'''simple docstring'''


def    _lowerCamelCase       ( lowerCamelCase_   :				list  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = len(lowerCamelCase_  )

     # Set the gap value to be decreased by a factor of 1.3
     # after each iteration
     UpperCAmelCase_				:  Optional[Any]  = 1.3

     # Continue sorting until the gap is 1
     while gap > 1:
          # Decrease the gap value
          UpperCAmelCase_				:  Optional[Any]  = int(gap / shrink  )

          # Sort the elements using insertion sort
          for i in range(lowerCamelCase_      ,	len(lowerCamelCase_  )  ):
               UpperCAmelCase_				:  Optional[int]  = collection[i]
               UpperCAmelCase_				:  Tuple  = i
               while j >= gap and collection[j - gap] > temp:
                    UpperCAmelCase_				:  Tuple  = collection[j - gap]
                    j -= gap
               UpperCAmelCase_				:  Optional[int]  = temp

     return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=3 ,						snake_case_=3_2 ,						snake_case_=3 ,						snake_case_=1_0 ,						snake_case_=[1_0, 2_0, 3_0, 4_0] ,						snake_case_=[1, 1, 2, 1] ,						snake_case_=True ,						snake_case_=True ,						snake_case_="relu" ,						snake_case_=3 ,						snake_case_=None ,						snake_case_=["stage2", "stage3", "stage4"] ,						snake_case_=[2, 3, 4] ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = parent
       UpperCAmelCase_				:  int  = batch_size
       UpperCAmelCase_				:  Optional[int]  = image_size
       UpperCAmelCase_				:  List[str]  = num_channels
       UpperCAmelCase_				:  Dict  = embeddings_size
       UpperCAmelCase_				:  List[str]  = hidden_sizes
       UpperCAmelCase_				:  int  = depths
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_labels
       UpperCAmelCase_				:  Union[str, Any]  = hidden_act
       UpperCAmelCase_				:  Optional[Any]  = num_labels
       UpperCAmelCase_				:  List[Any]  = scope
       UpperCAmelCase_				:  Tuple  = len(snake_case_		)
       UpperCAmelCase_				:  Dict  = out_features
       UpperCAmelCase_				:  Dict  = out_indices


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

       UpperCAmelCase_				:  List[str]  = self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return ResNetConfig(
           num_channels=self.num_channels ,						embeddings_size=self.embeddings_size ,						hidden_sizes=self.hidden_sizes ,						depths=self.depths ,						hidden_act=self.hidden_act ,						num_labels=self.num_labels ,						out_features=self.out_features ,						out_indices=self.out_indices ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.num_labels
       UpperCAmelCase_				:  Optional[Any]  = ResNetForImageClassification(snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Optional[int]  = model(snake_case_ ,						labels=snake_case_		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Dict  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[1], 4, 4]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(model.channels ,						config.hidden_sizes[1:]		)

       # verify backbone works with out_features=None
       UpperCAmelCase_				:  Tuple  = None
       UpperCAmelCase_				:  Tuple  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						1		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						1		)
       self.parent.assertListEqual(model.channels ,						[config.hidden_sizes[-1]]		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.prepare_config_and_inputs()
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = config_and_inputs
       UpperCAmelCase_				:  str  = {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase_						:List[str]		       =						(
      {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase_						:int		       =						True
  lowerCamelCase_						:Optional[Any]		       =						False
  lowerCamelCase_						:Union[str, Any]		       =						False
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:str		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModelTester(self		)
       UpperCAmelCase_				:  int  = ConfigTester(self ,						config_class=snake_case_ ,						has_text_modality=snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return


  @unittest.skip(reason='ResNet does not use inputs_embeds'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  @unittest.skip(reason='ResNet does not support input and output embeddings'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

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

            UpperCAmelCase_				:  List[str]  = ['pixel_values']
            self.assertListEqual(arg_names[:1] ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  List[Any]  = model_class(config=snake_case_		)
            for name, module in model.named_modules():
                 if isinstance(snake_case_ ,						(nn.BatchNormad, nn.GroupNorm)		):
                      self.assertTrue(
                          torch.all(module.weight == 1		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)
                      self.assertTrue(
                          torch.all(module.bias == 0		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  int  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            with torch.no_grad():
                 UpperCAmelCase_				:  str  = model(**self._prepare_for_class(snake_case_ ,						snake_case_		)		)

            UpperCAmelCase_				:  Union[str, Any]  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_				:  str  = self.model_tester.num_stages
            self.assertEqual(len(snake_case_		) ,						expected_num_stages + 1		)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]		) ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,						)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Optional[Any]  = ['basic', 'bottleneck']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 UpperCAmelCase_				:  Any  = layer_type
                 UpperCAmelCase_				:  str  = True
                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)

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

                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)


  @unittest.skip(reason='ResNet does not use feedforward chunking'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  str  = ResNetModel.from_pretrained(snake_case_		)
            self.assertIsNotNone(snake_case_		)







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
     return image






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







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (
           AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
           if is_vision_available()
           else None
       )






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(snake_case_		)

       UpperCAmelCase_				:  str  = self.default_image_processor
       UpperCAmelCase_				:  Union[str, Any]  = prepare_img()
       UpperCAmelCase_				:  Union[str, Any]  = image_processor(images=snake_case_ ,						return_tensors='pt'		).to(snake_case_		)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_				:  Union[str, Any]  = model(**snake_case_		)

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

       UpperCAmelCase_				:  Optional[int]  = torch.tensor([-11.10_69, -9.78_77, -8.37_77]		).to(snake_case_		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						snake_case_ ,						atol=1E-4		)		)







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						(ResNetBackbone,) if is_torch_available() else ()
  lowerCamelCase_						:List[str]		       =						False
  lowerCamelCase_						:Optional[Any]		       =						ResNetConfig


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetModelTester(self		)






