





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					,			snake_case					,			snake_case=13					,			snake_case=3					,			snake_case=True					,			snake_case=True					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=224					,			snake_case=1_000					,			snake_case=[3, 3, 6, 4]					,			snake_case=[48, 56, 112, 220]					,			)				->  Union[str, Any]:
      """simple docstring"""





      a__	:						int      		=  parent
      a__	:						Tuple      		=  batch_size
      a__	:						Optional[int]      		=  num_channels
      a__	:						Any      		=  is_training
      a__	:						Any      		=  use_labels
      a__	:						Dict      		=  hidden_dropout_prob
      a__	:						str      		=  attention_probs_dropout_prob
      a__	:						str      		=  num_labels
      a__	:						Optional[int]      		=  image_size
      a__	:						Optional[Any]      		=  layer_depths
      a__	:						Dict      		=  embed_dims





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						str      		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

      a__	:						Optional[Any]      		=  self.get_config()

      return config, pixel_values, labels





   def 	_snake_case ( self      )				->  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=__UpperCamelCase					,			layer_scale_init_value=1E-5					,			)





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  List[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  SwiftFormerModel(config=__UpperCamelCase      )
      model.to(__UpperCamelCase      )
      model.eval()
      a__	:						str      		=  model(__UpperCamelCase      )
      self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.embed_dims[-1], 7, 7)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						int      		=  self.num_labels
      a__	:						Union[str, Any]      		=  SwiftFormerForImageClassification(__UpperCamelCase      )
      model.to(__UpperCamelCase      )
      model.eval()
      a__	:						Union[str, Any]      		=  model(__UpperCamelCase					,			labels=__UpperCamelCase      )
      self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)      )

      a__	:						Optional[int]      		=  SwiftFormerForImageClassification(__UpperCamelCase      )
      model.to(__UpperCamelCase      )
      model.eval()

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





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      ((a__)		,     (a__)		,     (a__))	:						Dict      		=  self.prepare_config_and_inputs()
      a__	:						Tuple      		=  {"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Union[str, Any]     		=				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   _UpperCamelCase :						Tuple     		=				(
       {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCamelCase :						int     		=				False
   _UpperCamelCase :						Any     		=				False
   _UpperCamelCase :						Tuple     		=				False
   _UpperCamelCase :						int     		=				False
   _UpperCamelCase :						Optional[int]     		=				False





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						int      		=  SwiftFormerModelTester(self      )
      a__	:						str      		=  ConfigTester(
          self					,			config_class=__UpperCamelCase					,			has_text_modality=__UpperCamelCase					,			hidden_size=37					,			num_attention_heads=12					,			num_hidden_layers=12					,			)





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   @unittest.skip(reason="SwiftFormer does not use inputs_embeds"      )
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__		,     a__	:						Dict      		=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         a__	:						Union[str, Any]      		=  model_class(__UpperCamelCase      )
         a__	:						List[str]      		=  model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__UpperCamelCase					,			nn.Linear      )      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     a__	:						Any      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

         a__	:						Union[str, Any]      		=  ["pixel_values"]
         self.assertListEqual(arg_names[:1]					,			__UpperCamelCase      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCamelCase      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						List[Any]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase      )





   @slow
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a__	:						List[str]      		=  SwiftFormerModel.from_pretrained(__UpperCamelCase      )
         self.assertIsNotNone(__UpperCamelCase      )





   @unittest.skip(reason="SwiftFormer does not output attentions"      )
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      def check_hidden_states_output(snake_case					,			snake_case					,			snake_case      ):
         a__	:						Dict      		=  model_class(__UpperCamelCase      )
         model.to(__UpperCamelCase      )
         model.eval()

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

         a__	:						Tuple      		=  outputs.hidden_states

         a__	:						Dict      		=  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),
                    ]      )					,			)

      a__		,     a__	:						Union[str, Any]      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

         check_hidden_states_output(__UpperCamelCase					,			__UpperCamelCase					,			__UpperCamelCase      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      def _config_zero_init(snake_case      ):
         a__	:						Union[str, Any]      		=  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      ):
               a__	:						Union[str, Any]      		=  _config_zero_init(getattr(__UpperCamelCase					,			__UpperCamelCase      )      )
               setattr(__UpperCamelCase					,			__UpperCamelCase					,			__UpperCamelCase      )
         return configs_no_init

      a__		,     a__	:						Any      		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__	:						Dict      		=  _config_zero_init(__UpperCamelCase      )
      for model_class in self.all_model_classes:
         a__	:						Union[str, 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 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      pass







def       _A  (      ):
   a__	:						Tuple      		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image







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





   @cached_property
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





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





   @slow
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						int      		=  SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"      ).to(__UpperCamelCase      )

      a__	:						Optional[Any]      		=  self.default_image_processor
      a__	:						List[str]      		=  prepare_img()
      a__	:						Optional[int]      		=  image_processor(images=__UpperCamelCase					,			return_tensors="pt"      ).to(__UpperCamelCase      )

      # forward pass
      with torch.no_grad():
         a__	:						Optional[int]      		=  model(**__UpperCamelCase      )

      # verify the logits
      a__	:						Any      		=  torch.Size((1, 1_000)      )
      self.assertEqual(outputs.logits.shape					,			__UpperCamelCase      )

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








from PIL import Image






def       _A  (      lowerCamelCase							, lowerCamelCase   ):

   def brightness(lowerCamelCase   ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError("level must be between -255.0 (black) and 255.0 (white)"   )
   return img.point(lowerCamelCase   )


if __name__ == "__main__":
  # Load image
  with Image.open("""image_data/lena.jpg""") as img:
    # Change brightness to 100
    SCREAMING_SNAKE_CASE__       :			List[str]			      =						change_brightness(img, 1_0_0)
    brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

