
"""simple docstring"""




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    LevitForImageClassification,
						    LevitForImageClassificationWithTeacher,
						    LevitModel,
						)
						from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import LevitImageProcessor





class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.config_class(**self.inputs_dict    )
									self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''hidden_sizes'''    )    )
									self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,       '''num_attention_heads'''    )    )







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=[1_28, 2_56, 3_84]       ,       SCREAMING_SNAKE_CASE_=[4, 6, 8]       ,       SCREAMING_SNAKE_CASE_=[2, 3, 4]       ,       SCREAMING_SNAKE_CASE_=[16, 16, 16]       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=2       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = parent
									SCREAMING_SNAKE_CASE_						 = batch_size
									SCREAMING_SNAKE_CASE_						 = image_size
									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = kernel_size
									SCREAMING_SNAKE_CASE_						 = stride
									SCREAMING_SNAKE_CASE_						 = padding
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = key_dim
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = patch_size
									SCREAMING_SNAKE_CASE_						 = attention_ratio
									SCREAMING_SNAKE_CASE_						 = mlp_ratio
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = [
									    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
									    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
									]
									SCREAMING_SNAKE_CASE_						 = is_training
									SCREAMING_SNAKE_CASE_						 = use_labels
									SCREAMING_SNAKE_CASE_						 = num_labels
									SCREAMING_SNAKE_CASE_						 = initializer_range

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

									SCREAMING_SNAKE_CASE_						 = None
									if self.use_labels:
											SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size]       ,       self.num_labels    )

									SCREAMING_SNAKE_CASE_						 = self.get_config()
									return config, pixel_values, labels

							def _lowercase					(self    ):

									"""simple docstring"""







									return LevitConfig(
									    image_size=self.image_size       ,       num_channels=self.num_channels       ,       kernel_size=self.kernel_size       ,       stride=self.stride       ,       padding=self.padding       ,       patch_size=self.patch_size       ,       hidden_sizes=self.hidden_sizes       ,       num_attention_heads=self.num_attention_heads       ,       depths=self.depths       ,       key_dim=self.key_dim       ,       drop_path_rate=self.drop_path_rate       ,       mlp_ratio=self.mlp_ratio       ,       attention_ratio=self.attention_ratio       ,       initializer_range=self.initializer_range       ,       down_ops=self.down_ops       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = LevitModel(config=SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = (self.image_size, self.image_size)
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = image_size[0], image_size[1]
									for _ in range(4    ):
											SCREAMING_SNAKE_CASE_						 = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1    )
											SCREAMING_SNAKE_CASE_						 = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1    )
									self.parent.assertEqual(
									    result.last_hidden_state.shape       ,       (self.batch_size, ceil(height / 4    ) * ceil(width / 4    ), self.hidden_sizes[-1])       ,       )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.num_labels
									SCREAMING_SNAKE_CASE_						 = LevitForImageClassification(SCREAMING_SNAKE_CASE_    )
									model.to(SCREAMING_SNAKE_CASE_    )
									model.eval()
									SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       labels=SCREAMING_SNAKE_CASE_    )
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels)    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = config_and_inputs
									SCREAMING_SNAKE_CASE_						 = {'''pixel_values''': pixel_values}
									return config, inputs_dict







@require_torch
class  snake_case     (							__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__						     =      (
							    {
							        '''feature-extraction''': LevitModel,
							        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = LevitModelTester(self    )
									SCREAMING_SNAKE_CASE_						 = ConfigTester(self       ,       config_class=SCREAMING_SNAKE_CASE_       ,       has_text_modality=SCREAMING_SNAKE_CASE_       ,       hidden_size=37    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()

							def _lowercase					(self    ):

									"""simple docstring"""







									return

							@unittest.skip(reason='''Levit does not use inputs_embeds'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							@unittest.skip(reason='''Levit does not support input and output embeddings'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							@unittest.skip(reason='''Levit does not output attentions'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE_						 = ['''pixel_values''']
											self.assertListEqual(arg_names[:1]       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									def check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )
											model.to(SCREAMING_SNAKE_CASE_    )
											model.eval()

											with torch.no_grad():
													SCREAMING_SNAKE_CASE_						 = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )

											SCREAMING_SNAKE_CASE_						 = outputs.hidden_states

											SCREAMING_SNAKE_CASE_						 = len(self.model_tester.depths    ) + 1
											self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = (self.model_tester.image_size, self.model_tester.image_size)
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = image_size[0], image_size[1]
											for _ in range(4    ):
													SCREAMING_SNAKE_CASE_						 = floor(
													    (
													        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1    )
													SCREAMING_SNAKE_CASE_						 = floor(
													    (
													        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1    )
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]    )       ,       [
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]       ,       )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE_						 = True
											check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											SCREAMING_SNAKE_CASE_						 = True

											check_hidden_states_output(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									pass

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = super()._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )

									if return_labels:
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													del inputs_dict["labels"]

									return inputs_dict

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									if not self.model_tester.is_training:
											return

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_						 = True

									for model_class in self.all_model_classes:
											# LevitForImageClassificationWithTeacher supports inference-only
											if (
											    model_class in get_values(SCREAMING_SNAKE_CASE_    )
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
													continue
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )
											model.to(SCREAMING_SNAKE_CASE_    )
											model.train()
											SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = model(**SCREAMING_SNAKE_CASE_    ).loss
											loss.backward()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
											return

									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = True

									for model_class in self.all_model_classes:
											if model_class in get_values(SCREAMING_SNAKE_CASE_    ) or not model_class.supports_gradient_checkpointing:
													continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													continue
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )
											model.gradient_checkpointing_enable()
											model.to(SCREAMING_SNAKE_CASE_    )
											model.train()
											SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = model(**SCREAMING_SNAKE_CASE_    ).loss
											loss.backward()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_						 = [
									    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
									    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
									    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
									]

									for model_class in self.all_model_classes:
											if (
											    model_class
											    not in [
											        *get_values(SCREAMING_SNAKE_CASE_    ),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
													continue

											for problem_type in problem_types:
													with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'    ):
															SCREAMING_SNAKE_CASE_						 = problem_type['''title''']
															SCREAMING_SNAKE_CASE_						 = problem_type['''num_labels''']

															SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )
															model.to(SCREAMING_SNAKE_CASE_    )
															model.train()

															SCREAMING_SNAKE_CASE_						 = self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       return_labels=SCREAMING_SNAKE_CASE_    )

															if problem_type["num_labels"] > 1:
																	SCREAMING_SNAKE_CASE_						 = inputs['''labels'''].unsqueeze(1    ).repeat(1       ,       problem_type['''num_labels''']    )

															SCREAMING_SNAKE_CASE_						 = inputs['''labels'''].to(problem_type['''dtype''']    )

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE_    ) as warning_list:
																	SCREAMING_SNAKE_CASE_						 = model(**SCREAMING_SNAKE_CASE_    ).loss
															for w in warning_list:
																	if "Using a target size that is different to the input size" in str(w.message    ):
																			raise ValueError(
																			    f'Something is going wrong in the regression problem: intercepted {w.message}'    )

															loss.backward()







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE_						 = LevitModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
		return image





@require_torch
@require_vision
class  snake_case     (							unittest.TestCase   ):

							@cached_property
							def _lowercase					(self    ):

									"""simple docstring"""







									return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )







							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
									    SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.default_image_processor
									SCREAMING_SNAKE_CASE_						 = prepare_img()
									SCREAMING_SNAKE_CASE_						 = image_processor(images=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''    ).to(SCREAMING_SNAKE_CASE_    )

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE_						 = model(**SCREAMING_SNAKE_CASE_    )

									# verify the logits
									SCREAMING_SNAKE_CASE_						 = torch.Size((1, 10_00)    )
									self.assertEqual(outputs.logits.shape       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.tensor([1.04_48, -0.37_45, -1.83_17]    ).to(SCREAMING_SNAKE_CASE_    )

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

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3