



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     :						Dict				,	A     :						Dict				,	A     :						List[str]=13				,	A     :						Optional[int]=3				,	A     :						Dict=True				,	A     :						Dict=True				,	A     :						Any=0.1				,	A     :						List[str]=0.1				,	A     :						Any=2_24				,	A     :						Tuple=10_00				,	A     :						Union[str, Any]=[3, 3, 6, 4]				,	A     :						Union[str, Any]=[48, 56, 1_12, 2_20]				,	)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     is_training
						_UpperCAmelCase	 =     use_labels
						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     attention_probs_dropout_prob
						_UpperCAmelCase	 =     num_labels
						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     layer_depths
						_UpperCAmelCase	 =     embed_dims



			def 					_lowerCamelCase (		self     :						str)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

						_UpperCAmelCase	 =     self.get_config()

						return config, pixel_values, labels



			def 					_lowerCamelCase (		self     :						Any)     ->					Tuple:



						"""simple docstring"""






						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=_SCREAMING_SNAKE_CASE				,	layer_scale_init_value=1E-5				,	)



			def 					_lowerCamelCase (		self     :						int				,	A     :						Union[str, Any]				,	A     :						Dict				,	A     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwiftFormerModel(config=_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						_UpperCAmelCase	 =     model(_SCREAMING_SNAKE_CASE)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.embed_dims[-1], 7, 7))



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[str]				,	A     :						int				,	A     :						str)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.num_labels
						_UpperCAmelCase	 =     SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()
						_UpperCAmelCase	 =     model(_SCREAMING_SNAKE_CASE				,	labels=_SCREAMING_SNAKE_CASE)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels))

						_UpperCAmelCase	 =     SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
						model.to(_SCREAMING_SNAKE_CASE)
						model.eval()

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



			def 					_lowerCamelCase (		self     :						Any)     ->					Tuple:



						"""simple docstring"""






						((_UpperCAmelCase) ,  (_UpperCAmelCase) ,  (_UpperCAmelCase))	 =     self.prepare_config_and_inputs()
						_UpperCAmelCase	 =     {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			UpperCamelCase           =  (
			    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						Dict)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwiftFormerModelTester(self)
						_UpperCAmelCase	 =     ConfigTester(
						    self				,	config_class=_SCREAMING_SNAKE_CASE				,	has_text_modality=_SCREAMING_SNAKE_CASE				,	hidden_size=37				,	num_attention_heads=12				,	num_hidden_layers=12				,	)



			def 					_lowerCamelCase (		self     :						List[Any])     ->					int:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			@unittest.skip(reason='SwiftFormer does not use inputs_embeds')
			def 					_lowerCamelCase (		self     :						Any)     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

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



			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

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

									_UpperCAmelCase	 =     ['pixel_values']
									self.assertListEqual(arg_names[:1]				,	_SCREAMING_SNAKE_CASE)



			def 					_lowerCamelCase (		self     :						List[Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)



			@slow
			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase	 =     SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE)
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE)



			@unittest.skip(reason='SwiftFormer does not output attentions')
			def 					_lowerCamelCase (		self     :						List[Any])     ->					str:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						def check_hidden_states_output(A     :						Optional[Any]				,	A     :						Optional[int]				,	A     :						Union[str, Any]):
									_UpperCAmelCase	 =     model_class(_SCREAMING_SNAKE_CASE)
									model.to(_SCREAMING_SNAKE_CASE)
									model.eval()

									with torch.no_grad():
												_UpperCAmelCase	 =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE))

									_UpperCAmelCase	 =     outputs.hidden_states

									_UpperCAmelCase	 =     8
									self.assertEqual(len(_SCREAMING_SNAKE_CASE)				,	_SCREAMING_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(_SCREAMING_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),
												        ])				,	)

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     True
									check_hidden_states_output(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE)

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

									check_hidden_states_output(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE)



			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						def _config_zero_init(A     :						int):
									_UpperCAmelCase	 =     copy.deepcopy(_SCREAMING_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(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	1E-10)
												if isinstance(getattr(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE)				,	_SCREAMING_SNAKE_CASE):
															_UpperCAmelCase	 =     _config_zero_init(getattr(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE))
															setattr(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE)
									return configs_no_init

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						_UpperCAmelCase	 =     _config_zero_init(_SCREAMING_SNAKE_CASE)
						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     model_class(config=_SCREAMING_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     :						List[str])     ->					int:



						"""simple docstring"""






						pass





def 							A			(  )       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image







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



			@cached_property
			def 					_lowerCamelCase (		self     :						int)     ->					str:



						"""simple docstring"""






						return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs') if is_vision_available() else None



			@slow
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs').to(_SCREAMING_SNAKE_CASE)

						_UpperCAmelCase	 =     self.default_image_processor
						_UpperCAmelCase	 =     prepare_img()
						_UpperCAmelCase	 =     image_processor(images=_SCREAMING_SNAKE_CASE				,	return_tensors='pt').to(_SCREAMING_SNAKE_CASE)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase	 =     model(**_SCREAMING_SNAKE_CASE)

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__     = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = ["YolosFeatureExtractor"]
							UpperCAmelCase__     = ["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
