





'''simple docstring'''




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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	parent
							SCREAMING_SNAKE_CASE				:							int						=	batch_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	image_size
							SCREAMING_SNAKE_CASE				:							str						=	patch_size
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	num_channels
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	is_training
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	use_labels
							SCREAMING_SNAKE_CASE				:							str						=	hidden_size
							SCREAMING_SNAKE_CASE				:							List[str]						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							List[str]						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							List[str]						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_act
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							Dict						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							int						=	type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	encoder_stride
							SCREAMING_SNAKE_CASE				:							Dict						=	num_attention_outputs
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	embed_dim
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	embed_dim + 1
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	resolution
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	depths
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	hidden_sizes
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	dim
							SCREAMING_SNAKE_CASE				:							Tuple						=	mlp_expansion_ratio


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	None
							if self.use_labels:
									SCREAMING_SNAKE_CASE				:							Tuple						=	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size		)

							SCREAMING_SNAKE_CASE				:							int						=	self.get_config()
							return config, pixel_values, labels


					def lowerCamelCase_	(		self							:	Optional[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=lowerCamelCase_     ,		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 lowerCamelCase_	(		self							:	int     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	EfficientFormerModel(config=lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)		)


					def lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	EfficientFormerForImageClassification(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		labels=lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							SCREAMING_SNAKE_CASE				:							str						=	1
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	EfficientFormerForImageClassification(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()

							SCREAMING_SNAKE_CASE				:							List[Any]						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							SCREAMING_SNAKE_CASE				:							str						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)



					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE
							),    (
							    SCREAMING_SNAKE_CASE
							),    (
							    SCREAMING_SNAKE_CASE
							),    
							)				:							Any						=	config_and_inputs
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class       UpperCamelCase__			(			lowercase_		,			lowercase_		,			unittest.TestCase	):




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

					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	EfficientFormerModelTester(self		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	ConfigTester(
							    self     ,		config_class=lowerCamelCase_     ,		has_text_modality=lowerCamelCase_     ,		hidden_size=37		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							self.config_tester.run_common_tests()


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

							'''simple docstring'''



							pass


					@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""		)
					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							pass


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							def check_hidden_states_output(lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	List[Any]		):
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Tuple						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

									SCREAMING_SNAKE_CASE				:							Any						=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									SCREAMING_SNAKE_CASE				:							Any						=	getattr(
									    self.model_tester     ,		"""expected_num_hidden_layers"""     ,		self.model_tester.num_hidden_layers + 1		)
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									if hasattr(self.model_tester     ,		"""encoder_seq_length"""		):
											SCREAMING_SNAKE_CASE				:							str						=	self.model_tester.encoder_seq_length
											if hasattr(self.model_tester     ,		"""chunk_length"""		) and self.model_tester.chunk_length > 1:
													SCREAMING_SNAKE_CASE				:							str						=	seq_length * self.model_tester.chunk_length
									else:
											SCREAMING_SNAKE_CASE				:							List[str]						=	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:
											SCREAMING_SNAKE_CASE				:							List[Any]						=	outputs.decoder_hidden_states

											self.assertIsInstance(lowerCamelCase_     ,		(list, tuple)		)
											self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Dict						=	getattr(self.model_tester     ,		"""seq_length"""     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							str						=	getattr(self.model_tester     ,		"""decoder_seq_length"""     ,		lowerCamelCase_		)

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

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

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
									check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE				:							List[str]						=	True

									check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Dict=False		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	super()._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_     ,		return_labels=lowerCamelCase_		)

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

							return inputs_dict


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_		)


					@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""		)
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_		)


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							if not self.model_tester.is_training:
									return

							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True

							for model_class in self.all_model_classes:
									# EfficientFormerForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(lowerCamelCase_		)
									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
									):
											continue
									SCREAMING_SNAKE_CASE				:							int						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.train()
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_     ,		return_labels=lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							str						=	model(**lowerCamelCase_		).loss
									loss.backward()


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_SNAKE_CASE				:							Dict						=	[
							    {"""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(lowerCamelCase_		),
									    ]
									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
									):
											continue

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

													SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(lowerCamelCase_		)
													model.to(lowerCamelCase_		)
													model.train()

													SCREAMING_SNAKE_CASE				:							str						=	self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_     ,		return_labels=lowerCamelCase_		)

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

													SCREAMING_SNAKE_CASE				:							Optional[Any]						=	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=lowerCamelCase_		) as warning_list:
															SCREAMING_SNAKE_CASE				:							str						=	model(**lowerCamelCase_		).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 lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	EfficientFormerModel.from_pretrained(lowerCamelCase_		)
									self.assertIsNotNone(lowerCamelCase_		)



					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	True

							SCREAMING_SNAKE_CASE				:							Any						=	getattr(self.model_tester     ,		"""seq_length"""     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	getattr(self.model_tester     ,		"""encoder_seq_length"""     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	getattr(self.model_tester     ,		"""key_length"""     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	getattr(self.model_tester     ,		"""chunk_length"""     ,		lowerCamelCase_		)

							if chunk_length is not None and hasattr(self.model_tester     ,		"""num_hashes"""		):
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	True
									SCREAMING_SNAKE_CASE				:							str						=	False
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	True
									SCREAMING_SNAKE_CASE				:							str						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()
									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							List[Any]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							List[str]						=	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(lowerCamelCase_		)     ,		self.model_tester.num_attention_outputs		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE				:							List[Any]						=	True
									SCREAMING_SNAKE_CASE				:							Tuple						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()
									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Any						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(lowerCamelCase_		)     ,		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 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image




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




					"""simple docstring"""


					@cached_property
					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""		).to(
							    lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Any						=	self.default_image_processor
							SCREAMING_SNAKE_CASE				:							str						=	prepare_img()
							SCREAMING_SNAKE_CASE				:							Any						=	image_processor(images=lowerCamelCase_     ,		return_tensors="""pt"""		).to(lowerCamelCase_		)

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE				:							str						=	model(**lowerCamelCase_		)

							# verify the logits
							SCREAMING_SNAKE_CASE				:							Tuple						=	(1, 10_00)
							self.assertEqual(outputs.logits.shape     ,		lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.tensor([-0.0_555, 0.4_825, -0.0_852]		).to(lowerCamelCase_		)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,		lowerCamelCase_     ,		atol=1e-4		)		)



					@slow
					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	EfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    """snap-research/efficientformer-l1-300"""		).to(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.default_image_processor
							SCREAMING_SNAKE_CASE				:							str						=	prepare_img()
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	image_processor(images=lowerCamelCase_     ,		return_tensors="""pt"""		).to(lowerCamelCase_		)

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE				:							int						=	model(**lowerCamelCase_		)

							# verify the logits
							SCREAMING_SNAKE_CASE				:							List[Any]						=	(1, 10_00)
							self.assertEqual(outputs.logits.shape     ,		lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Any						=	torch.tensor([-0.1_312, 0.4_353, -1.0_499]		).to(lowerCamelCase_		)
							self.assertTrue(torch.allclose(outputs.logits[0][:3]     ,		lowerCamelCase_     ,		atol=1e-4		)		)










'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE__						=					(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE__						=					()


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	4
							SCREAMING_SNAKE_CASE				:							int						=	3
							SCREAMING_SNAKE_CASE				:							Any						=	8
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	8

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.rand((batch_size, num_channels, height, width)		)

							return sample


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	4
							SCREAMING_SNAKE_CASE				:							Tuple						=	3
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	8
							SCREAMING_SNAKE_CASE				:							List[str]						=	8

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	batch_size * num_channels * height * width
							SCREAMING_SNAKE_CASE				:							Dict						=	torch.arange(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	sample.reshape(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	sample / num_elems
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	sample.permute(3     ,		0     ,		1     ,		2		)

							return sample


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							def model(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		*lowerCamelCase_							:	int		):
									return sample * t / (t + 1)

							return model


					def lowerCamelCase_	(		self							:	str     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
							    """num_train_timesteps""": 20_00,
							    """snr""": 0.15,
							    """sigma_min""": 0.01,
							    """sigma_max""": 13_48,
							    """sampling_eps""": 1e-5,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							int						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							int						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)
							kwargs.update(lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Tuple						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							int						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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

									SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]     ,		[1, 1_00, 10_00]		):
									self.check_over_configs(sigma_min=lowerCamelCase_     ,		sigma_max=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Dict						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	3

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							Dict						=	self.dummy_sample_deter

							scheduler.set_sigmas(lowerCamelCase_		)
							scheduler.set_timesteps(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(0		)

							for i, t in enumerate(scheduler.timesteps		):
									SCREAMING_SNAKE_CASE				:							int						=	scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps		):
											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		).prev_sample

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	scheduler.step_pred(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	output.prev_sample, output.prev_sample_mean

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert np.isclose(result_sum.item()     ,		14_372_758_528.0		)
							assert np.isclose(result_mean.item()     ,		18_714_530.0		)



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							Any						=	kwargs.pop("""num_inference_steps"""     ,		lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							List[str]						=	0.1 * sample

									if num_inference_steps is not None and hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											scheduler.set_timesteps(lowerCamelCase_		)
									elif num_inference_steps is not None and not hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	num_inference_steps

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler.step_pred(lowerCamelCase_     ,		0     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler.step_pred(lowerCamelCase_     ,		1     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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



