





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 							_UpperCamelCase					:

							def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: str=13					,   _SCREAMING_SNAKE_CASE: Dict=3					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[str]=0.1					,   _SCREAMING_SNAKE_CASE: List[str]=0.1					,   _SCREAMING_SNAKE_CASE: int=224					,   _SCREAMING_SNAKE_CASE: List[Any]=1000					,   _SCREAMING_SNAKE_CASE: Tuple=[3, 3, 6, 4]					,   _SCREAMING_SNAKE_CASE: Any=[48, 56, 112, 220]					,   )       -> Union[str, 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 lowercase (		self: Tuple		)       -> Union[str, Any]:




														"""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 lowercase (		self: Union[str, Any]		)       -> Optional[int]:




														"""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=A__					,   layer_scale_init_value=1e-5					,   )

							def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> List[str]:




														"""simple docstring"""


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

							def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> List[str]:




														"""simple docstring"""


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

														UpperCamelCase_          =  SwiftFormerForImageClassification(A__		)
														model.to(A__		)
														model.eval()

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



							def lowercase (		self: Optional[Any]		)       -> Dict:




														"""simple docstring"""


														(UpperCamelCase_)          =  self.prepare_config_and_inputs()
														UpperCamelCase_          =  {"""pixel_values""": pixel_values}
														return config, inputs_dict



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

							_UpperCamelCase				:					Dict         =   False
							_UpperCamelCase				:					Union[str, Any]         =   False
							_UpperCamelCase				:					Any         =   False
							_UpperCamelCase				:					int         =   False
							_UpperCamelCase				:					Any         =   False

							def lowercase (		self: Union[str, Any]		)       -> List[str]:




														"""simple docstring"""


														UpperCamelCase_          =  SwiftFormerModelTester(self		)
														UpperCamelCase_          =  ConfigTester(
														    self					,   config_class=A__					,   has_text_modality=A__					,   hidden_size=37					,   num_attention_heads=12					,   num_hidden_layers=12					,   )

							def lowercase (		self: Optional[int]		)       -> str:




														"""simple docstring"""


														self.config_tester.run_common_tests()

							@unittest.skip(reason="SwiftFormer does not use inputs_embeds"		)
							def lowercase (		self: Tuple		)       -> List[Any]:




														"""simple docstring"""


														pass

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




														"""simple docstring"""


														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

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

							def lowercase (		self: Optional[int]		)       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase_          =  model_class(A__		)
																					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]					,   A__		)

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




														"""simple docstring"""


														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*A__		)

							def lowercase (		self: Tuple		)       -> Union[str, Any]:




														"""simple docstring"""


														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*A__		)

							@slow
							def lowercase (		self: Dict		)       -> List[str]:




														"""simple docstring"""


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

							@unittest.skip(reason="SwiftFormer does not output attentions"		)
							def lowercase (		self: Optional[int]		)       -> int:




														"""simple docstring"""


														pass

							def lowercase (		self: Optional[Any]		)       -> str:




														"""simple docstring"""


														def check_hidden_states_output(_SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[str]		):
																					UpperCamelCase_          =  model_class(A__		)
																					model.to(A__		)
																					model.eval()

																					with torch.no_grad():
																												UpperCamelCase_          =  model(**self._prepare_for_class(A__					,   A__		)		)

																					UpperCamelCase_          =  outputs.hidden_states

																					UpperCamelCase_          =  8
																					self.assertEqual(len(A__		)					,   A__		)  # 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(A__		)		):
																												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_          =  self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase_          =  True
																					check_hidden_states_output(A__					,   A__					,   A__		)

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

																					check_hidden_states_output(A__					,   A__					,   A__		)

							def lowercase (		self: Dict		)       -> Union[str, Any]:




														"""simple docstring"""


														def _config_zero_init(_SCREAMING_SNAKE_CASE: Any		):
																					UpperCamelCase_          =  copy.deepcopy(A__		)
																					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(A__					,   A__					,   1e-10		)
																												if isinstance(getattr(A__					,   A__					,   A__		)					,   A__		):
																																			UpperCamelCase_          =  _config_zero_init(getattr(A__					,   A__		)		)
																																			setattr(A__					,   A__					,   A__		)
																					return configs_no_init

														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

														UpperCamelCase_          =  _config_zero_init(A__		)
														for model_class in self.all_model_classes:
																					UpperCamelCase_          =  model_class(config=A__		)
																					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 lowercase (		self: Optional[Any]		)       -> List[str]:




														"""simple docstring"""


														pass




def  lowerCAmelCase_ (							)     ->		Optional[Any]:
							UpperCamelCase_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							return image







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

							@cached_property
							def lowercase (		self: List[Any]		)       -> Union[str, Any]:




														"""simple docstring"""


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



							@slow
							def lowercase (		self: Dict		)       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"		).to(A__		)

														UpperCamelCase_          =  self.default_image_processor
														UpperCamelCase_          =  prepare_img()
														UpperCamelCase_          =  image_processor(images=A__					,   return_tensors="pt"		).to(A__		)

														# forward pass
														with torch.no_grad():
																					UpperCamelCase_          =  model(**A__		)

														# verify the logits
														UpperCamelCase_          =  torch.Size((1, 1000)		)
														self.assertEqual(outputs.logits.shape					,   A__		)

														UpperCamelCase_          =  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]		).to(A__		)
														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   A__					,   atol=1e-4		)		)








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

