




import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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 (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    EfficientFormerForImageClassification,
		    EfficientFormerForImageClassificationWithTeacher,
		    EfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_efficientformer import (
		    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor
class 		UpperCAmelCase       :







						'''simple docstring'''





						def __init__(						self							:					Optional[int]	,     __lowercase							:					List[Any]	,     __lowercase							:					int = 13	,     __lowercase							:					int = 64	,     __lowercase							:					int = 2	,     __lowercase							:					int = 3	,     __lowercase							:					int = 3	,     __lowercase							:					bool = True	,     __lowercase							:					bool = True	,     __lowercase							:					int = 1_28	,     __lowercase							:					Union[str, Any]=[16, 32, 64, 1_28]	,     __lowercase							:					int = 7	,     __lowercase							:					int = 4	,     __lowercase							:					int = 37	,     __lowercase							:					str = "gelu"	,     __lowercase							:					float = 0.1	,     __lowercase							:					float = 0.1	,     __lowercase							:					int = 10	,     __lowercase							:					float = 0.02	,     __lowercase							:					int = 2	,     __lowercase							:					int = 1	,     __lowercase							:					int = 1_28	,     __lowercase							:					List[int] = [2, 2, 2, 2]	,     __lowercase							:					int = 2	,     __lowercase							:					int = 2	,     ):






							"""simple docstring"""






							snake_case_		   =					parent
							snake_case_		   =					batch_size
							snake_case_		   =					image_size
							snake_case_		   =					patch_size
							snake_case_		   =					num_channels
							snake_case_		   =					is_training
							snake_case_		   =					use_labels
							snake_case_		   =					hidden_size
							snake_case_		   =					num_hidden_layers
							snake_case_		   =					num_attention_heads
							snake_case_		   =					intermediate_size
							snake_case_		   =					hidden_act
							snake_case_		   =					hidden_dropout_prob
							snake_case_		   =					attention_probs_dropout_prob
							snake_case_		   =					type_sequence_label_size
							snake_case_		   =					initializer_range
							snake_case_		   =					encoder_stride
							snake_case_		   =					num_attention_outputs
							snake_case_		   =					embed_dim
							snake_case_		   =					embed_dim + 1
							snake_case_		   =					resolution
							snake_case_		   =					depths
							snake_case_		   =					hidden_sizes
							snake_case_		   =					dim
							snake_case_		   =					mlp_expansion_ratio






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






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

							snake_case_		   =					None
							if self.use_labels:
								snake_case_		   =					ids_tensor([self.batch_size]	,     self.type_sequence_label_size						)

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






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






							return EfficientFormerConfig(
							    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=__lowercase	,     initializer_range=self.initializer_range	,     encoder_stride=self.encoder_stride	,     resolution=self.resolution	,     depths=self.depths	,     hidden_sizes=self.hidden_sizes	,     dim=self.dim	,     mlp_expansion_ratio=self.mlp_expansion_ratio	,     )






						def 	snake_case__						(						self							:					Dict	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					List[str]	,     __lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					EfficientFormerModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		   =					model(__lowercase						)
							self.parent.assertEqual(result.last_hidden_state.shape	,     (self.batch_size, self.seq_length, self.hidden_size)						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					int	,     __lowercase							:					int	,     __lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					self.type_sequence_label_size
							snake_case_		   =					EfficientFormerForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		   =					model(__lowercase	,     labels=__lowercase						)
							self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.type_sequence_label_size)						)

							# test greyscale images
							snake_case_		   =					1
							snake_case_		   =					EfficientFormerForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()

							snake_case_		   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							snake_case_		   =					model(__lowercase						)
							self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.type_sequence_label_size)						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				(
							    snake_case_
							)   ,				
							)		   =					config_and_inputs
							snake_case_		   =					{"pixel_values": pixel_values}
							return config, inputs_dict







@require_torch
class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (
						    (
						        EfficientFormerModel,
						        EfficientFormerForImageClassificationWithTeacher,
						        EfficientFormerForImageClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=       (
						    {
						        '''feature-extraction''': EfficientFormerModel,
						        '''image-classification''': (
						            EfficientFormerForImageClassification,
						            EfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase_							=       False

						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False
						lowerCAmelCase_							=       False






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					EfficientFormerModelTester(self						)
							snake_case_		   =					ConfigTester(
							    self	,     config_class=__lowercase	,     has_text_modality=__lowercase	,     hidden_size=37						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						@unittest.skip(reason="EfficientFormer does not use inputs_embeds"						)
						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							pass






						@unittest.skip(reason="EfficientFormer does not support input and output embeddings"						)
						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							pass






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_   ,				snake_case_		   =					self.model_tester.prepare_config_and_inputs_for_common()

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

								snake_case_		   =					["pixel_values"]
								self.assertListEqual(arg_names[:1]	,     __lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							def check_hidden_states_output(__lowercase							:					Optional[Any]	,     __lowercase							:					str	,     __lowercase							:					Optional[int]						):
								snake_case_		   =					model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()

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

								snake_case_		   =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								snake_case_		   =					getattr(
								    self.model_tester	,     "expected_num_hidden_layers"	,     self.model_tester.num_hidden_layers + 1						)
								self.assertEqual(len(__lowercase						)	,     __lowercase						)

								if hasattr(self.model_tester	,     "encoder_seq_length"						):
									snake_case_		   =					self.model_tester.encoder_seq_length
									if hasattr(self.model_tester	,     "chunk_length"						) and self.model_tester.chunk_length > 1:
										snake_case_		   =					seq_length * self.model_tester.chunk_length
								else:
									snake_case_		   =					self.model_tester.seq_length

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:]						)	,     [seq_length, self.model_tester.hidden_size]	,     )

								if config.is_encoder_decoder:
									snake_case_		   =					outputs.decoder_hidden_states

									self.assertIsInstance(__lowercase	,     (list, tuple)						)
									self.assertEqual(len(__lowercase						)	,     __lowercase						)
									snake_case_		   =					getattr(self.model_tester	,     "seq_length"	,     __lowercase						)
									snake_case_		   =					getattr(self.model_tester	,     "decoder_seq_length"	,     __lowercase						)

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]						)	,     [decoder_seq_length, self.model_tester.hidden_size]	,     )

							snake_case_   ,				snake_case_		   =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

								check_hidden_states_output(__lowercase	,     __lowercase	,     __lowercase						)






						def 	snake_case__						(						self							:					List[str]	,     __lowercase							:					List[str]	,     __lowercase							:					Dict	,     __lowercase							:					List[Any]=False						):






							"""simple docstring"""






							snake_case_		   =					super()._prepare_for_class(__lowercase	,     __lowercase	,     return_labels=__lowercase						)

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

							return inputs_dict






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)






						@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"						)
						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase						)






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							if not self.model_tester.is_training:
								return

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

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






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_   ,				snake_case_		   =					self.model_tester.prepare_config_and_inputs_for_common()

							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(__lowercase						),
								    ]
								    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
								):
									continue

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

										snake_case_		   =					model_class(__lowercase						)
										model.to(__lowercase						)
										model.train()

										snake_case_		   =					self._prepare_for_class(__lowercase	,     __lowercase	,     return_labels=__lowercase						)

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

										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=__lowercase						) as warning_list:
											snake_case_		   =					model(**__lowercase						).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 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		   =					EfficientFormerModel.from_pretrained(__lowercase						)
								self.assertIsNotNone(__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






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

							snake_case_		   =					getattr(self.model_tester	,     "seq_length"	,     __lowercase						)
							snake_case_		   =					getattr(self.model_tester	,     "encoder_seq_length"	,     __lowercase						)
							snake_case_		   =					getattr(self.model_tester	,     "key_length"	,     __lowercase						)
							snake_case_		   =					getattr(self.model_tester	,     "chunk_length"	,     __lowercase						)

							if chunk_length is not None and hasattr(self.model_tester	,     "num_hashes"						):
								snake_case_		   =					encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
								snake_case_		   =					True
								snake_case_		   =					False
								snake_case_		   =					True
								snake_case_		   =					model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()
								with torch.no_grad():
									snake_case_		   =					model(**self._prepare_for_class(__lowercase	,     __lowercase						)						)
								snake_case_		   =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(__lowercase						)	,     self.model_tester.num_attention_outputs						)

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								snake_case_		   =					True
								snake_case_		   =					model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()
								with torch.no_grad():
									snake_case_		   =					model(**self._prepare_for_class(__lowercase	,     __lowercase						)						)
								snake_case_		   =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(__lowercase						)	,     self.model_tester.num_attention_outputs						)

								if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:]						)	,     [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]	,     )
								else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:]						)	,     [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]	,     )







def 						lowerCamelCase__   (		):







	'''simple docstring'''





	snake_case_		   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






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







						'''simple docstring'''





						@cached_property
						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							return (
							    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"						)
							    if is_vision_available()
							    else None
							)






						@slow
						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"						).to(
							    __lowercase						)

							snake_case_		   =					self.default_image_processor
							snake_case_		   =					prepare_img()
							snake_case_		   =					image_processor(images=__lowercase	,     return_tensors="pt"						).to(__lowercase						)

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

							# verify the logits
							snake_case_		   =					(1, 10_00)
							self.assertEqual(outputs.logits.shape	,     __lowercase						)

							snake_case_		   =					torch.tensor([-0.0555, 0.4825, -0.0852]						).to(__lowercase						)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]	,     __lowercase	,     atol=1E-4						)						)






						@slow
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					EfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    "snap-research/efficientformer-l1-300"						).to(__lowercase						)

							snake_case_		   =					self.default_image_processor
							snake_case_		   =					prepare_img()
							snake_case_		   =					image_processor(images=__lowercase	,     return_tensors="pt"						).to(__lowercase						)

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

							# verify the logits
							snake_case_		   =					(1, 10_00)
							self.assertEqual(outputs.logits.shape	,     __lowercase						)

							snake_case_		   =					torch.tensor([-0.1312, 0.4353, -1.0499]						).to(__lowercase						)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]	,     __lowercase	,     atol=1E-4						)						)






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






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

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
