

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 						__magic_name__					:






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






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         UpperCamelCase_			  =						None
         if self.use_labels:
             UpperCamelCase_			  =						ids_tensor([self.batch_size]		,						self.type_sequence_label_size	)

         UpperCamelCase_			  =						self.get_config()

         return config, pixel_values, labels






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         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		,						_lowercase		,						_lowercase		,						_lowercase	)->     Dict:
         UpperCamelCase_			  =						SwinvaModel(config=_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase	)

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

         # test greyscale images
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						SwinvaForMaskedImageModeling(_lowercase	)
         model.to(_lowercase	)
         model.eval()

         UpperCamelCase_			  =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
         UpperCamelCase_			  =						model(_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, 1, self.image_size, self.image_size)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Optional[Any]:
         UpperCamelCase_			  =						self.type_sequence_label_size
         UpperCamelCase_			  =						SwinvaForImageClassification(_lowercase	)
         model.to(_lowercase	)
         model.eval()
         UpperCamelCase_			  =						model(_lowercase		,						labels=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						self.prepare_config_and_inputs()
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						config_and_inputs
         UpperCamelCase_			  =						{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 						__magic_name__					(							snake_case			,      snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:Tuple										=      (
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     UpperCamelCase_	:Optional[Any]										=      (
         {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCamelCase_	:Any										=      False
     UpperCamelCase_	:Any										=      False
     UpperCamelCase_	:str										=      False
     UpperCamelCase_	:Optional[Any]										=      False






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						SwinvaModelTester(self	)
         UpperCamelCase_			  =						ConfigTester(self		,						config_class=_lowercase		,						embed_dim=37	)






     def 	UpperCAmelCase_				(				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 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase	)






     @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."	)
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         pass






     @unittest.skip(reason="Swinv2 does not use inputs_embeds"	)
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         pass






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

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)
             self.assertIsInstance(model.get_input_embeddings()		,						(nn.Module)	)
             UpperCamelCase_			  =						model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowercase		,						nn.Linear	)	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase_			  =						["pixel_values"]
             self.assertListEqual(arg_names[:1]		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_			  =						True

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						False
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						model_class(_lowercase	)
             model.to(_lowercase	)
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)	)
             UpperCamelCase_			  =						outputs.attentions
             UpperCamelCase_			  =						len(self.model_tester.depths	)
             self.assertEqual(len(_lowercase	)		,						_lowercase	)

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

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

             # Check attention is always last and order is fine
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						model_class(_lowercase	)
             model.to(_lowercase	)
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)	)

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

             UpperCamelCase_			  =						outputs.attentions

             self.assertEqual(len(_lowercase	)		,						_lowercase	)

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)->     Any:
         UpperCamelCase_			  =						model_class(_lowercase	)
         model.to(_lowercase	)
         model.eval()

         with torch.no_grad():
             UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)	)

         UpperCamelCase_			  =						outputs.hidden_states

         UpperCamelCase_			  =						getattr(
             self.model_tester		,						"expected_num_hidden_layers"		,						len(self.model_tester.depths	) + 1	)
         self.assertEqual(len(_lowercase	)		,						_lowercase	)

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

         UpperCamelCase_			  =						(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_			  =						outputs.reshaped_hidden_states
         self.assertEqual(len(_lowercase	)		,						_lowercase	)

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






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

         UpperCamelCase_			  =						(
             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_			  =						True
             self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase_			  =						True

             self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_			  =						3

         UpperCamelCase_			  =						(
             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_			  =						(
             config.patch_size
             if isinstance(config.patch_size		,						collections.abc.Iterable	)
             else (config.patch_size, config.patch_size)
         )

         UpperCamelCase_			  =						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCamelCase_			  =						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						True
             self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						(padded_height, padded_width)	)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase_			  =						True
             self.check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase		,						(padded_height, padded_width)	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_			  =						SwinvaModel.from_pretrained(_lowercase	)
             self.assertIsNotNone(_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_			  =						_config_zero_init(_lowercase	)
         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						model_class(config=_lowercase	)
             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 						__magic_name__					(							unittest.TestCase				):






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






     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"	).to(
             _lowercase	)
         UpperCamelCase_			  =						self.default_image_processor

         UpperCamelCase_			  =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
         UpperCamelCase_			  =						image_processor(images=_lowercase		,						return_tensors="pt"	).to(_lowercase	)

         # forward pass
         with torch.no_grad():
             UpperCamelCase_			  =						model(**_lowercase	)

         # verify the logits
         UpperCamelCase_			  =						torch.Size((1, 1_000)	)
         self.assertEqual(outputs.logits.shape		,						_lowercase	)
         UpperCamelCase_			  =						torch.tensor([-0.3_947, -0.4_306, 0.0_026]	).to(_lowercase	)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]		,						_lowercase		,						atol=1e-4	)	)



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
