v_prediction',    use_karras_sigmas=A      )
									lowerCamelCase					:			Optional[Any]									= torch.mean(torch.abs(A      )      )

									assert abs(result_mean.item() - 0.0649      ) < 1e-3




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.scheduler_classes[0]
									lowerCamelCase					:			Dict									= self.get_scheduler_config(thresholding=A,    dynamic_thresholding_ratio=0      )
									lowerCamelCase					:			str									= scheduler_class(**A      )

									lowerCamelCase					:			List[Any]									= 10
									lowerCamelCase					:			List[str]									= self.dummy_model()
									lowerCamelCase					:			int									= self.dummy_sample_deter.half()
									scheduler.set_timesteps(A      )

									for i, t in enumerate(scheduler.timesteps      ):
													lowerCamelCase					:			str									= model(A,    A      )
													lowerCamelCase					:			Tuple									= scheduler.step(A,    A,    A      ).prev_sample

									assert sample.dtype == torch.floataa
