


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_	:
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=13 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=[1, 2, 1] ,					__SCREAMING_SNAKE_CASE=[2, 2, 4] ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=2.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-5 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=10 ,					__SCREAMING_SNAKE_CASE=8 ,					):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		parent
              UpperCamelCase    :	Optional[int]   	=		batch_size
              UpperCamelCase    :	str   	=		image_size
              UpperCamelCase    :	Tuple   	=		patch_size
              UpperCamelCase    :	Any   	=		num_channels
              UpperCamelCase    :	Optional[Any]   	=		embed_dim
              UpperCamelCase    :	Any   	=		depths
              UpperCamelCase    :	Optional[Any]   	=		num_heads
              UpperCamelCase    :	Tuple   	=		window_size
              UpperCamelCase    :	List[Any]   	=		mlp_ratio
              UpperCamelCase    :	Union[str, Any]   	=		qkv_bias
              UpperCamelCase    :	Any   	=		hidden_dropout_prob
              UpperCamelCase    :	str   	=		attention_probs_dropout_prob
              UpperCamelCase    :	List[Any]   	=		drop_path_rate
              UpperCamelCase    :	str   	=		hidden_act
              UpperCamelCase    :	Optional[int]   	=		use_absolute_embeddings
              UpperCamelCase    :	List[Any]   	=		patch_norm
              UpperCamelCase    :	List[Any]   	=		layer_norm_eps
              UpperCamelCase    :	Any   	=		initializer_range
              UpperCamelCase    :	Optional[int]   	=		is_training
              UpperCamelCase    :	Dict   	=		scope
              UpperCamelCase    :	Optional[int]   	=		use_labels
              UpperCamelCase    :	Optional[Any]   	=		type_sequence_label_size
              UpperCamelCase    :	List[Any]   	=		encoder_stride






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

              UpperCamelCase    :	List[str]   	=		None
              if self.use_labels:
                     UpperCamelCase    :	int   	=		ids_tensor([self.batch_size] ,					self.type_sequence_label_size						)

              UpperCamelCase    :	Dict   	=		self.get_config()

              return config, pixel_values, labels






       def  _lowercase   (     self						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		SwinvaModel(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Optional[int]   	=		model(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	str   	=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
              UpperCamelCase    :	List[Any]   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Dict   	=		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    :	List[str]   	=		1
              UpperCamelCase    :	List[str]   	=		SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()

              UpperCamelCase    :	Any   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.type_sequence_label_size
              UpperCamelCase    :	Union[str, Any]   	=		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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.prepare_config_and_inputs()
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		config_and_inputs
              UpperCamelCase    :	Dict   	=		{'''pixel_values''': pixel_values}
              return config, inputs_dict




@require_torch
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       __UpperCamelCase      :     Dict							=					(
           {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase      :     Optional[int]							=					False
       __UpperCamelCase      :     Optional[int]							=					False
       __UpperCamelCase      :     int							=					False
       __UpperCamelCase      :     Union[str, Any]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		SwinvaModelTester(self						)
              UpperCamelCase    :	Any   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					embed_dim=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		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  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip(reason='''Swinv2 does not use inputs_embeds'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs_for_common()

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Tuple   	=		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    :	Optional[Any]   	=		[*signature.parameters.keys()]

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Any   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	Tuple   	=		True

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Any   	=		True
                     UpperCamelCase    :	Union[str, Any]   	=		False
                     UpperCamelCase    :	List[str]   	=		True
                     UpperCamelCase    :	int   	=		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    :	List[Any]   	=		outputs.attentions
                     UpperCamelCase    :	List[str]   	=		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    :	int   	=		config.window_size**2
                     UpperCamelCase    :	Dict   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     model.to(__SCREAMING_SNAKE_CASE						)
                     model.eval()
                     with torch.no_grad():
                            UpperCamelCase    :	Optional[Any]   	=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)
                     UpperCamelCase    :	Optional[Any]   	=		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    :	Union[str, Any]   	=		len(__SCREAMING_SNAKE_CASE						)

                     # Check attention is always last and order is fine
                     UpperCamelCase    :	str   	=		True
                     UpperCamelCase    :	Any   	=		True
                     UpperCamelCase    :	Union[str, 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						)						)

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

                     UpperCamelCase    :	int   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()

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

              UpperCamelCase    :	Tuple   	=		outputs.hidden_states

              UpperCamelCase    :	List[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    :	Union[str, Any]   	=		(
                  config.patch_size
                  if isinstance(config.patch_size ,					collections.abc.Iterable						)
                  else (config.patch_size, config.patch_size)
              )

              UpperCamelCase    :	List[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] ,					)

              UpperCamelCase    :	List[str]   	=		outputs.reshaped_hidden_states
              self.assertEqual(len(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		reshaped_hidden_states[0].shape
              UpperCamelCase    :	int   	=		(
                  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  _lowercase   (     self						):




              """simple docstring"""




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

              UpperCamelCase    :	str   	=		(
                  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    :	str   	=		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    :	str   	=		True

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	Tuple   	=		3

              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)
              )
              UpperCamelCase    :	Tuple   	=		(
                  config.patch_size
                  if isinstance(config.patch_size ,					collections.abc.Iterable						)
                  else (config.patch_size, config.patch_size)
              )

              UpperCamelCase    :	Tuple   	=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
              UpperCamelCase    :	Dict   	=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	Optional[int]   	=		SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	int   	=		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase    :	int   	=		_config_zero_init(__SCREAMING_SNAKE_CASE						)
              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Dict   	=		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):
       '''simple docstring'''






       @cached_property
       def  _lowercase   (     self						):




              """simple docstring"""




              return (
                  AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''						)
                  if is_vision_available()
                  else None
              )






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''						).to(
                  __SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		self.default_image_processor

              UpperCamelCase    :	str   	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
              UpperCamelCase    :	Any   	=		image_processor(images=__SCREAMING_SNAKE_CASE ,					return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

              # forward pass
              with torch.no_grad():
                     UpperCamelCase    :	int   	=		model(**__SCREAMING_SNAKE_CASE						)

              # verify the logits
              UpperCamelCase    :	Union[str, Any]   	=		torch.Size((1, 1_000)						)
              self.assertEqual(outputs.logits.shape ,					__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Tuple   	=		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						)						)





def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if number > 0:
              raise ValueError('''input must be a negative integer'''       )
       UpperCamelCase    :	List[str]   	=		len(bin(SCREAMING_SNAKE_CASE_       )[3:]       )
       UpperCamelCase    :	List[str]   	=		bin(abs(SCREAMING_SNAKE_CASE_       ) - (1 << binary_number_length)       )[3:]
       UpperCamelCase    :	Dict   	=		(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE_       ))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
  import doctest

  doctest.testmod()

