





"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class      UpperCAmelCase_  :



      def __init__(					self					:     Optional[int]					,     A					:     List[Any]					,     A					:     Any=1_3					,     A					:     Any=3_2					,     A					:     List[Any]=2					,     A					:     Optional[int]=3					,     A					:     List[str]=1_6					,     A					:     List[str]=[1, 2, 1]					,     A					:     Dict=[2, 2, 4]					,     A					:     List[Any]=2					,     A					:     Optional[Any]=2.0					,     A					:     Any=True					,     A					:     List[Any]=0.0					,     A					:     int=0.0					,     A					:     str=0.1					,     A					:     List[str]="gelu"					,     A					:     Optional[int]=False					,     A					:     Tuple=True					,     A					:     Any=0.02					,     A					:     List[str]=1e-5					,     A					:     List[str]=True					,     A					:     List[Any]=None					,     A					:     Optional[Any]=True					,     A					:     Optional[Any]=1_0					,     A					:     Tuple=8					,     ):
           _UpperCAmelCase      :     str        =   parent
           _UpperCAmelCase      :     Tuple        =   batch_size
           _UpperCAmelCase      :     Optional[int]        =   image_size
           _UpperCAmelCase      :     str        =   patch_size
           _UpperCAmelCase      :     List[Any]        =   num_channels
           _UpperCAmelCase      :     Tuple        =   embed_dim
           _UpperCAmelCase      :     Union[str, Any]        =   depths
           _UpperCAmelCase      :     Optional[Any]        =   num_heads
           _UpperCAmelCase      :     str        =   window_size
           _UpperCAmelCase      :     str        =   mlp_ratio
           _UpperCAmelCase      :     Union[str, Any]        =   qkv_bias
           _UpperCAmelCase      :     Optional[int]        =   hidden_dropout_prob
           _UpperCAmelCase      :     str        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     Dict        =   drop_path_rate
           _UpperCAmelCase      :     Union[str, Any]        =   hidden_act
           _UpperCAmelCase      :     Optional[int]        =   use_absolute_embeddings
           _UpperCAmelCase      :     List[Any]        =   patch_norm
           _UpperCAmelCase      :     List[Any]        =   layer_norm_eps
           _UpperCAmelCase      :     Any        =   initializer_range
           _UpperCAmelCase      :     Union[str, Any]        =   is_training
           _UpperCAmelCase      :     List[Any]        =   scope
           _UpperCAmelCase      :     Union[str, Any]        =   use_labels
           _UpperCAmelCase      :     List[Any]        =   type_sequence_label_size
           _UpperCAmelCase      :     Dict        =   encoder_stride



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     int        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

           _UpperCAmelCase      :     Union[str, Any]        =   None
           if self.use_labels:
                _UpperCAmelCase      :     int        =   ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)

           _UpperCAmelCase      :     Union[str, Any]        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     Optional[Any]						):
           return SwinvaConfig(
               image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     embed_dim=self.embed_dim					,     depths=self.depths					,     num_heads=self.num_heads					,     window_size=self.window_size					,     mlp_ratio=self.mlp_ratio					,     qkv_bias=self.qkv_bias					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     drop_path_rate=self.drop_path_rate					,     hidden_act=self.hidden_act					,     use_absolute_embeddings=self.use_absolute_embeddings					,     path_norm=self.patch_norm					,     layer_norm_eps=self.layer_norm_eps					,     initializer_range=self.initializer_range					,     encoder_stride=self.encoder_stride					,     )



      def 						snake_case_ (					self					:     Tuple					,     A					:     Any					,     A					:     Union[str, Any]					,     A					:     Dict						):
           _UpperCAmelCase      :     Optional[Any]        =   SwinvaModel(config=__SCREAMING_SNAKE_CASE						)
           model.to(__SCREAMING_SNAKE_CASE						)
           model.eval()
           _UpperCAmelCase      :     Union[str, Any]        =   model(__SCREAMING_SNAKE_CASE						)

           _UpperCAmelCase      :     str        =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
           _UpperCAmelCase      :     Dict        =   int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

           self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, expected_seq_len, expected_dim)						)



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     List[Any]					,     A					:     Any					,     A					:     Dict						):
           _UpperCAmelCase      :     Any        =   SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE						)
           model.to(__SCREAMING_SNAKE_CASE						)
           model.eval()
           _UpperCAmelCase      :     Optional[int]        =   model(__SCREAMING_SNAKE_CASE						)
           self.parent.assertEqual(
               result.logits.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

           # test greyscale images
           _UpperCAmelCase      :     Optional[Any]        =   1
           _UpperCAmelCase      :     List[str]        =   SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE						)
           model.to(__SCREAMING_SNAKE_CASE						)
           model.eval()

           _UpperCAmelCase      :     Optional[int]        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
           _UpperCAmelCase      :     List[str]        =   model(__SCREAMING_SNAKE_CASE						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, 1, self.image_size, self.image_size)						)



      def 						snake_case_ (					self					:     Dict					,     A					:     Dict					,     A					:     Any					,     A					:     Tuple						):
           _UpperCAmelCase      :     List[str]        =   self.type_sequence_label_size
           _UpperCAmelCase      :     Tuple        =   SwinvaForImageClassification(__SCREAMING_SNAKE_CASE						)
           model.to(__SCREAMING_SNAKE_CASE						)
           model.eval()
           _UpperCAmelCase      :     Optional[int]        =   model(__SCREAMING_SNAKE_CASE					,     labels=__SCREAMING_SNAKE_CASE						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     List[str]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   config_and_inputs
           _UpperCAmelCase      :     Optional[Any]        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class      UpperCAmelCase_  (    lowerCAmelCase_	,      lowerCAmelCase_	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			(
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      __SCREAMING_SNAKE_CASE						:       int								=			(
          {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE						:       str								=			False
      __SCREAMING_SNAKE_CASE						:       int								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       Any								=			False



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   SwinvaModelTester(self						)
           _UpperCAmelCase      :     List[Any]        =   ConfigTester(self					,     config_class=__SCREAMING_SNAKE_CASE					,     embed_dim=3_7						)



      def 						snake_case_ (					self					:     Any						):
           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 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)



      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
      def 						snake_case_ (					self					:     List[Any]						):
           pass



      @unittest.skip(reason="Swinv2 does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     Optional[int]						):
           pass



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     Optional[int]        =   model_class(__SCREAMING_SNAKE_CASE						)
                self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)						)
                _UpperCAmelCase      :     Any        =   model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE					,     nn.Linear						)						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase      :     Any        =   ["pixel_values"]
                self.assertListEqual(arg_names[:1]					,     __SCREAMING_SNAKE_CASE						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase      :     Any        =   True

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     int        =   True
                _UpperCAmelCase      :     Dict        =   False
                _UpperCAmelCase      :     Union[str, Any]        =   True
                _UpperCAmelCase      :     Tuple        =   model_class(__SCREAMING_SNAKE_CASE						)
                model.to(__SCREAMING_SNAKE_CASE						)
                model.eval()
                with torch.no_grad():
                     _UpperCAmelCase      :     str        =   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE						)						)
                _UpperCAmelCase      :     str        =   outputs.attentions
                _UpperCAmelCase      :     Tuple        =   len(self.model_tester.depths						)
                self.assertEqual(len(__SCREAMING_SNAKE_CASE						)					,     __SCREAMING_SNAKE_CASE						)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                _UpperCAmelCase      :     Tuple        =   True
                _UpperCAmelCase      :     Tuple        =   config.window_size**2
                _UpperCAmelCase      :     Optional[Any]        =   model_class(__SCREAMING_SNAKE_CASE						)
                model.to(__SCREAMING_SNAKE_CASE						)
                model.eval()
                with torch.no_grad():
                     _UpperCAmelCase      :     str        =   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE						)						)
                _UpperCAmelCase      :     Dict        =   outputs.attentions
                self.assertEqual(len(__SCREAMING_SNAKE_CASE						)					,     __SCREAMING_SNAKE_CASE						)

                self.assertListEqual(
                    list(attentions[0].shape[-3:]						)					,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,     )
                _UpperCAmelCase      :     Optional[int]        =   len(__SCREAMING_SNAKE_CASE						)

                # Check attention is always last and order is fine
                _UpperCAmelCase      :     str        =   True
                _UpperCAmelCase      :     Dict        =   True
                _UpperCAmelCase      :     Optional[Any]        =   model_class(__SCREAMING_SNAKE_CASE						)
                model.to(__SCREAMING_SNAKE_CASE						)
                model.eval()
                with torch.no_grad():
                     _UpperCAmelCase      :     int        =   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE						)						)

                if hasattr(self.model_tester					,     "num_hidden_states_types"						):
                     _UpperCAmelCase      :     Tuple        =   self.model_tester.num_hidden_states_types
                else:
                     # also another +1 for reshaped_hidden_states
                     _UpperCAmelCase      :     List[Any]        =   2
                self.assertEqual(out_len + added_hidden_states					,     len(__SCREAMING_SNAKE_CASE						)						)

                _UpperCAmelCase      :     Tuple        =   outputs.attentions

                self.assertEqual(len(__SCREAMING_SNAKE_CASE						)					,     __SCREAMING_SNAKE_CASE						)

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]						)					,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,     )



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     Optional[Any]					,     A					:     Union[str, Any]					,     A					:     str					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[str]        =   model_class(__SCREAMING_SNAKE_CASE						)
           model.to(__SCREAMING_SNAKE_CASE						)
           model.eval()

           with torch.no_grad():
                _UpperCAmelCase      :     List[Any]        =   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE						)						)

           _UpperCAmelCase      :     List[Any]        =   outputs.hidden_states

           _UpperCAmelCase      :     str        =   getattr(
               self.model_tester					,     "expected_num_hidden_layers"					,     len(self.model_tester.depths						) + 1						)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE						)					,     __SCREAMING_SNAKE_CASE						)

           # Swinv2 has a different seq_length
           _UpperCAmelCase      :     Any        =   (
               config.patch_size
               if isinstance(config.patch_size					,     collections.abc.Iterable						)
               else (config.patch_size, config.patch_size)
           )

           _UpperCAmelCase      :     Optional[Any]        =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]						)					,     [num_patches, self.model_tester.embed_dim]					,     )

           _UpperCAmelCase      :     List[Any]        =   outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE						)					,     __SCREAMING_SNAKE_CASE						)

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   reshaped_hidden_states[0].shape
           _UpperCAmelCase      :     List[str]        =   (
               reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     height * width						).permute(0					,     2					,     1						)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]						)					,     [num_patches, self.model_tester.embed_dim]					,     )



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase      :     Tuple        =   (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size					,     collections.abc.Iterable						)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     Any        =   True
                self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,     __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      :     Optional[int]        =   True

                self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase      :     Tuple        =   3

           _UpperCAmelCase      :     Optional[int]        =   (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size					,     collections.abc.Iterable						)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           _UpperCAmelCase      :     Optional[Any]        =   (
               config.patch_size
               if isinstance(config.patch_size					,     collections.abc.Iterable						)
               else (config.patch_size, config.patch_size)
           )

           _UpperCAmelCase      :     int        =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           _UpperCAmelCase      :     str        =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     int        =   True
                self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     (padded_height, padded_width)						)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase      :     Dict        =   True
                self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     (padded_height, padded_width)						)



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Optional[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)



      @slow
      def 						snake_case_ (					self					:     str						):

           for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase      :     Tuple        =   SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase      :     List[str]        =   _config_zero_init(__SCREAMING_SNAKE_CASE						)
           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     Union[str, Any]        =   model_class(config=__SCREAMING_SNAKE_CASE						)
                for name, param in model.named_parameters():
                     if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()					,     [0.0, 1.0]					,     msg=f'Parameter {name} of model {model_class} seems not properly initialized'					,     )







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



      @cached_property
      def 						snake_case_ (					self					:     str						):
           return (
               AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"						)
               if is_vision_available()
               else None
           )



      @slow
      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"						).to(
               __SCREAMING_SNAKE_CASE						)
           _UpperCAmelCase      :     Optional[Any]        =   self.default_image_processor

           _UpperCAmelCase      :     Optional[Any]        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
           _UpperCAmelCase      :     List[Any]        =   image_processor(images=__SCREAMING_SNAKE_CASE					,     return_tensors="pt"						).to(__SCREAMING_SNAKE_CASE						)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase      :     List[Any]        =   model(**__SCREAMING_SNAKE_CASE						)

           # verify the logits
           _UpperCAmelCase      :     int        =   torch.Size((1, 1_0_0_0)						)
           self.assertEqual(outputs.logits.shape					,     __SCREAMING_SNAKE_CASE						)
           _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor([-0.3_947, -0.4_306, 0.0_026]						).to(__SCREAMING_SNAKE_CASE						)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     __SCREAMING_SNAKE_CASE					,     atol=1e-4						)						)





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict=28_123    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   [1] * (limit + 1)

     for i in range(2						,  int(limit**0.5    ) + 1    ):
          sum_divs[i * i] += i
          for k in range(i + 1						,  limit // i + 1    ):
               sum_divs[k * i] += k + i

     _UpperCAmelCase      :     Any        =   set()
     _UpperCAmelCase      :     List[str]        =   0

     for n in range(1						,  limit + 1    ):
          if sum_divs[n] > n:
               abundants.add(SCREAMING_SNAKE_CASE__    )

          if not any((n - a in abundants) for a in abundants    ):
               res += n

     return res


if __name__ == "__main__":
 print(solution())
