






"""simple docstring"""


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor




class 						__UpperCamelCase     (     __snake_case						):
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[int]:
											lowerCAmelCase   :Tuple						=			self.config_class(**self.inputs_dict   )
											self.parent.assertTrue(hasattr(UpperCAmelCase     , 'tf_padding'   )   )
											self.parent.assertTrue(hasattr(UpperCAmelCase     , 'depth_multiplier'   )   )




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Optional[int]=13     , UpperCAmelCase							:	Optional[int]=3     , UpperCAmelCase							:	Optional[int]=32     , UpperCAmelCase							:	str=0.2_5     , UpperCAmelCase							:	Optional[Any]=8     , UpperCAmelCase							:	List[str]=8     , UpperCAmelCase							:	Union[str, Any]=6     , UpperCAmelCase							:	Dict=32     , UpperCAmelCase							:	Tuple=True     , UpperCAmelCase							:	str=True     , UpperCAmelCase							:	Optional[Any]=True     , UpperCAmelCase							:	str="relu6"     , UpperCAmelCase							:	Optional[int]=1280     , UpperCAmelCase							:	List[Any]=0.1     , UpperCAmelCase							:	List[Any]=0.0_2     , UpperCAmelCase							:	Optional[Any]=True     , UpperCAmelCase							:	Union[str, Any]=True     , UpperCAmelCase							:	List[Any]=10     , UpperCAmelCase							:	Tuple=None     , )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			parent
											lowerCAmelCase   :Optional[int]						=			batch_size
											lowerCAmelCase   :List[Any]						=			num_channels
											lowerCAmelCase   :List[Any]						=			image_size
											lowerCAmelCase   :List[Any]						=			depth_multiplier
											lowerCAmelCase   :Optional[Any]						=			depth_divisible_by
											lowerCAmelCase   :List[Any]						=			min_depth
											lowerCAmelCase   :Dict						=			expand_ratio
											lowerCAmelCase   :Optional[int]						=			tf_padding
											lowerCAmelCase   :List[str]						=			output_stride
											lowerCAmelCase   :List[str]						=			first_layer_is_expansion
											lowerCAmelCase   :Tuple						=			finegrained_output
											lowerCAmelCase   :List[Any]						=			hidden_act
											lowerCAmelCase   :Tuple						=			last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier   )
											lowerCAmelCase   :Any						=			classifier_dropout_prob
											lowerCAmelCase   :List[Any]						=			use_labels
											lowerCAmelCase   :Tuple						=			is_training
											lowerCAmelCase   :int						=			num_labels
											lowerCAmelCase   :Tuple						=			initializer_range
											lowerCAmelCase   :Optional[Any]						=			scope
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :int						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

											lowerCAmelCase   :Optional[int]						=			None
											lowerCAmelCase   :List[Any]						=			None
											if self.use_labels:
																	lowerCAmelCase   :Any						=			ids_tensor([self.batch_size]     , self.num_labels   )
																	lowerCAmelCase   :Optional[int]						=			ids_tensor([self.batch_size, self.image_size, self.image_size]     , self.num_labels   )

											lowerCAmelCase   :Union[str, Any]						=			self.get_config()

											return config, pixel_values, labels, pixel_labels
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[int]:
											return MobileNetVaConfig(
											    num_channels=self.num_channels     , image_size=self.image_size     , depth_multiplier=self.depth_multiplier     , depth_divisible_by=self.depth_divisible_by     , min_depth=self.min_depth     , expand_ratio=self.expand_ratio     , output_stride=self.output_stride     , first_layer_is_expansion=self.first_layer_is_expansion     , finegrained_output=self.finegrained_output     , hidden_act=self.hidden_act     , tf_padding=self.tf_padding     , classifier_dropout_prob=self.classifier_dropout_prob     , initializer_range=self.initializer_range     , )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	List[Any]   )	->       str:
											lowerCAmelCase   :List[Any]						=			MobileNetVaModel(config=UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Tuple						=			model(UpperCAmelCase   )
											self.parent.assertEqual(
											    result.last_hidden_state.shape     , (
											        self.batch_size,
											        self.last_hidden_size,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     , )
											self.parent.assertEqual(
											    result.pooler_output.shape     , (self.batch_size, self.last_hidden_size)     , )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Tuple   )	->       int:
											lowerCAmelCase   :Dict						=			self.num_labels
											lowerCAmelCase   :Tuple						=			MobileNetVaForImageClassification(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Dict						=			model(UpperCAmelCase     , labels=UpperCAmelCase   )
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Union[str, Any]   )	->       Optional[Any]:
											lowerCAmelCase   :int						=			self.num_labels
											lowerCAmelCase   :Dict						=			MobileNetVaForSemanticSegmentation(UpperCAmelCase   )
											model.to(UpperCAmelCase   )
											model.eval()
											lowerCAmelCase   :Dict						=			model(UpperCAmelCase   )
											self.parent.assertEqual(
											    result.logits.shape     , (
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     , )
											lowerCAmelCase   :int						=			model(UpperCAmelCase     , labels=UpperCAmelCase   )
											self.parent.assertEqual(
											    result.logits.shape     , (
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     , )
					def        UpperCAmelCase__ (							self							:	Any   )	->       int:
											lowerCAmelCase   :Union[str, Any]						=			self.prepare_config_and_inputs()
											lowerCAmelCase   :Union[str, Any]						=			config_and_inputs
											lowerCAmelCase   :Any						=			{"pixel_values": pixel_values}
											return config, inputs_dict




@require_torch
class 						__UpperCamelCase     (     __snake_case   ,     __snake_case   ,     unittest.TestCase						):
					lowercase_    :				Tuple      =  (
					    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					lowercase_    :				List[Any]      =  (
					    {
					        """feature-extraction""": MobileNetVaModel,
					        """image-classification""": MobileNetVaForImageClassification,
					        """image-segmentation""": MobileNetVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					lowercase_    :				Optional[Any]      =  False
					lowercase_    :				Union[str, Any]      =  False
					lowercase_    :				Any      =  False
					lowercase_    :				int      =  False
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Any:
											lowerCAmelCase   :Any						=			MobileNetVaModelTester(self   )
											lowerCAmelCase   :Any						=			MobileNetVaConfigTester(self     , config_class=UpperCAmelCase     , has_text_modality=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[Any]:
											self.config_tester.run_common_tests()
					@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Optional[int]:
											pass
					@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Tuple:
											pass
					@unittest.skip(reason='MobileNetV2 does not output attentions'   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Dict:
											pass
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											lowerCAmelCase   :Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowerCAmelCase   :Any						=			["pixel_values"]
																	self.assertListEqual(arg_names[:1]     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Optional[Any]:
											lowerCAmelCase   :List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Tuple:
											def check_hidden_states_output(UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Optional[int]   ):
																	lowerCAmelCase   :Optional[Any]						=			model_class(UpperCAmelCase   )
																	model.to(UpperCAmelCase   )
																	model.eval()

																	with torch.no_grad():
																							lowerCAmelCase   :Dict						=			model(**self._prepare_for_class(UpperCAmelCase     , UpperCAmelCase   )   )

																	lowerCAmelCase   :Optional[Any]						=			outputs.hidden_states

																	lowerCAmelCase   :Optional[Any]						=			16
																	self.assertEqual(len(UpperCAmelCase   )     , UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowerCAmelCase   :Optional[Any]						=			True
																	check_hidden_states_output(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowerCAmelCase   :Optional[Any]						=			True

																	check_hidden_states_output(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str   )	->       List[Any]:
											lowerCAmelCase   :Optional[int]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict:
											lowerCAmelCase   :Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[int]:
											for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase   :Optional[Any]						=			MobileNetVaModel.from_pretrained(UpperCAmelCase   )
																	self.assertIsNotNone(UpperCAmelCase   )


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
						return image







@require_torch
@require_vision
class 						__UpperCamelCase     (     unittest.TestCase						):
					@cached_property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       int:
											return (
											    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'   ) if is_vision_available() else None
											)
					@slow
					def        UpperCAmelCase__ (							self							:	int   )	->       str:
											lowerCAmelCase   :List[Any]						=			MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'   ).to(UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			self.default_image_processor
											lowerCAmelCase   :Tuple						=			prepare_img()
											lowerCAmelCase   :Optional[Any]						=			image_processor(images=UpperCAmelCase     , return_tensors='pt'   ).to(UpperCAmelCase   )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase   :Union[str, Any]						=			model(**UpperCAmelCase   )

											# verify the logits
											lowerCAmelCase   :str						=			torch.Size((1, 1001)   )
											self.assertEqual(outputs.logits.shape     , UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]   ).to(UpperCAmelCase   )

											self.assertTrue(torch.allclose(outputs.logits[0, :3]     , UpperCAmelCase     , atol=1e-4   )   )
					@slow
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict:
											lowerCAmelCase   :int						=			MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'   )
											lowerCAmelCase   :Dict						=			model.to(UpperCAmelCase   )

											lowerCAmelCase   :int						=			MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'   )

											lowerCAmelCase   :str						=			prepare_img()
											lowerCAmelCase   :Dict						=			image_processor(images=UpperCAmelCase     , return_tensors='pt'   ).to(UpperCAmelCase   )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase   :Any						=			model(**UpperCAmelCase   )
											lowerCAmelCase   :int						=			outputs.logits

											# verify the logits
											lowerCAmelCase   :List[str]						=			torch.Size((1, 21, 65, 65)   )
											self.assertEqual(logits.shape     , UpperCAmelCase   )

											lowerCAmelCase   :str						=			torch.tensor(
											    [
											        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
											        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
											        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
											    ]     , device=UpperCAmelCase     , )

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

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

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

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

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

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

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

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

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






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							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=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							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=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




