


'''simple docstring'''






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

		from transformers import SwiftFormerForImageClassification, SwiftFormerModel
		from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=3   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=2_24   ,       lowerCamelCase_=10_00   ,       lowerCamelCase_=[3, 3, 6, 4]   ,       lowerCamelCase_=[48, 56, 1_12, 2_20]   ,       )			->				Union[str, Any]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      attention_probs_dropout_prob
														lowerCAmelCase__			  =      num_labels
														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      layer_depths
														lowerCAmelCase__			  =      embed_dims







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.num_labels  )

														lowerCAmelCase__			  =      self.get_config()

														return config, pixel_values, labels







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														return SwiftFormerConfig(
														    depths=self.layer_depths   ,       embed_dims=self.embed_dims   ,       mlp_ratio=4   ,       downsamples=[True, True, True, True]   ,       hidden_act='''gelu'''   ,       num_labels=self.num_labels   ,       down_patch_size=3   ,       down_stride=2   ,       down_pad=1   ,       drop_rate=0.0   ,       drop_path_rate=0.0   ,       use_layer_scale=lowerCamelCase_   ,       layer_scale_init_value=1e-5   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      SwiftFormerModel(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.embed_dims[-1], 7, 7)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      SwiftFormerForImageClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )

														lowerCAmelCase__			  =      SwiftFormerForImageClassification(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()

														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														((lowerCAmelCase__)  ,   (lowerCAmelCase__)  ,   (lowerCAmelCase__))			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__			  =      {'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Union[str, Any]      		= (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							lowercase__     :  Tuple      		= (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__     :  Dict      		= False
							lowercase__     :  Dict      		= False
							lowercase__     :  List[Any]      		= False
							lowercase__     :  List[str]      		= False
							lowercase__     :  Tuple      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      SwiftFormerModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(
														    self   ,       config_class=lowerCamelCase_   ,       has_text_modality=lowerCamelCase_   ,       hidden_size=37   ,       num_attention_heads=12   ,       num_hidden_layers=12   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														self.config_tester.run_common_tests()







							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					lowerCAmelCase__			  =      model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCamelCase_   ,       nn.Linear  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__			  =      ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      SwiftFormerModel.from_pretrained(lowerCamelCase_  )
																					self.assertIsNotNone(lowerCamelCase_  )







							@unittest.skip(reason='''SwiftFormer does not output attentions'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														pass







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														def check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					lowerCAmelCase__			  =      model_class(lowerCamelCase_  )
																					model.to(lowerCamelCase_  )
																					model.eval()

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

																					lowerCAmelCase__			  =      outputs.hidden_states

																					lowerCAmelCase__			  =      8
																					self.assertEqual(len(lowerCamelCase_  )   ,       lowerCamelCase_  )  # TODO

																					# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																					# with the width and height being successively divided by 2, after every 2 blocks
																					for i in range(len(lowerCamelCase_  )  ):
																												self.assertEqual(
																												    hidden_states[i].shape   ,       torch.Size(
																												        [
																												            self.model_tester.batch_size,
																												            self.model_tester.embed_dims[i // 2],
																												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																												        ]  )   ,       )

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      True
																					check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )

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

																					check_hidden_states_output(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														def _config_zero_init(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      copy.deepcopy(lowerCamelCase_  )
																					for key in configs_no_init.__dict__.keys():
																												if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																																			setattr(lowerCamelCase_   ,       lowerCamelCase_   ,       1e-10  )
																												if isinstance(getattr(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )   ,       lowerCamelCase_  ):
																																			lowerCAmelCase__			  =      _config_zero_init(getattr(lowerCamelCase_   ,       lowerCamelCase_  )  )
																																			setattr(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
																					return configs_no_init

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														lowerCAmelCase__			  =      _config_zero_init(lowerCamelCase_  )
														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      model_class(config=lowerCamelCase_  )
																					for name, param in model.named_parameters():
																												if param.requires_grad:
																																			self.assertIn(
																																			    ((param.data.mean() * 1e9) / 1e9).round().item()   ,       [0.0, 1.0]   ,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,       )







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														pass


def    _snake_case  (	)		->       List[Any]:
							lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
							return image






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





							'''simple docstring'''






							@cached_property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''  ) if is_vision_available() else None







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''  ).to(lowerCamelCase_  )

														lowerCAmelCase__			  =      self.default_image_processor
														lowerCAmelCase__			  =      prepare_img()
														lowerCAmelCase__			  =      image_processor(images=lowerCamelCase_   ,       return_tensors='''pt'''  ).to(lowerCamelCase_  )

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

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 10_00)  )
														self.assertEqual(outputs.logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]  ).to(lowerCamelCase_  )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )



'''simple docstring'''






def    _snake_case  (	A					,       A			)		->       int:
							return x if y == 0 else greatest_common_divisor(A					,       x % y			)


def    _snake_case  (	A					,       A			)		->       int:
							return (x * y) // greatest_common_divisor(A					,       A			)


def    _snake_case  (	A = 20			)		->       int:
							lowerCAmelCase__			  =      1
							for i in range(1					,       n + 1			):
														lowerCAmelCase__			  =      lcm(A					,       A			)
							return g


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