
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   __lowercase						:






	def __init__(self				: str      ,	snake_case				: Union[str, Any]      ,	snake_case				: Union[str, Any]=13      ,	snake_case				: Tuple=32      ,	snake_case				: Optional[Any]=2      ,	snake_case				: Union[str, Any]=3      ,	snake_case				: Union[str, Any]=16      ,	snake_case				: Optional[int]=[1, 2, 1]      ,	snake_case				: List[Any]=[2, 2, 4]      ,	snake_case				: Optional[Any]=2      ,	snake_case				: Union[str, Any]=2.0      ,	snake_case				: Any=True      ,	snake_case				: Tuple=0.0      ,	snake_case				: Optional[Any]=0.0      ,	snake_case				: List[Any]=0.1      ,	snake_case				: Union[str, Any]="gelu"      ,	snake_case				: Optional[int]=False      ,	snake_case				: int=True      ,	snake_case				: Optional[int]=0.02      ,	snake_case				: int=1e-5      ,	snake_case				: Any=True      ,	snake_case				: List[str]=None      ,	snake_case				: Any=True      ,	snake_case				: str=10      ,	snake_case				: Union[str, Any]=8      ,	) ->			List[str]:
							_lowercase       :	Optional[Any]           =   parent
							_lowercase       :	int           =   batch_size
							_lowercase       :	Union[str, Any]           =   image_size
							_lowercase       :	str           =   patch_size
							_lowercase       :	int           =   num_channels
							_lowercase       :	Dict           =   embed_dim
							_lowercase       :	Any           =   depths
							_lowercase       :	str           =   num_heads
							_lowercase       :	int           =   window_size
							_lowercase       :	List[str]           =   mlp_ratio
							_lowercase       :	Union[str, Any]           =   qkv_bias
							_lowercase       :	str           =   hidden_dropout_prob
							_lowercase       :	List[Any]           =   attention_probs_dropout_prob
							_lowercase       :	str           =   drop_path_rate
							_lowercase       :	int           =   hidden_act
							_lowercase       :	Dict           =   use_absolute_embeddings
							_lowercase       :	str           =   patch_norm
							_lowercase       :	Optional[int]           =   layer_norm_eps
							_lowercase       :	int           =   initializer_range
							_lowercase       :	List[Any]           =   is_training
							_lowercase       :	str           =   scope
							_lowercase       :	Union[str, Any]           =   use_labels
							_lowercase       :	List[str]           =   type_sequence_label_size
							_lowercase       :	List[str]           =   encoder_stride






	def       _a(self				: List[str]    ) ->			Optional[int]:
							_lowercase       :	Dict           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_lowercase       :	Dict           =   None
							if self.use_labels:
													_lowercase       :	List[str]           =   ids_tensor([self.batch_size]      ,	self.type_sequence_label_size    )

							_lowercase       :	List[Any]           =   self.get_config()

							return config, pixel_values, labels






	def       _a(self				: Union[str, Any]    ) ->			Tuple:
							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       _a(self				: Dict      ,	snake_case				: List[Any]      ,	snake_case				: List[str]      ,	snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	List[str]           =   SwinvaModel(config=_lowercase    )
							model.to(_lowercase    )
							model.eval()
							_lowercase       :	Tuple           =   model(_lowercase    )

							_lowercase       :	Optional[Any]           =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
							_lowercase       :	str           =   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       _a(self				: Tuple      ,	snake_case				: Optional[Any]      ,	snake_case				: Union[str, Any]      ,	snake_case				: Any    ) ->			List[Any]:
							_lowercase       :	List[Any]           =   SwinvaForMaskedImageModeling(config=_lowercase    )
							model.to(_lowercase    )
							model.eval()
							_lowercase       :	Optional[int]           =   model(_lowercase    )
							self.parent.assertEqual(
							    result.logits.shape      ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

							# test greyscale images
							_lowercase       :	List[Any]           =   1
							_lowercase       :	List[str]           =   SwinvaForMaskedImageModeling(_lowercase    )
							model.to(_lowercase    )
							model.eval()

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






	def       _a(self				: int      ,	snake_case				: Union[str, Any]      ,	snake_case				: List[Any]      ,	snake_case				: Any    ) ->			Optional[Any]:
							_lowercase       :	Tuple           =   self.type_sequence_label_size
							_lowercase       :	str           =   SwinvaForImageClassification(_lowercase    )
							model.to(_lowercase    )
							model.eval()
							_lowercase       :	int           =   model(_lowercase      ,	labels=_lowercase    )
							self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.type_sequence_label_size)    )






	def       _a(self				: List[Any]    ) ->			Dict:
							_lowercase       :	Any           =   self.prepare_config_and_inputs()
							_lowercase      ,    _lowercase      ,    _lowercase       :	Dict           =   config_and_inputs
							_lowercase       :	int           =   {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class   __lowercase						(						__snake_case   , __snake_case   , unittest.TestCase       ):
	_A       =   (
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	_A       =   (
	    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   False






	def       _a(self				: Optional[int]    ) ->			Optional[int]:
							_lowercase       :	str           =   SwinvaModelTester(self    )
							_lowercase       :	int           =   ConfigTester(self      ,	config_class=_lowercase      ,	embed_dim=37    )






	def       _a(self				: str    ) ->			Any:
							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       _a(self				: List[Any]    ) ->			int:
							_lowercase       :	str           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowercase    )






	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."    )
	def       _a(self				: int    ) ->			Optional[int]:
							pass






	@unittest.skip(reason="Swinv2 does not use inputs_embeds"    )
	def       _a(self				: Any    ) ->			Union[str, Any]:
							pass






	def       _a(self				: int    ) ->			Optional[Any]:
							_lowercase      ,    _lowercase       :	Any           =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_lowercase       :	Optional[int]           =   model_class(_lowercase    )
													self.assertIsInstance(model.get_input_embeddings()      ,	(nn.Module)    )
													_lowercase       :	str           =   model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowercase      ,	nn.Linear    )    )






	def       _a(self				: Any    ) ->			Optional[int]:
							_lowercase      ,    _lowercase       :	Union[str, Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

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

													_lowercase       :	Optional[Any]           =   ["pixel_values"]
													self.assertListEqual(arg_names[:1]      ,	_lowercase    )






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase      ,    _lowercase       :	Any           =   self.model_tester.prepare_config_and_inputs_for_common()
							_lowercase       :	int           =   True

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

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

													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )      ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,	)
													_lowercase       :	Tuple           =   len(_lowercase    )

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

													if hasattr(self.model_tester      ,	"num_hidden_states_types"    ):
																			_lowercase       :	Optional[int]           =   self.model_tester.num_hidden_states_types
													else:
																			# also another +1 for reshaped_hidden_states
																			_lowercase       :	Union[str, Any]           =   2
													self.assertEqual(out_len + added_hidden_states      ,	len(_lowercase    )    )

													_lowercase       :	Optional[Any]           =   outputs.attentions

													self.assertEqual(len(_lowercase    )      ,	_lowercase    )

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






	def       _a(self				: Dict      ,	snake_case				: List[str]      ,	snake_case				: str      ,	snake_case				: List[str]      ,	snake_case				: Tuple    ) ->			Any:
							_lowercase       :	Tuple           =   model_class(_lowercase    )
							model.to(_lowercase    )
							model.eval()

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

							_lowercase       :	Union[str, Any]           =   outputs.hidden_states

							_lowercase       :	str           =   getattr(
							    self.model_tester      ,	"expected_num_hidden_layers"      ,	len(self.model_tester.depths    ) + 1    )
							self.assertEqual(len(_lowercase    )      ,	_lowercase    )

							# Swinv2 has a different seq_length
							_lowercase       :	Union[str, Any]           =   (
							    config.patch_size
							    if isinstance(config.patch_size      ,	collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							_lowercase       :	Union[str, Any]           =   (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]      ,	)

							_lowercase       :	Dict           =   outputs.reshaped_hidden_states
							self.assertEqual(len(_lowercase    )      ,	_lowercase    )

							_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Union[str, Any]           =   reshaped_hidden_states[0].shape
							_lowercase       :	Tuple           =   (
							    reshaped_hidden_states[0].view(_lowercase      ,	_lowercase      ,	height * width    ).permute(0      ,	2      ,	1    )
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]    )      ,	[num_patches, self.model_tester.embed_dim]      ,	)






	def       _a(self				: int    ) ->			List[str]:
							_lowercase      ,    _lowercase       :	Dict           =   self.model_tester.prepare_config_and_inputs_for_common()

							_lowercase       :	Any           =   (
							    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:
													_lowercase       :	Union[str, Any]           =   True
													self.check_hidden_states_output(_lowercase      ,	_lowercase      ,	_lowercase      ,	_lowercase    )

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

													self.check_hidden_states_output(_lowercase      ,	_lowercase      ,	_lowercase      ,	_lowercase    )






	def       _a(self				: Optional[int]    ) ->			Union[str, Any]:
							_lowercase      ,    _lowercase       :	List[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()
							_lowercase       :	List[Any]           =   3

							_lowercase       :	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)
							)
							_lowercase       :	int           =   (
							    config.patch_size
							    if isinstance(config.patch_size      ,	collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

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

							for model_class in self.all_model_classes:
													_lowercase       :	Optional[int]           =   True
													self.check_hidden_states_output(_lowercase      ,	_lowercase      ,	_lowercase      ,	(padded_height, padded_width)    )

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






	def       _a(self				: Tuple    ) ->			List[str]:
							_lowercase       :	Any           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase    )






	def       _a(self				: Any    ) ->			str:
							_lowercase       :	Dict           =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowercase    )






	@slow
	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowercase       :	Tuple           =   SwinvaModel.from_pretrained(_lowercase    )
													self.assertIsNotNone(_lowercase    )






	def       _a(self				: Union[str, Any]    ) ->			int:
							_lowercase      ,    _lowercase       :	Optional[Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

							_lowercase       :	Union[str, Any]           =   _config_zero_init(_lowercase    )
							for model_class in self.all_model_classes:
													_lowercase       :	str           =   model_class(config=_lowercase    )
													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   __lowercase						(						unittest.TestCase       ):






	@cached_property
	def       _a(self				: List[Any]    ) ->			Optional[Any]:
							return (
							    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"    )
							    if is_vision_available()
							    else None
							)






	@slow
	def       _a(self				: List[Any]    ) ->			Optional[Any]:
							_lowercase       :	Any           =   SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"    ).to(
							    _lowercase    )
							_lowercase       :	str           =   self.default_image_processor

							_lowercase       :	Dict           =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							_lowercase       :	Optional[Any]           =   image_processor(images=_lowercase      ,	return_tensors="pt"    ).to(_lowercase    )

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

							# verify the logits
							_lowercase       :	Any           =   torch.Size((1, 1000)    )
							self.assertEqual(outputs.logits.shape      ,	_lowercase    )
							_lowercase       :	str           =   torch.tensor([-0.39_47, -0.43_06, 0.00_26]    ).to(_lowercase    )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,	_lowercase      ,	atol=1e-4    )    )







def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[0, 1]
    UpperCamelCase_			  =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]			)
        if fib[i + 2] > n:
            break
        i += 1
    UpperCamelCase_			  =						0
    for j in range(len(SCREAMING_SNAKE_CASE_			) - 1			):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(F'''{solution() = }''')
