






'''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 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=224  ,      _lowercase=1000  ,      _lowercase=[3, 3, 6, 4]  ,      _lowercase=[48, 56, 112, 220]  ,      )						->  str:
							lowercase_      :   Tuple        =			parent
							lowercase_      :   List[Any]        =			batch_size
							lowercase_      :   List[str]        =			num_channels
							lowercase_      :   int        =			is_training
							lowercase_      :   str        =			use_labels
							lowercase_      :   Tuple        =			hidden_dropout_prob
							lowercase_      :   Union[str, Any]        =			attention_probs_dropout_prob
							lowercase_      :   Optional[Any]        =			num_labels
							lowercase_      :   str        =			image_size
							lowercase_      :   Dict        =			layer_depths
							lowercase_      :   Optional[int]        =			embed_dims
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   Tuple        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

							lowercase_      :   List[str]        =			self.get_config()

							return config, pixel_values, labels
	def        lowerCamelCase__		(							self       )						->  Tuple:
							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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Any:
							lowercase_      :   List[Any]        =			SwiftFormerModel(config=__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							lowercase_      :   List[Any]        =			model(__lowerCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.embed_dims[-1], 7, 7)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   List[str]        =			self.num_labels
							lowercase_      :   Optional[Any]        =			SwiftFormerForImageClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()
							lowercase_      :   Dict        =			model(__lowerCamelCase  ,      labels=__lowerCamelCase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)       )

							lowercase_      :   Dict        =			SwiftFormerForImageClassification(__lowerCamelCase       )
							model.to(__lowerCamelCase       )
							model.eval()

							lowercase_      :   int        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
							lowercase_      :   Tuple        =			model(__lowerCamelCase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)       )







	def        lowerCamelCase__		(							self       )						->  Any:
							(lowercase_)      :   Optional[Any]        =			self.prepare_config_and_inputs()
							lowercase_      :   Union[str, Any]        =			{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class 							__magic_name__     (	lowerCamelCase__,						lowerCamelCase__,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE_					:      Tuple        =  (
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  False
	SCREAMING_SNAKE_CASE_					:      int        =  False
	SCREAMING_SNAKE_CASE_					:      int        =  False
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  False
	SCREAMING_SNAKE_CASE_					:      Any        =  False
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   int        =			SwiftFormerModelTester(self       )
							lowercase_      :   List[str]        =			ConfigTester(
							    self  ,      config_class=__lowerCamelCase  ,      has_text_modality=__lowerCamelCase  ,      hidden_size=37  ,      num_attention_heads=12  ,      num_hidden_layers=12  ,      )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							self.config_tester.run_common_tests()
	@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							pass
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   Tuple        =			model_class(__lowerCamelCase       )
													lowercase_      :   Tuple        =			model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__lowerCamelCase  ,      nn.Linear       )       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   Optional[int]        =			self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase_      :   int        =			['''pixel_values''']
													self.assertListEqual(arg_names[:1]  ,      __lowerCamelCase       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Union[str, Any]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_      :   str        =			SwiftFormerModel.from_pretrained(__lowerCamelCase       )
													self.assertIsNotNone(__lowerCamelCase       )
	@unittest.skip(reason='SwiftFormer does not output attentions'       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							pass
	def        lowerCamelCase__		(							self       )						->  Tuple:






							def check_hidden_states_output(_lowercase  ,      _lowercase  ,      _lowercase       ):
													lowercase_      :   Optional[Any]        =			model_class(__lowerCamelCase       )
													model.to(__lowerCamelCase       )
													model.eval()

													with torch.no_grad():
																			lowercase_      :   str        =			model(**self._prepare_for_class(__lowerCamelCase  ,      __lowerCamelCase       )       )

													lowercase_      :   Dict        =			outputs.hidden_states

													lowercase_      :   Dict        =			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),
																			        ]       )  ,      )

							lowercase_      :   List[Any]        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   Dict        =			True
													check_hidden_states_output(__lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase_      :   Optional[int]        =			True

													check_hidden_states_output(__lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:






							def _config_zero_init(_lowercase       ):
													lowercase_      :   List[Any]        =			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-1_0       )
																			if isinstance(getattr(__lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase       )  ,      __lowerCamelCase       ):
																									lowercase_      :   List[str]        =			_config_zero_init(getattr(__lowerCamelCase  ,      __lowerCamelCase       )       )
																									setattr(__lowerCamelCase  ,      __lowerCamelCase  ,      __lowerCamelCase       )
													return configs_no_init

							lowercase_      :   Dict        =			self.model_tester.prepare_config_and_inputs_for_common()

							lowercase_      :   Union[str, Any]        =			_config_zero_init(__lowerCamelCase       )
							for model_class in self.all_model_classes:
													lowercase_      :   Tuple        =			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        lowerCamelCase__		(							self       )						->  Tuple:
							pass






def 	_UpperCAmelCase    (  )			->			Dict:





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






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







	"""simple docstring"""



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







	@slow
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   Optional[int]        =			SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(__lowerCamelCase       )

							lowercase_      :   int        =			self.default_image_processor
							lowercase_      :   int        =			prepare_img()
							lowercase_      :   Optional[int]        =			image_processor(images=__lowerCamelCase  ,      return_tensors='pt'       ).to(__lowerCamelCase       )

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

							# verify the logits
							lowercase_      :   Optional[int]        =			torch.Size((1, 1000)       )
							self.assertEqual(outputs.logits.shape  ,      __lowerCamelCase       )

							lowercase_      :   int        =			torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]       ).to(__lowerCamelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,      __lowerCamelCase  ,      atol=1E-4       )       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	int						,			a		:	int							)			->			int:





						"""simple docstring"""
						while second != 0:
												lowercase_      :   Any        =			first & second
												first ^= second
												lowercase_      :   List[str]        =			c << 1
						return first


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A:      Union[str, Any]  				=				int(input("Enter the first number: ").strip())
						A:      Union[str, Any]  				=				int(input("Enter the second number: ").strip())
						print(f"""{add(first, second) = }""")
