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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:	Optional[int]        =					None
       if self.use_labels:
         _lowerCAmelCase						:	Any        =					ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )

       _lowerCAmelCase						:	List[Any]        =					self.get_config()

       return config, pixel_values, labels






     def    __A  (		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    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					SwinvaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	List[Any]        =					model(a__  )

       _lowerCAmelCase						:	int        =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
       _lowerCAmelCase						:	Union[str, 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    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[int]        =					SwinvaForMaskedImageModeling(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Optional[Any]        =					model(a__  )
       self.parent.assertEqual(
           result.logits.shape    ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

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

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






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					self.type_sequence_label_size
       _lowerCAmelCase						:	Union[str, Any]        =					SwinvaForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Any        =					model(a__    ,					labels=a__  )
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	int        =					config_and_inputs
       _lowerCAmelCase						:	List[Any]        =					{"""pixel_values""": pixel_values}
       return config, inputs_dict




@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[Any]		 =		(
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     _UpperCamelCase							:      List[Any]		 =		(
         {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:      List[Any]		 =		False
     _UpperCamelCase							:      Tuple		 =		False
     _UpperCamelCase							:      Dict		 =		False
     _UpperCamelCase							:      str		 =		False






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					SwinvaModelTester(self  )
       _lowerCAmelCase						:	List[str]        =					ConfigTester(self    ,					config_class=a__    ,					embed_dim=37  )






     def    __A  (		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    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					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    __A  (		self  ):
       pass






     @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""  )
     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase						:	List[Any]        =					["""pixel_values"""]
         self.assertListEqual(arg_names[:1]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:	Optional[int]        =					True

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

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

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

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

         _lowerCAmelCase						:	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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					model_class(a__  )
       model.to(a__  )
       model.eval()

       with torch.no_grad():
         _lowerCAmelCase						:	int        =					model(**self._prepare_for_class(a__    ,					a__  )  )

       _lowerCAmelCase						:	List[str]        =					outputs.hidden_states

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

       _lowerCAmelCase						:	int        =					(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]    ,					)

       _lowerCAmelCase						:	Dict        =					outputs.reshaped_hidden_states
       self.assertEqual(len(a__  )    ,					a__  )

       _lowerCAmelCase						:	str        =					reshaped_hidden_states[0].shape
       _lowerCAmelCase						:	Dict        =					(
           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    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase						:	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:
         _lowerCAmelCase						:	int        =					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"]
         _lowerCAmelCase						:	List[Any]        =					True

         self.check_hidden_states_output(a__    ,					a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:	Any        =					3

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

       _lowerCAmelCase						:	Tuple        =					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       _lowerCAmelCase						:	List[Any]        =					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Dict        =					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"]
         _lowerCAmelCase						:	Any        =					True
         self.check_hidden_states_output(a__    ,					a__    ,					a__    ,					(padded_height, padded_width)  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def    __A  (		self  ):
       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:	List[str]        =					SwinvaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase						:	Optional[int]        =					_config_zero_init(a__  )
       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	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    __A  (		self  ):
       return (
           AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""  )
           if is_vision_available()
           else None
       )






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""  ).to(
           a__  )
       _lowerCAmelCase						:	Optional[int]        =					self.default_image_processor

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

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:	str        =					model(**a__  )

       # verify the logits
       _lowerCAmelCase						:	Optional[int]        =					torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape    ,					a__  )
       _lowerCAmelCase						:	Optional[Any]        =					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"""




_a				:		Any	   =       '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a				:		List[Any]	   =       [{'type': 'code', 'content': INSTALL_CONTENT}]
_a				:		Union[str, Any]	   =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




