


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 	snake_case_       :
					def __init__(				self       :List[Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :List[str]=13      ,__snake_case       :Union[str, Any]=3      ,__snake_case       :Any=True      ,__snake_case       :List[Any]=True      ,__snake_case       :Union[str, Any]=0.1      ,__snake_case       :List[str]=0.1      ,__snake_case       :List[Any]=2_24      ,__snake_case       :int=10_00      ,__snake_case       :Any=[3, 3, 6, 4]      ,__snake_case       :Any=[48, 56, 1_12, 2_20]      ,) ->   Dict:
												a__					=					parent
												a__					=					batch_size
												a__					=					num_channels
												a__					=					is_training
												a__					=					use_labels
												a__					=					hidden_dropout_prob
												a__					=					attention_probs_dropout_prob
												a__					=					num_labels
												a__					=					image_size
												a__					=					layer_depths
												a__					=					embed_dims
					def 			lowerCamelCase__(				self       :Dict				) ->   Union[str, Any]:
												a__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

												a__					=					self.get_config()

												return config, pixel_values, labels
					def 			lowerCamelCase__(				self       :List[Any]				) ->   List[Any]:
												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=__snake_case      ,layer_scale_init_value=1E-5      ,)
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :Dict      ,__snake_case       :List[Any]      ,__snake_case       :Union[str, Any]				) ->   Tuple:
												a__					=					SwiftFormerModel(config=__snake_case				)
												model.to(__snake_case				)
												model.eval()
												a__					=					model(__snake_case				)
												self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.embed_dims[-1], 7, 7)				)
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[Any]      ,__snake_case       :Tuple				) ->   Any:
												a__					=					self.num_labels
												a__					=					SwiftFormerForImageClassification(__snake_case				)
												model.to(__snake_case				)
												model.eval()
												a__					=					model(__snake_case      ,labels=__snake_case				)
												self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)				)

												a__					=					SwiftFormerForImageClassification(__snake_case				)
												model.to(__snake_case				)
												model.eval()

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



					def 			lowerCamelCase__(				self       :str				) ->   str:
												((a__)				, (a__)				, (a__))					=					self.prepare_config_and_inputs()
												a__					=					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   str   				=      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__ :   List[str]   				=      (
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

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

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)
																			a__					=					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case      ,nn.Linear				)				)
					def 			lowerCamelCase__(				self       :str				) ->   int:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																			a__					=					['pixel_values']
																			self.assertListEqual(arg_names[:1]      ,__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   str:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case				)
					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__snake_case				)
					@slow
					def 			lowerCamelCase__(				self       :List[str]				) ->   Dict:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__					=					SwiftFormerModel.from_pretrained(__snake_case				)
																			self.assertIsNotNone(__snake_case				)
					@unittest.skip(reason='SwiftFormer does not output attentions'				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[str]:
												pass
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												def check_hidden_states_output(__snake_case       :List[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]				):
																			a__					=					model_class(__snake_case				)
																			model.to(__snake_case				)
																			model.eval()

																			with torch.no_grad():
																										a__					=					model(**self._prepare_for_class(__snake_case      ,__snake_case				)				)

																			a__					=					outputs.hidden_states

																			a__					=					8
																			self.assertEqual(len(__snake_case				)      ,__snake_case				)  # 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(__snake_case				)				):
																										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),
																										        ]				)      ,)

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__					=					True
																			check_hidden_states_output(__snake_case      ,__snake_case      ,__snake_case				)

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

																			check_hidden_states_output(__snake_case      ,__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[Any]:
												def _config_zero_init(__snake_case       :List[str]				):
																			a__					=					copy.deepcopy(__snake_case				)
																			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(__snake_case      ,__snake_case      ,1E-10				)
																										if isinstance(getattr(__snake_case      ,__snake_case      ,__snake_case				)      ,__snake_case				):
																																	a__					=					_config_zero_init(getattr(__snake_case      ,__snake_case				)				)
																																	setattr(__snake_case      ,__snake_case      ,__snake_case				)
																			return configs_no_init

												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

												a__					=					_config_zero_init(__snake_case				)
												for model_class in self.all_model_classes:
																			a__					=					model_class(config=__snake_case				)
																			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       :Union[str, Any]				) ->   str:
												pass






def 							__lowercase       (			):
							a__					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
							return image







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



					@slow
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												a__					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'				).to(__snake_case				)

												a__					=					self.default_image_processor
												a__					=					prepare_img()
												a__					=					image_processor(images=__snake_case      ,return_tensors='pt'				).to(__snake_case				)

												# forward pass
												with torch.no_grad():
																			a__					=					model(**__snake_case				)

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

												a__					=					torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]				).to(__snake_case				)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,__snake_case      ,atol=1E-4				)				)








from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0       ):
							a__					=					0

							for outer_width in range(3 ,   (limit // 4) + 2       ):
														if outer_width**2 > limit:
																					a__					=					max(ceil(sqrt(outer_width**2 - limit       )       ) ,   1       )
														else:
																					a__					=					1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


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




