






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   lowerCamelCase				:



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=3    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=1_0_0_0    ,		_UpperCamelCase=[3, 3, 6, 4]    ,		_UpperCamelCase=[4_8, 5_6, 1_1_2, 2_2_0]    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				str						=						parent
							UpperCAmelCase_   :				Union[str, Any]						=						batch_size
							UpperCAmelCase_   :				List[str]						=						num_channels
							UpperCAmelCase_   :				Dict						=						is_training
							UpperCAmelCase_   :				Union[str, Any]						=						use_labels
							UpperCAmelCase_   :				List[Any]						=						hidden_dropout_prob
							UpperCAmelCase_   :				Tuple						=						attention_probs_dropout_prob
							UpperCAmelCase_   :				Any						=						num_labels
							UpperCAmelCase_   :				str						=						image_size
							UpperCAmelCase_   :				Tuple						=						layer_depths
							UpperCAmelCase_   :				List[Any]						=						embed_dims



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							UpperCAmelCase_   :				Any						=						None
							if self.use_labels:
												UpperCAmelCase_   :				Dict						=						ids_tensor([self.batch_size]    ,		self.num_labels       )

							UpperCAmelCase_   :				str						=						self.get_config()

							return config, pixel_values, labels



		def  __UpperCAmelCase      (							self       )     ->							str:
							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=_UpperCamelCase    ,		layer_scale_init_value=1E-5    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModel(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.embed_dims[-1], 7, 7)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=						self.num_labels
							UpperCAmelCase_   :				Dict						=						SwiftFormerForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Tuple						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )

							UpperCAmelCase_   :				Union[str, Any]						=						SwiftFormerForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()

							UpperCAmelCase_   :				int						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							(UpperCAmelCase_)   :				Union[str, Any]						=						self.prepare_config_and_inputs()
							UpperCAmelCase_   :				Tuple						=						{'pixel_values': pixel_values}
							return config, inputs_dict





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

		_snake_case   :   str							=  False
		_snake_case   :   Optional[Any]							=  False
		_snake_case   :   Dict							=  False
		_snake_case   :   Optional[int]							=  False
		_snake_case   :   Optional[int]							=  False



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModelTester(self       )
							UpperCAmelCase_   :				List[Any]						=						ConfigTester(
							    self    ,		config_class=_UpperCamelCase    ,		has_text_modality=_UpperCamelCase    ,		hidden_size=3_7    ,		num_attention_heads=1_2    ,		num_hidden_layers=1_2    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							self.config_tester.run_common_tests()



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



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Any						=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				Optional[Any]						=						model_class(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(_UpperCamelCase    ,		nn.Linear       )       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase_   :				Any						=						['pixel_values']
												self.assertListEqual(arg_names[:1]    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				int						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModel.from_pretrained(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )



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



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							def check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				int						=						model_class(_UpperCamelCase       )
												model.to(_UpperCamelCase       )
												model.eval()

												with torch.no_grad():
																	UpperCAmelCase_   :				Union[str, Any]						=						model(**self._prepare_for_class(_UpperCamelCase    ,		_UpperCamelCase       )       )

												UpperCAmelCase_   :				Dict						=						outputs.hidden_states

												UpperCAmelCase_   :				int						=						8
												self.assertEqual(len(_UpperCamelCase       )    ,		_UpperCamelCase       )  # 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(_UpperCamelCase       )       ):
																	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),
																	        ]       )    ,		)

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

							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				int						=						True
												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

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

												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							def _config_zero_init(_UpperCamelCase       ):
												UpperCAmelCase_   :				Dict						=						copy.deepcopy(_UpperCamelCase       )
												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(_UpperCamelCase    ,		_UpperCamelCase    ,		1E-10       )
																	if isinstance(getattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		_UpperCamelCase       ):
																						UpperCAmelCase_   :				List[str]						=						_config_zero_init(getattr(_UpperCamelCase    ,		_UpperCamelCase       )       )
																						setattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
												return configs_no_init

							UpperCAmelCase_   :				int						=						self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_   :				int						=						_config_zero_init(_UpperCamelCase       )
							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				Optional[Any]						=						model_class(config=_UpperCamelCase       )
												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  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							pass





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Dict						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






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



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



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						self.default_image_processor
							UpperCAmelCase_   :				Tuple						=						prepare_img()
							UpperCAmelCase_   :				Dict						=						image_processor(images=_UpperCamelCase    ,		return_tensors='pt'       ).to(_UpperCamelCase       )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_   :				List[str]						=						model(**_UpperCamelCase       )

							# verify the logits
							UpperCAmelCase_   :				Optional[Any]						=						torch.Size((1, 1_0_0_0)       )
							self.assertEqual(outputs.logits.shape    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(_UpperCamelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		_UpperCamelCase    ,		atol=1E-4       )       )














def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[str]					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							str					,	__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					if index == r:
										for j in range(__snake_case  ):
															print(data[j]					,	end=' '  )
										print(' '  )
										return
					#  When no more elements are there to put in data[]
					if i >= n:
										return
					# current is included, put next at next location
					UpperCAmelCase_   :				int						=						arr[i]
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	index + 1					,	__snake_case					,	i + 1  )
					# current is excluded, replace it with
					# next (Note that i+1 is passed, but
					# index is not changed)
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	i + 1  )
					# The main function that prints all combinations
					# of size r in arr[] of size n. This function
					# mainly uses combinationUtil()




def 					lowercase__				(					__snake_case   :							List[Any]					,	__snake_case   :							List[Any]					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						[0] * r
					# Print all combination using temporary array 'data[]'
					combination_util(__snake_case					,	__snake_case					,	__snake_case					,	0					,	__snake_case					,	0  )


if __name__ == "__main__":
		# Driver code to check the function above
		__UpperCAmelCase     							=						[10, 20, 30, 40, 50]
		print_combination(arr, len(arr), 3)
		# This code is contributed by Ambuj sahu






