


"""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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=32 ,     lowerCamelCase__=2 ,     lowerCamelCase__=3 ,     lowerCamelCase__=16 ,     lowerCamelCase__=[1, 2, 1] ,     lowerCamelCase__=[2, 2, 4] ,     lowerCamelCase__=2 ,     lowerCamelCase__=2.0 ,     lowerCamelCase__=True ,     lowerCamelCase__=0.0 ,     lowerCamelCase__=0.0 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__="gelu" ,     lowerCamelCase__=False ,     lowerCamelCase__=True ,     lowerCamelCase__=0.02 ,     lowerCamelCase__=1E-5 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     lowerCamelCase__=10 ,     lowerCamelCase__=8 ,     )      ->							List[str]:
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       List[Any]				=						batch_size
    lowercase__       :       List[Any]				=						image_size
    lowercase__       :       Union[str, Any]				=						patch_size
    lowercase__       :       List[Any]				=						num_channels
    lowercase__       :       Tuple				=						embed_dim
    lowercase__       :       Any				=						depths
    lowercase__       :       int				=						num_heads
    lowercase__       :       Any				=						window_size
    lowercase__       :       Tuple				=						mlp_ratio
    lowercase__       :       List[Any]				=						qkv_bias
    lowercase__       :       Optional[Any]				=						hidden_dropout_prob
    lowercase__       :       Optional[Any]				=						attention_probs_dropout_prob
    lowercase__       :       List[Any]				=						drop_path_rate
    lowercase__       :       Union[str, Any]				=						hidden_act
    lowercase__       :       Optional[int]				=						use_absolute_embeddings
    lowercase__       :       int				=						patch_norm
    lowercase__       :       Optional[Any]				=						layer_norm_eps
    lowercase__       :       str				=						initializer_range
    lowercase__       :       List[str]				=						is_training
    lowercase__       :       str				=						scope
    lowercase__       :       Any				=						use_labels
    lowercase__       :       List[Any]				=						type_sequence_label_size
    lowercase__       :       Optional[Any]				=						encoder_stride


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       str				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

    lowercase__       :       str				=						self.get_config()

    return config, pixel_values, labels


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       str				=						SwinvaModel(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       Any				=						model(lowerCamelCase__						)

    lowercase__       :       str				=						((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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Tuple:
    lowercase__       :       Tuple				=						SwinvaForMaskedImageModeling(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       Union[str, Any]				=						model(lowerCamelCase__						)
    self.parent.assertEqual(
        result.logits.shape ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

    # test greyscale images
    lowercase__       :       Optional[int]				=						1
    lowercase__       :       Any				=						SwinvaForMaskedImageModeling(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()

    lowercase__       :       List[str]				=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
    lowercase__       :       Union[str, Any]				=						model(lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, 1, self.image_size, self.image_size)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       Tuple				=						self.type_sequence_label_size
    lowercase__       :       Dict				=						SwinvaForImageClassification(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       List[Any]				=						model(lowerCamelCase__ ,     labels=lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)						)







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

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Dict									=    (
       (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
   )
   _a   :       List[str]									=    (
       {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
       if is_torch_available()
       else {}
   )

   _a   :       Dict									=    False
   _a   :       Any									=    False
   _a   :       List[Any]									=    False
   _a   :       Union[str, Any]									=    False


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       str				=						SwinvaModelTester(self						)
    lowercase__       :       Any				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     embed_dim=37						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    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						)      ->							int:
    lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass


   @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""						)
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    pass


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__	,       lowercase__       :       str				=						self.model_tester.prepare_config_and_inputs_for_common()

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


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__	,       lowercase__       :       List[str]				=						self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__       :       int				=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__	,       lowercase__       :       str				=						self.model_tester.prepare_config_and_inputs_for_common()
    lowercase__       :       str				=						True

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

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

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

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

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

     lowercase__       :       Optional[Any]				=						outputs.attentions

     self.assertEqual(len(lowerCamelCase__						) ,     lowerCamelCase__						)

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       int				=						model_class(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()

    with torch.no_grad():
     lowercase__       :       Union[str, Any]				=						model(**self._prepare_for_class(lowerCamelCase__ ,     lowerCamelCase__						)						)

    lowercase__       :       List[str]				=						outputs.hidden_states

    lowercase__       :       Optional[int]				=						getattr(
        self.model_tester ,     """expected_num_hidden_layers""" ,     len(self.model_tester.depths						) + 1						)
    self.assertEqual(len(lowerCamelCase__						) ,     lowerCamelCase__						)

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

    lowercase__       :       List[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] ,     )

    lowercase__       :       List[Any]				=						outputs.reshaped_hidden_states
    self.assertEqual(len(lowerCamelCase__						) ,     lowerCamelCase__						)

    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       int				=						reshaped_hidden_states[0].shape
    lowercase__       :       Optional[int]				=						(
        reshaped_hidden_states[0].view(lowerCamelCase__ ,     lowerCamelCase__ ,     height * width						).permute(0 ,     2 ,     1						)
    )
    self.assertListEqual(
        list(reshaped_hidden_states.shape[-2:]						) ,     [num_patches, self.model_tester.embed_dim] ,     )


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__	,       lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs_for_common()

    lowercase__       :       Dict				=						(
        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__       :       int				=						True
     self.check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)

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

     self.check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__	,       lowercase__       :       Any				=						self.model_tester.prepare_config_and_inputs_for_common()
    lowercase__       :       Union[str, Any]				=						3

    lowercase__       :       List[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)
    )
    lowercase__       :       Tuple				=						(
        config.patch_size
        if isinstance(config.patch_size ,     collections.abc.Iterable						)
        else (config.patch_size, config.patch_size)
    )

    lowercase__       :       Union[str, Any]				=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    lowercase__       :       Optional[int]				=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
     lowercase__       :       str				=						True
     self.check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     (padded_height, padded_width)						)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowercase__       :       List[str]				=						True
     self.check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     (padded_height, padded_width)						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Dict				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       str				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__						)


   @slow
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Optional[Any]				=						SwinvaModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__	,       lowercase__       :       Optional[int]				=						self.model_tester.prepare_config_and_inputs_for_common()

    lowercase__       :       Union[str, Any]				=						_config_zero_init(lowerCamelCase__						)
    for model_class in self.all_model_classes:
     lowercase__       :       List[str]				=						model_class(config=lowerCamelCase__						)
     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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



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







   @slow
   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""						).to(
        lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						self.default_image_processor

    lowercase__       :       Tuple				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    lowercase__       :       int				=						image_processor(images=lowerCamelCase__ ,     return_tensors="""pt"""						).to(lowerCamelCase__						)

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

    # verify the logits
    lowercase__       :       List[str]				=						torch.Size((1, 1000)						)
    self.assertEqual(outputs.logits.shape ,     lowerCamelCase__						)
    lowercase__       :       Dict				=						torch.tensor([-0.3947, -0.4306, 0.0026]						).to(lowerCamelCase__						)
    self.assertTrue(torch.allclose(outputs.logits[0, :3] ,     lowerCamelCase__ ,     atol=1E-4						)						)



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff