
"""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 						a__		:
   def __init__(     self      ,       _a      ,       _a=13      ,       _a=32      ,       _a=2      ,       _a=3      ,       _a=16      ,       _a=[1, 2, 1]      ,       _a=[2, 2, 4]      ,       _a=2      ,       _a=2.0      ,       _a=True      ,       _a=0.0      ,       _a=0.0      ,       _a=0.1      ,       _a="gelu"      ,       _a=False      ,       _a=True      ,       _a=0.0_2      ,       _a=1E-5      ,       _a=True      ,       _a=None      ,       _a=True      ,       _a=10      ,       _a=8      ,       ):
          lowercase       :							Union[str, Any]									=				parent
          lowercase       :							List[str]									=				batch_size
          lowercase       :							str									=				image_size
          lowercase       :							List[Any]									=				patch_size
          lowercase       :							str									=				num_channels
          lowercase       :							List[str]									=				embed_dim
          lowercase       :							List[Any]									=				depths
          lowercase       :							Optional[int]									=				num_heads
          lowercase       :							List[Any]									=				window_size
          lowercase       :							Any									=				mlp_ratio
          lowercase       :							Any									=				qkv_bias
          lowercase       :							int									=				hidden_dropout_prob
          lowercase       :							Tuple									=				attention_probs_dropout_prob
          lowercase       :							Union[str, Any]									=				drop_path_rate
          lowercase       :							Optional[Any]									=				hidden_act
          lowercase       :							List[str]									=				use_absolute_embeddings
          lowercase       :							List[Any]									=				patch_norm
          lowercase       :							Optional[Any]									=				layer_norm_eps
          lowercase       :							Union[str, Any]									=				initializer_range
          lowercase       :							Union[str, Any]									=				is_training
          lowercase       :							Any									=				scope
          lowercase       :							Tuple									=				use_labels
          lowercase       :							Union[str, Any]									=				type_sequence_label_size
          lowercase       :							Tuple									=				encoder_stride
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          lowercase       :							Tuple									=				None
          if self.use_labels:
                 lowercase       :							List[Any]									=				ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)

          lowercase       :							str									=				self.get_config()

          return config, pixel_values, labels
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							List[Any]									=				SwinvaModel(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							int									=				model(_a							)

          lowercase       :							List[Any]									=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
          lowercase       :							Optional[int]									=				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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Optional[int]									=				SwinvaForMaskedImageModeling(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							Any									=				model(_a							)
          self.parent.assertEqual(
              result.logits.shape      ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

          # test greyscale images
          lowercase       :							Union[str, Any]									=				1
          lowercase       :							Any									=				SwinvaForMaskedImageModeling(_a							)
          model.to(_a							)
          model.eval()

          lowercase       :							Dict									=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          lowercase       :							List[str]									=				model(_a							)
          self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, 1, self.image_size, self.image_size)							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				self.type_sequence_label_size
          lowercase       :							int									=				SwinvaForImageClassification(_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							Union[str, Any]									=				model(_a      ,       labels=_a							)
          self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)

   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.prepare_config_and_inputs()
          lowercase						,       lowercase						,       lowercase       :							Optional[Any]									=				config_and_inputs
          lowercase       :							str									=				{"pixel_values": pixel_values}
          return config, inputs_dict


@require_torch
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							(
       (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
   )
   __lowerCAmelCase				=							(
       {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				SwinvaModelTester(self							)
          lowercase       :							Optional[Any]									=				ConfigTester(self      ,       config_class=_a      ,       embed_dim=37							)
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a							)
   @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."							)
   def      __magic_name__     (     self							):
          pass
   @unittest.skip(reason="Swinv2 does not use inputs_embeds"							)
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Dict									=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase       :							List[Any]									=				model_class(_a							)
                 self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)							)
                 lowercase       :							Optional[Any]									=				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_a      ,       nn.Linear							)							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Tuple									=				self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase       :							int									=				["pixel_values"]
                 self.assertListEqual(arg_names[:1]      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							List[str]									=				self.model_tester.prepare_config_and_inputs_for_common()
          lowercase       :							Optional[Any]									=				True

          for model_class in self.all_model_classes:
                 lowercase       :							Dict									=				True
                 lowercase       :							List[Any]									=				False
                 lowercase       :							Optional[Any]									=				True
                 lowercase       :							Dict									=				model_class(_a							)
                 model.to(_a							)
                 model.eval()
                 with torch.no_grad():
                        lowercase       :							int									=				model(**self._prepare_for_class(_a      ,       _a							)							)
                 lowercase       :							int									=				outputs.attentions
                 lowercase       :							Any									=				len(self.model_tester.depths							)
                 self.assertEqual(len(_a							)      ,       _a							)

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

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]							)      ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,       )
                 lowercase       :							List[str]									=				len(_a							)

                 # Check attention is always last and order is fine
                 lowercase       :							Optional[Any]									=				True
                 lowercase       :							Any									=				True
                 lowercase       :							Tuple									=				model_class(_a							)
                 model.to(_a							)
                 model.eval()
                 with torch.no_grad():
                        lowercase       :							Any									=				model(**self._prepare_for_class(_a      ,       _a							)							)

                 if hasattr(self.model_tester      ,       "num_hidden_states_types"							):
                        lowercase       :							Any									=				self.model_tester.num_hidden_states_types
                 else:
                        # also another +1 for reshaped_hidden_states
                        lowercase       :							Dict									=				2
                 self.assertEqual(out_len + added_hidden_states      ,       len(_a							)							)

                 lowercase       :							Union[str, Any]									=				outputs.attentions

                 self.assertEqual(len(_a							)      ,       _a							)

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]							)      ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,       )
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          lowercase       :							str									=				model_class(_a							)
          model.to(_a							)
          model.eval()

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

          lowercase       :							Tuple									=				outputs.hidden_states

          lowercase       :							Optional[Any]									=				getattr(
              self.model_tester      ,       "expected_num_hidden_layers"      ,       len(self.model_tester.depths							) + 1							)
          self.assertEqual(len(_a							)      ,       _a							)

          # Swinv2 has a different seq_length
          lowercase       :							Union[str, Any]									=				(
              config.patch_size
              if isinstance(config.patch_size      ,       collections.abc.Iterable							)
              else (config.patch_size, config.patch_size)
          )

          lowercase       :							str									=				(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]      ,       )

          lowercase       :							List[Any]									=				outputs.reshaped_hidden_states
          self.assertEqual(len(_a							)      ,       _a							)

          lowercase						,       lowercase						,       lowercase						,       lowercase       :							Tuple									=				reshaped_hidden_states[0].shape
          lowercase       :							List[str]									=				(
              reshaped_hidden_states[0].view(_a      ,       _a      ,       height * width							).permute(0      ,       2      ,       1							)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]							)      ,       [num_patches, self.model_tester.embed_dim]      ,       )
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self.model_tester.prepare_config_and_inputs_for_common()

          lowercase       :							Union[str, Any]									=				(
              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:
                 lowercase       :							str									=				True
                 self.check_hidden_states_output(_a      ,       _a      ,       _a      ,       _a							)

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

                 self.check_hidden_states_output(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Dict									=				self.model_tester.prepare_config_and_inputs_for_common()
          lowercase       :							Optional[int]									=				3

          lowercase       :							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)
          )
          lowercase       :							Optional[Any]									=				(
              config.patch_size
              if isinstance(config.patch_size      ,       collections.abc.Iterable							)
              else (config.patch_size, config.patch_size)
          )

          lowercase       :							Optional[Any]									=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          lowercase       :							Any									=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
                 lowercase       :							Union[str, Any]									=				True
                 self.check_hidden_states_output(_a      ,       _a      ,       _a      ,       (padded_height, padded_width)							)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase       :							Dict									=				True
                 self.check_hidden_states_output(_a      ,       _a      ,       _a      ,       (padded_height, padded_width)							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a							)
   @slow
   def      __magic_name__     (     self							):
          for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase       :							List[Any]									=				SwinvaModel.from_pretrained(_a							)
                 self.assertIsNotNone(_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Tuple									=				self.model_tester.prepare_config_and_inputs_for_common()

          lowercase       :							Optional[Any]									=				_config_zero_init(_a							)
          for model_class in self.all_model_classes:
                 lowercase       :							List[str]									=				model_class(config=_a							)
                 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 						a__		( unittest.TestCase		):
   @cached_property
   def      __magic_name__     (     self							):
          return (
              AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"							)
              if is_vision_available()
              else None
          )

   @slow
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"							).to(
              _a							)
          lowercase       :							List[str]									=				self.default_image_processor

          lowercase       :							int									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
          lowercase       :							Union[str, Any]									=				image_processor(images=_a      ,       return_tensors="pt"							).to(_a							)

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

          # verify the logits
          lowercase       :							Tuple									=				torch.Size((1, 1_000)							)
          self.assertEqual(outputs.logits.shape      ,       _a							)
          lowercase       :							int									=				torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]							).to(_a							)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       _a      ,       atol=1E-4							)							)




"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


