"""simple docstring"""






import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Optional[int]=13    ,lowerCamelCase__   :	Optional[int]=32    ,lowerCamelCase__   :	List[str]=2    ,lowerCamelCase__   :	List[Any]=3    ,lowerCamelCase__   :	str=16    ,lowerCamelCase__   :	Union[str, Any]=[1, 2, 1]    ,lowerCamelCase__   :	Tuple=[2, 2, 4]    ,lowerCamelCase__   :	Optional[Any]=2    ,lowerCamelCase__   :	Optional[Any]=2.0    ,lowerCamelCase__   :	Union[str, Any]=True    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	Tuple=0.0    ,lowerCamelCase__   :	Union[str, Any]=0.1    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Optional[Any]=False    ,lowerCamelCase__   :	Union[str, Any]=True    ,lowerCamelCase__   :	Optional[int]=0.0_2    ,lowerCamelCase__   :	Optional[int]=1e-5    ,lowerCamelCase__   :	Optional[int]=True    ,lowerCamelCase__   :	int=None    ,lowerCamelCase__   :	Tuple=True    ,lowerCamelCase__   :	Optional[Any]=10    ,lowerCamelCase__   :	Optional[Any]=8    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							patch_size
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							embed_dim
									UpperCAmelCase__             =							depths
									UpperCAmelCase__             =							num_heads
									UpperCAmelCase__             =							window_size
									UpperCAmelCase__             =							mlp_ratio
									UpperCAmelCase__             =							qkv_bias
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							drop_path_rate
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							use_absolute_embeddings
									UpperCAmelCase__             =							patch_norm
									UpperCAmelCase__             =							layer_norm_eps
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							is_training
									UpperCAmelCase__             =							scope
									UpperCAmelCase__             =							use_labels
									UpperCAmelCase__             =							type_sequence_label_size
									UpperCAmelCase__             =							encoder_stride







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

									UpperCAmelCase__             =							self.get_config()

									return config, pixel_values, labels







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return SwinvaConfig(
									    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    ,)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	List[Any]			):
									UpperCAmelCase__             =							SwinvaModel(config=lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__			)

									UpperCAmelCase__             =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
									UpperCAmelCase__             =							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   :	List[str]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Dict			):
									UpperCAmelCase__             =							SwinvaForMaskedImageModeling(config=lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(
									    result.logits.shape    ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

									# test greyscale images
									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							SwinvaForMaskedImageModeling(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()

									UpperCAmelCase__             =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, 1, self.image_size, self.image_size)			)







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.type_sequence_label_size
									UpperCAmelCase__             =							SwinvaForImageClassification(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__    ,labels=lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.type_sequence_label_size)			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.prepare_config_and_inputs()
									UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							config_and_inputs
									UpperCAmelCase__             =							{'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = (
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					snake_case__				    = (
					    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							SwinvaModelTester(self			)
									UpperCAmelCase__             =							ConfigTester(self    ,config_class=lowerCamelCase__    ,embed_dim=37			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									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   :	Optional[int]			):
									UpperCAmelCase__             =							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   :	Any			):
									pass







					@unittest.skip(reason='Swinv2 does not use inputs_embeds'			)
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

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

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







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()
									UpperCAmelCase__             =							True

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							True
													UpperCAmelCase__             =							False
													UpperCAmelCase__             =							True
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.eval()
													with torch.no_grad():
																	UpperCAmelCase__             =							model(**self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__			)			)
													UpperCAmelCase__             =							outputs.attentions
													UpperCAmelCase__             =							len(self.model_tester.depths			)
													self.assertEqual(len(lowerCamelCase__			)    ,lowerCamelCase__			)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													UpperCAmelCase__             =							True
													UpperCAmelCase__             =							config.window_size**2
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.eval()
													with torch.no_grad():
																	UpperCAmelCase__             =							model(**self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__			)			)
													UpperCAmelCase__             =							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]    ,)
													UpperCAmelCase__             =							len(lowerCamelCase__			)

													# Check attention is always last and order is fine
													UpperCAmelCase__             =							True
													UpperCAmelCase__             =							True
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.eval()
													with torch.no_grad():
																	UpperCAmelCase__             =							model(**self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__			)			)

													if hasattr(self.model_tester    ,'num_hidden_states_types'			):
																	UpperCAmelCase__             =							self.model_tester.num_hidden_states_types
													else:
																	# also another +1 for reshaped_hidden_states
																	UpperCAmelCase__             =							2
													self.assertEqual(out_len + added_hidden_states    ,len(lowerCamelCase__			)			)

													UpperCAmelCase__             =							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   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							model_class(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()

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

									UpperCAmelCase__             =							outputs.hidden_states

									UpperCAmelCase__             =							getattr(
									    self.model_tester    ,'expected_num_hidden_layers'    ,len(self.model_tester.depths			) + 1			)
									self.assertEqual(len(lowerCamelCase__			)    ,lowerCamelCase__			)

									# Swinv2 has a different seq_length
									UpperCAmelCase__             =							(
									    config.patch_size
									    if isinstance(config.patch_size    ,collections.abc.Iterable			)
									    else (config.patch_size, config.patch_size)
									)

									UpperCAmelCase__             =							(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]    ,)

									UpperCAmelCase__             =							outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCamelCase__			)    ,lowerCamelCase__			)

									UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							reshaped_hidden_states[0].shape
									UpperCAmelCase__             =							(
									    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   :	Union[str, Any]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									UpperCAmelCase__             =							(
									    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:
													UpperCAmelCase__             =							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"]
													UpperCAmelCase__             =							True

													self.check_hidden_states_output(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()
									UpperCAmelCase__             =							3

									UpperCAmelCase__             =							(
									    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)
									)
									UpperCAmelCase__             =							(
									    config.patch_size
									    if isinstance(config.patch_size    ,collections.abc.Iterable			)
									    else (config.patch_size, config.patch_size)
									)

									UpperCAmelCase__             =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									UpperCAmelCase__             =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							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"]
													UpperCAmelCase__             =							True
													self.check_hidden_states_output(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,(padded_height, padded_width)			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)







					@slow
					def 		__lowerCAmelCase						(				self   :	int			):
									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase__             =							SwinvaModel.from_pretrained(lowerCamelCase__			)
													self.assertIsNotNone(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									UpperCAmelCase__             =							_config_zero_init(lowerCamelCase__			)
									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							model_class(config=lowerCamelCase__			)
													for name, param in model.named_parameters():
																	if "embeddings" not in name and "logit_scale" 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 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					@cached_property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									return (
									    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'			)
									    if is_vision_available()
									    else None
									)







					@slow
					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'			).to(
									    lowerCamelCase__			)
									UpperCAmelCase__             =							self.default_image_processor

									UpperCAmelCase__             =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
									UpperCAmelCase__             =							image_processor(images=lowerCamelCase__    ,return_tensors='pt'			).to(lowerCamelCase__			)

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

									# verify the logits
									UpperCAmelCase__             =							torch.Size((1, 1_000)			)
									self.assertEqual(outputs.logits.shape    ,lowerCamelCase__			)
									UpperCAmelCase__             =							torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]			).to(lowerCamelCase__			)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,lowerCamelCase__    ,atol=1e-4			)			)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



