

"""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      __lowercase			:

  def __init__(  self		:			Tuple					,A		:			Union[str, Any]					,A		:			Union[str, Any]=13					,A		:			Optional[Any]=3					,A		:			int=True					,A		:			List[Any]=True					,A		:			Union[str, Any]=0.1					,A		:			Any=0.1					,A		:			Optional[int]=224					,A		:			int=1_000					,A		:			Optional[int]=[3, 3, 6, 4]					,A		:			Union[str, Any]=[48, 56, 112, 220]					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					parent
   UpperCAmelCase__					:  Any					   =					batch_size
   UpperCAmelCase__					:  Dict					   =					num_channels
   UpperCAmelCase__					:  Optional[Any]					   =					is_training
   UpperCAmelCase__					:  str					   =					use_labels
   UpperCAmelCase__					:  str					   =					hidden_dropout_prob
   UpperCAmelCase__					:  Tuple					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  List[Any]					   =					num_labels
   UpperCAmelCase__					:  Optional[int]					   =					image_size
   UpperCAmelCase__					:  List[str]					   =					layer_depths
   UpperCAmelCase__					:  Tuple					   =					embed_dims

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   UpperCAmelCase__					:  Optional[int]					   =					self.get_config()

   return config, pixel_values, labels

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''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					,A		:			str					,A		:			Tuple					,A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					SwiftFormerModel(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Dict					   =					model(A    )
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.embed_dims[-1], 7, 7)    )

  def    __lowercase       (  self		:			List[Any]					,A		:			Optional[Any]					,A		:			Any					,A		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.num_labels
   UpperCAmelCase__					:  Tuple					   =					SwiftFormerForImageClassification(A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  List[Any]					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)    )

   UpperCAmelCase__					:  Union[str, Any]					   =					SwiftFormerForImageClassification(A    )
   model.to(A    )
   model.eval()

   UpperCAmelCase__					:  Tuple					   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  Union[str, Any]					   =					model(A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)    )




  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   ((UpperCAmelCase__)   ,      (UpperCAmelCase__)   ,      (UpperCAmelCase__))					:  Optional[Any]					   =					self.prepare_config_and_inputs()
   UpperCAmelCase__					:  str					   =					{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  snake_case_				       =       (
      {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					SwiftFormerModelTester(self    )
   UpperCAmelCase__					:  List[Any]					   =					ConfigTester(
       self					,config_class=A					,has_text_modality=A					,hidden_size=37					,num_attention_heads=12					,num_hidden_layers=12					,)

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""    )
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  List[str]					   =					model_class(A    )
    UpperCAmelCase__					:  int					   =					model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(A					,nn.Linear    )    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.model_tester.prepare_config_and_inputs_for_common()

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

    UpperCAmelCase__					:  Any					   =					["""pixel_values"""]
    self.assertListEqual(arg_names[:1]					,A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*A    )

  @slow
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__					:  Dict					   =					SwiftFormerModel.from_pretrained(A    )
    self.assertIsNotNone(A    )

  @unittest.skip(reason="""SwiftFormer does not output attentions"""    )
  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   def check_hidden_states_output(A		:			Optional[int]					,A		:			Any					,A		:			Union[str, Any]    ):
    UpperCAmelCase__					:  Any					   =					model_class(A    )
    model.to(A    )
    model.eval()

    with torch.no_grad():
     UpperCAmelCase__					:  str					   =					model(**self._prepare_for_class(A					,A    )    )

    UpperCAmelCase__					:  List[Any]					   =					outputs.hidden_states

    UpperCAmelCase__					:  int					   =					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__   ,      UpperCAmelCase__					:  Dict					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  int					   =					True
    check_hidden_states_output(A					,A					,A    )

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

    check_hidden_states_output(A					,A					,A    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   def _config_zero_init(A		:			Any    ):
    UpperCAmelCase__					:  str					   =					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__					:  int					   =					_config_zero_init(getattr(A					,A    )    )
      setattr(A					,A					,A    )
    return configs_no_init

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

   UpperCAmelCase__					:  Any					   =					_config_zero_init(A    )
   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  Optional[int]					   =					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[int]    ):






   '''simple docstring'''
   pass







def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
 return image







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

  @cached_property
  def    __lowercase       (  self		:			int    ):






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




  @slow
  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""    ).to(A    )

   UpperCAmelCase__					:  List[Any]					   =					self.default_image_processor
   UpperCAmelCase__					:  List[str]					   =					prepare_img()
   UpperCAmelCase__					:  List[Any]					   =					image_processor(images=A					,return_tensors="""pt"""    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  Optional[Any]					   =					model(**A    )

   # verify the logits
   UpperCAmelCase__					:  int					   =					torch.Size((1, 1_000)    )
   self.assertEqual(outputs.logits.shape					,A    )

   UpperCAmelCase__					:  List[str]					   =					torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]    ).to(A    )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]					,A					,atol=1e-4    )    )








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase =				logging.get_logger(__name__)







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """timm_backbone"""

  def __init__(  self		:			List[str]					,A		:			Any=None					,A		:			List[Any]=3					,A		:			Any=True					,A		:			Union[str, Any]=True					,A		:			List[Any]=None					,**A		:			Optional[int]					,):






   '''simple docstring'''
   super().__init__(**A    )
   UpperCAmelCase__					:  Optional[int]					   =					backbone
   UpperCAmelCase__					:  Dict					   =					num_channels
   UpperCAmelCase__					:  Optional[int]					   =					features_only
   UpperCAmelCase__					:  Tuple					   =					use_pretrained_backbone
   UpperCAmelCase__					:  str					   =					True
   UpperCAmelCase__					:  List[str]					   =					out_indices if out_indices is not None else (-1,)





