





'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor




class       UpperCamelCase__			:




     """simple docstring"""


     def __init__(		self							:	Optional[int]     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	List[Any]=13     ,		lowerCamelCase_							:	List[str]=32     ,		lowerCamelCase_							:	Optional[int]=2     ,		lowerCamelCase_							:	str=3     ,		lowerCamelCase_							:	Dict=16     ,		lowerCamelCase_							:	Optional[int]=[1, 2, 1]     ,		lowerCamelCase_							:	Optional[Any]=[2, 2, 4]     ,		lowerCamelCase_							:	Tuple=2     ,		lowerCamelCase_							:	int=2.0     ,		lowerCamelCase_							:	str=True     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Union[str, Any]=0.0     ,		lowerCamelCase_							:	List[Any]=0.1     ,		lowerCamelCase_							:	Any="gelu"     ,		lowerCamelCase_							:	Union[str, Any]=False     ,		lowerCamelCase_							:	int=True     ,		lowerCamelCase_							:	List[str]=0.02     ,		lowerCamelCase_							:	str=1e-5     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	str=None     ,		lowerCamelCase_							:	Union[str, Any]=True     ,		lowerCamelCase_							:	Dict=10     ,		lowerCamelCase_							:	Union[str, Any]=8     ,		lowerCamelCase_							:	Tuple=["stage1", "stage2"]     ,		lowerCamelCase_							:	Dict=[1, 2]     ,		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	parent
       SCREAMING_SNAKE_CASE				:							str						=	batch_size
       SCREAMING_SNAKE_CASE				:							List[Any]						=	image_size
       SCREAMING_SNAKE_CASE				:							Tuple						=	patch_size
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	num_channels
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	embed_dim
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	depths
       SCREAMING_SNAKE_CASE				:							List[Any]						=	num_heads
       SCREAMING_SNAKE_CASE				:							List[Any]						=	window_size
       SCREAMING_SNAKE_CASE				:							str						=	mlp_ratio
       SCREAMING_SNAKE_CASE				:							Any						=	qkv_bias
       SCREAMING_SNAKE_CASE				:							Dict						=	hidden_dropout_prob
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	drop_path_rate
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	hidden_act
       SCREAMING_SNAKE_CASE				:							int						=	use_absolute_embeddings
       SCREAMING_SNAKE_CASE				:							List[Any]						=	patch_norm
       SCREAMING_SNAKE_CASE				:							str						=	layer_norm_eps
       SCREAMING_SNAKE_CASE				:							Any						=	initializer_range
       SCREAMING_SNAKE_CASE				:							Dict						=	is_training
       SCREAMING_SNAKE_CASE				:							int						=	scope
       SCREAMING_SNAKE_CASE				:							Any						=	use_labels
       SCREAMING_SNAKE_CASE				:							List[str]						=	type_sequence_label_size
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	encoder_stride
       SCREAMING_SNAKE_CASE				:							List[Any]						=	out_features
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	out_indices


     def lowerCamelCase_	(		self							:	Tuple		):

       '''simple docstring'''



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

       SCREAMING_SNAKE_CASE				:							Tuple						=	None
       if self.use_labels:
         SCREAMING_SNAKE_CASE				:							Optional[Any]						=	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							:	Union[str, Any]		):

       '''simple docstring'''



       return SwinConfig(
           image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		depths=self.depths     ,		num_heads=self.num_heads     ,		window_size=self.window_size     ,		mlp_ratio=self.mlp_ratio     ,		qkv_bias=self.qkv_bias     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		drop_path_rate=self.drop_path_rate     ,		hidden_act=self.hidden_act     ,		use_absolute_embeddings=self.use_absolute_embeddings     ,		path_norm=self.patch_norm     ,		layer_norm_eps=self.layer_norm_eps     ,		initializer_range=self.initializer_range     ,		encoder_stride=self.encoder_stride     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		)


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

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	SwinModel(config=lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()
       SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_		)

       SCREAMING_SNAKE_CASE				:							Optional[int]						=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
       SCREAMING_SNAKE_CASE				:							int						=	int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

       self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, expected_seq_len, expected_dim)		)


     def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	int		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	SwinBackbone(config=lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()
       SCREAMING_SNAKE_CASE				:							int						=	model(lowerCamelCase_		)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps		)     ,		len(config.out_features		)		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		)     ,		[self.batch_size, model.channels[0], 16, 16]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		)     ,		len(config.out_features		)		)

       # verify backbone works with out_features=None
       SCREAMING_SNAKE_CASE				:							List[Any]						=	None
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinBackbone(config=lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()
       SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(lowerCamelCase_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		)     ,		1		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		)     ,		[self.batch_size, model.channels[-1], 4, 4]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		)     ,		1		)


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

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	SwinForMaskedImageModeling(config=lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()
       SCREAMING_SNAKE_CASE				:							int						=	model(lowerCamelCase_		)
       self.parent.assertEqual(
           result.logits.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

       # test greyscale images
       SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	1
       SCREAMING_SNAKE_CASE				:							List[str]						=	SwinForMaskedImageModeling(lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()

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


     def lowerCamelCase_	(		self							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Any		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							List[Any]						=	self.type_sequence_label_size
       SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinForImageClassification(lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()
       SCREAMING_SNAKE_CASE				:							Dict						=	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				:							List[Any]						=	SwinForImageClassification(lowerCamelCase_		)
       model.to(lowerCamelCase_		)
       model.eval()

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



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

       '''simple docstring'''



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







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




     """simple docstring"""
     SCREAMING_SNAKE_CASE__						=					(
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__						=					(
         {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__						=					True

     SCREAMING_SNAKE_CASE__						=					False
     SCREAMING_SNAKE_CASE__						=					False
     SCREAMING_SNAKE_CASE__						=					False


     def lowerCamelCase_	(		self							:	Any		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Dict						=	SwinModelTester(self		)
       SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	ConfigTester(self     ,		config_class=lowerCamelCase_     ,		embed_dim=37		)


     def lowerCamelCase_	(		self							:	Optional[Any]		):

       '''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 lowerCamelCase_	(		self							:	List[Any]		):

       '''simple docstring'''



       return


     def lowerCamelCase_	(		self							:	List[Any]		):

       '''simple docstring'''



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


     @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""		)
     def lowerCamelCase_	(		self							:	Tuple		):

       '''simple docstring'''



       pass


     def lowerCamelCase_	(		self							:	Optional[int]		):

       '''simple docstring'''



       super().test_training_gradient_checkpointing()


     def lowerCamelCase_	(		self							:	Dict		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							List[Any]						=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*lowerCamelCase_		)


     def lowerCamelCase_	(		self							:	Dict		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							str						=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_		)


     def lowerCamelCase_	(		self							:	Tuple		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							List[str]						=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)


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

       '''simple docstring'''



       pass


     @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""		)
     def lowerCamelCase_	(		self							:	List[Any]		):

       '''simple docstring'''



       pass


     def lowerCamelCase_	(		self							:	Dict		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:							int						=	model_class(lowerCamelCase_		)
         self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)		)
         SCREAMING_SNAKE_CASE				:							Tuple						=	model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCamelCase_     ,		nn.Linear		)		)


     def lowerCamelCase_	(		self							:	Tuple		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:							Optional[int]						=	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[int]						=	[*signature.parameters.keys()]

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


     def lowerCamelCase_	(		self							:	Tuple		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE				:							Tuple						=	True

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:							List[Any]						=	True
         SCREAMING_SNAKE_CASE				:							Any						=	False
         SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
         SCREAMING_SNAKE_CASE				:							List[Any]						=	model_class(lowerCamelCase_		)
         model.to(lowerCamelCase_		)
         model.eval()
         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:							List[str]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
         SCREAMING_SNAKE_CASE				:							Dict						=	outputs.attentions
         SCREAMING_SNAKE_CASE				:							Any						=	len(self.model_tester.depths		)
         self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         SCREAMING_SNAKE_CASE				:							List[str]						=	True
         SCREAMING_SNAKE_CASE				:							List[str]						=	config.window_size**2
         SCREAMING_SNAKE_CASE				:							str						=	model_class(lowerCamelCase_		)
         model.to(lowerCamelCase_		)
         model.eval()
         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:							List[str]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
         SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	outputs.attentions
         self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

         self.assertListEqual(
             list(attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)
         SCREAMING_SNAKE_CASE				:							Optional[Any]						=	len(lowerCamelCase_		)

         # Check attention is always last and order is fine
         SCREAMING_SNAKE_CASE				:							List[str]						=	True
         SCREAMING_SNAKE_CASE				:							Dict						=	True
         SCREAMING_SNAKE_CASE				:							Dict						=	model_class(lowerCamelCase_		)
         model.to(lowerCamelCase_		)
         model.eval()
         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

         # also another +1 for reshaped_hidden_states
         SCREAMING_SNAKE_CASE				:							Any						=	1 if model_class.__name__ == """SwinBackbone""" else 2
         self.assertEqual(out_len + added_hidden_states     ,		len(lowerCamelCase_		)		)

         SCREAMING_SNAKE_CASE				:							Optional[Any]						=	outputs.attentions

         self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)


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

       '''simple docstring'''



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

       with torch.no_grad():
         SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

       SCREAMING_SNAKE_CASE				:							str						=	outputs.hidden_states

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

       # Swin has a different seq_length
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	(
           config.patch_size
           if isinstance(config.patch_size     ,		collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE				:							Optional[int]						=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]		)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

       if not model_class.__name__ == "SwinBackbone":
         SCREAMING_SNAKE_CASE				:							Tuple						=	outputs.reshaped_hidden_states
         self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

         SCREAMING_SNAKE_CASE				:							Dict						=	reshaped_hidden_states[0].shape
         SCREAMING_SNAKE_CASE				:							Any						=	(
             reshaped_hidden_states[0].view(lowerCamelCase_     ,		lowerCamelCase_     ,		height * width		).permute(0     ,		2     ,		1		)
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]		)     ,		[num_patches, self.model_tester.embed_dim]     ,		)


     def lowerCamelCase_	(		self							:	Any		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE				:							Optional[int]						=	(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable		)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

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

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

         self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


     def lowerCamelCase_	(		self							:	int		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Tuple						=	self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE				:							int						=	3

       SCREAMING_SNAKE_CASE				:							int						=	(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable		)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       SCREAMING_SNAKE_CASE				:							Optional[Any]						=	(
           config.patch_size
           if isinstance(config.patch_size     ,		collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE				:							Any						=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       SCREAMING_SNAKE_CASE				:							List[Any]						=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:							List[Any]						=	True
         self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE				:							Optional[int]						=	True
         self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)


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

       '''simple docstring'''



       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE				:							Dict						=	SwinModel.from_pretrained(lowerCamelCase_		)
         self.assertIsNotNone(lowerCamelCase_		)



     def lowerCamelCase_	(		self							:	List[Any]		):

       '''simple docstring'''



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

       SCREAMING_SNAKE_CASE				:							Optional[int]						=	_config_zero_init(lowerCamelCase_		)
       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(config=lowerCamelCase_		)
         for name, param in model.named_parameters():
           if "embeddings" not in name and param.requires_grad:
             self.assertIn(
                 ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,		)







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




     """simple docstring"""


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

       '''simple docstring'''



       return (
           AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
           if is_vision_available()
           else None
       )



     @slow
     def lowerCamelCase_	(		self							:	List[str]		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		).to(lowerCamelCase_		)
       SCREAMING_SNAKE_CASE				:							List[Any]						=	self.default_image_processor

       SCREAMING_SNAKE_CASE				:							Any						=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       SCREAMING_SNAKE_CASE				:							int						=	image_processor(images=lowerCamelCase_     ,		return_tensors="""pt"""		).to(lowerCamelCase_		)

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

       # verify the logits
       SCREAMING_SNAKE_CASE				:							List[str]						=	torch.Size((1, 10_00)		)
       self.assertEqual(outputs.logits.shape     ,		lowerCamelCase_		)
       SCREAMING_SNAKE_CASE				:							int						=	torch.tensor([-0.0_948, -0.6_454, -0.0_921]		).to(lowerCamelCase_		)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		lowerCamelCase_     ,		atol=1e-4		)		)







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




     """simple docstring"""
     SCREAMING_SNAKE_CASE__						=					(SwinBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__						=					SwinConfig


     def lowerCamelCase_	(		self							:	str		):

       '''simple docstring'''



       SCREAMING_SNAKE_CASE				:							Any						=	SwinModelTester(self		)










'''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		)



