




import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
      from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class      A     :
     """simple docstring"""

     def __init__(      self					:				List[str]		,					A__					:				Union[str, Any]		,					A__					:				List[str]=1_3		,					A__					:				Dict=3_2		,					A__					:				Union[str, Any]=2		,					A__					:				Optional[int]=3		,					A__					:				List[str]=1_6		,					A__					:				List[Any]=[1, 2, 1]		,					A__					:				Union[str, Any]=[2, 2, 4]		,					A__					:				Union[str, Any]=2		,					A__					:				Union[str, Any]=2.0		,					A__					:				Tuple=True		,					A__					:				Optional[Any]=0.0		,					A__					:				Any=0.0		,					A__					:				int=0.1		,					A__					:				Union[str, Any]="gelu"		,					A__					:				List[str]=False		,					A__					:				Union[str, Any]=True		,					A__					:				List[str]=0.02		,					A__					:				List[Any]=1E-5		,					A__					:				Tuple=True		,					A__					:				Optional[int]=None		,					A__					:				Tuple=True		,					A__					:				Tuple=1_0		,					A__					:				Dict=8		,					A__					:				Any=["stage1", "stage2"]		,					A__					:				Dict=[1, 2]		,					)			->    Tuple:
           lowerCamelCase__ 					=			parent
           lowerCamelCase__ 					=			batch_size
           lowerCamelCase__ 					=			image_size
           lowerCamelCase__ 					=			patch_size
           lowerCamelCase__ 					=			num_channels
           lowerCamelCase__ 					=			embed_dim
           lowerCamelCase__ 					=			depths
           lowerCamelCase__ 					=			num_heads
           lowerCamelCase__ 					=			window_size
           lowerCamelCase__ 					=			mlp_ratio
           lowerCamelCase__ 					=			qkv_bias
           lowerCamelCase__ 					=			hidden_dropout_prob
           lowerCamelCase__ 					=			attention_probs_dropout_prob
           lowerCamelCase__ 					=			drop_path_rate
           lowerCamelCase__ 					=			hidden_act
           lowerCamelCase__ 					=			use_absolute_embeddings
           lowerCamelCase__ 					=			patch_norm
           lowerCamelCase__ 					=			layer_norm_eps
           lowerCamelCase__ 					=			initializer_range
           lowerCamelCase__ 					=			is_training
           lowerCamelCase__ 					=			scope
           lowerCamelCase__ 					=			use_labels
           lowerCamelCase__ 					=			type_sequence_label_size
           lowerCamelCase__ 					=			encoder_stride
           lowerCamelCase__ 					=			out_features
           lowerCamelCase__ 					=			out_indices




     def 	__snake_case				(      self					:				Optional[Any]     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

           lowerCamelCase__ 					=			self.get_config()

           return config, pixel_values, labels




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[int]:
           return SwinConfig(
               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		,					out_features=self.out_features		,					out_indices=self.out_indices		,					)




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Optional[int]		,					A__					:				int		,					A__					:				str     )			->    List[str]:
           lowerCamelCase__ 					=			SwinModel(config=A__     )
           model.to(A__     )
           model.eval()
           lowerCamelCase__ 					=			model(A__     )

           lowerCamelCase__ 					=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
           lowerCamelCase__ 					=			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 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Any		,					A__					:				str		,					A__					:				str     )			->    Optional[Any]:
           lowerCamelCase__ 					=			SwinBackbone(config=A__     )
           model.to(A__     )
           model.eval()
           lowerCamelCase__ 					=			model(A__     )

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps     )		,					len(config.out_features     )     )
           self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,					[self.batch_size, model.channels[0], 1_6, 1_6]     )

           # verify channels
           self.parent.assertEqual(len(model.channels     )		,					len(config.out_features     )     )

           # verify backbone works with out_features=None
           lowerCamelCase__ 					=			None
           lowerCamelCase__ 					=			SwinBackbone(config=A__     )
           model.to(A__     )
           model.eval()
           lowerCamelCase__ 					=			model(A__     )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps     )		,					1     )
           self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,					[self.batch_size, model.channels[-1], 4, 4]     )

           # verify channels
           self.parent.assertEqual(len(model.channels     )		,					1     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				str		,					A__					:				Optional[int]		,					A__					:				Tuple     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			SwinForMaskedImageModeling(config=A__     )
           model.to(A__     )
           model.eval()
           lowerCamelCase__ 					=			model(A__     )
           self.parent.assertEqual(
               result.logits.shape		,					(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

           # test greyscale images
           lowerCamelCase__ 					=			1
           lowerCamelCase__ 					=			SwinForMaskedImageModeling(A__     )
           model.to(A__     )
           model.eval()

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




     def 	__snake_case				(      self					:				Tuple		,					A__					:				int		,					A__					:				Dict		,					A__					:				Tuple     )			->    Any:
           lowerCamelCase__ 					=			self.type_sequence_label_size
           lowerCamelCase__ 					=			SwinForImageClassification(A__     )
           model.to(A__     )
           model.eval()
           lowerCamelCase__ 					=			model(A__		,					labels=A__     )
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)     )

           # test greyscale images
           lowerCamelCase__ 					=			1
           lowerCamelCase__ 					=			SwinForImageClassification(A__     )
           model.to(A__     )
           model.eval()

           lowerCamelCase__ 					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
           lowerCamelCase__ 					=			model(A__     )
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)     )




     def 	__snake_case				(      self					:				List[str]     )			->    List[str]:
           lowerCamelCase__ 					=			self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase__
           )					,						(
               lowerCamelCase__
           )					,						(
               lowerCamelCase__
           )					,						
           ) 					=			config_and_inputs
           lowerCamelCase__ 					=			{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class      A     (						_lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		List[Any]       =(
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _snake_case:		List[str]       =(
         {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     _snake_case:		Optional[int]       =True

     _snake_case:		List[str]       =False
     _snake_case:		Tuple       =False
     _snake_case:		Union[str, Any]       =False




     def 	__snake_case				(      self					:				List[str]     )			->    int:
           lowerCamelCase__ 					=			SwinModelTester(self     )
           lowerCamelCase__ 					=			ConfigTester(self		,					config_class=A__		,					embed_dim=3_7     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    Tuple:
           self.create_and_test_config_common_properties()
           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 	__snake_case				(      self					:				Dict     )			->    str:
           return




     def 	__snake_case				(      self					:				Optional[int]     )			->    Tuple:
           lowerCamelCase__ 					=			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 	__snake_case				(      self					:				int     )			->    Dict:
           pass




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           super().test_training_gradient_checkpointing()




     def 	__snake_case				(      self					:				str     )			->    List[Any]:
           lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*A__     )




     def 	__snake_case				(      self					:				List[str]     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A__     )




     @unittest.skip(reason='Swin does not use inputs_embeds'     )
     def 	__snake_case				(      self					:				int     )			->    Tuple:
           pass




     @unittest.skip(reason='Swin Transformer does not use feedforward chunking'     )
     def 	__snake_case				(      self					:				Dict     )			->    int:
           pass




     def 	__snake_case				(      self					:				List[Any]     )			->    Optional[int]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()

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




     def 	__snake_case				(      self					:				str     )			->    Union[str, Any]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase__ 					=			['pixel_values']
                 self.assertListEqual(arg_names[:1]		,					A__     )




     def 	__snake_case				(      self					:				int     )			->    List[Any]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__ 					=			True

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

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

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

                 # also another +1 for reshaped_hidden_states
                 lowerCamelCase__ 					=			1 if model_class.__name__ == 'SwinBackbone' else 2
                 self.assertEqual(out_len + added_hidden_states		,					len(A__     )     )

                 lowerCamelCase__ 					=			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 	__snake_case				(      self					:				Optional[int]		,					A__					:				Union[str, Any]		,					A__					:				List[Any]		,					A__					:				Optional[int]		,					A__					:				str     )			->    int:
           lowerCamelCase__ 					=			model_class(A__     )
           model.to(A__     )
           model.eval()

           with torch.no_grad():
                 lowerCamelCase__ 					=			model(**self._prepare_for_class(A__		,					A__     )     )

           lowerCamelCase__ 					=			outputs.hidden_states

           lowerCamelCase__ 					=			getattr(
               self.model_tester		,					'expected_num_hidden_layers'		,					len(self.model_tester.depths     ) + 1     )
           self.assertEqual(len(A__     )		,					A__     )

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

           lowerCamelCase__ 					=			(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]		,					)

           if not model_class.__name__ == "SwinBackbone":
                 lowerCamelCase__ 					=			outputs.reshaped_hidden_states
                 self.assertEqual(len(A__     )		,					A__     )

                 lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			reshaped_hidden_states[0].shape
                 lowerCamelCase__ 					=			(
                     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 	__snake_case				(      self					:				str     )			->    Tuple:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase__ 					=			(
               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__ 					=			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__ 					=			True

                 self.check_hidden_states_output(A__		,					A__		,					A__		,					A__     )




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__ 					=			3

           lowerCamelCase__ 					=			(
               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__ 					=			(
               config.patch_size
               if isinstance(config.patch_size		,					collections.abc.Iterable     )
               else (config.patch_size, config.patch_size)
           )

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

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




     @slow
     def 	__snake_case				(      self					:				Optional[int]     )			->    Optional[Any]:
           for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__ 					=			SwinModel.from_pretrained(A__     )
                 self.assertIsNotNone(A__     )




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase__ 					=			_config_zero_init(A__     )
           for model_class in self.all_model_classes:
                 lowerCamelCase__ 					=			model_class(config=A__     )
                 for name, param in model.named_parameters():
                       if "embeddings" 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						):
     """simple docstring"""

     @cached_property
     def 	__snake_case				(      self					:				Any     )			->    int:
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'     )
               if is_vision_available()
               else None
           )




     @slow
     def 	__snake_case				(      self					:				Optional[Any]     )			->    str:
           lowerCamelCase__ 					=			SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'     ).to(A__     )
           lowerCamelCase__ 					=			self.default_image_processor

           lowerCamelCase__ 					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
           lowerCamelCase__ 					=			image_processor(images=A__		,					return_tensors='pt'     ).to(A__     )

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__ 					=			model(**A__     )

           # verify the logits
           lowerCamelCase__ 					=			torch.Size((1, 1_0_0_0)     )
           self.assertEqual(outputs.logits.shape		,					A__     )
           lowerCamelCase__ 					=			torch.tensor([-0.0948, -0.6454, -0.0921]     ).to(A__     )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,					A__		,					atol=1E-4     )     )






@require_torch
class      A     (						unittest.TestCase     ,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(SwinBackbone,) if is_torch_available() else ()
     _snake_case:		List[str]       =SwinConfig




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           lowerCamelCase__ 					=			SwinModelTester(self     )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)