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      :						int    ,					lowercase__      :						Optional[int]    ,					lowercase__      :						int=13    ,					lowercase__      :						Union[str, Any]=32    ,					lowercase__      :						Optional[int]=2    ,					lowercase__      :						int=3    ,					lowercase__      :						Union[str, Any]=16    ,					lowercase__      :						Optional[int]=[1, 2, 1]    ,					lowercase__      :						Optional[int]=[2, 2, 4]    ,					lowercase__      :						str=2    ,					lowercase__      :						Tuple=2.0    ,					lowercase__      :						Optional[int]=True    ,					lowercase__      :						str=0.0    ,					lowercase__      :						Dict=0.0    ,					lowercase__      :						List[Any]=0.1    ,					lowercase__      :						Union[str, Any]="gelu"    ,					lowercase__      :						int=False    ,					lowercase__      :						Optional[int]=True    ,					lowercase__      :						List[str]=0.02    ,					lowercase__      :						int=1e-5    ,					lowercase__      :						List[str]=True    ,					lowercase__      :						Any=None    ,					lowercase__      :						Union[str, Any]=True    ,					lowercase__      :						Optional[Any]=10    ,					lowercase__      :						List[Any]=8    ,					lowercase__      :						Any=["stage1", "stage2"]    ,					lowercase__      :						List[Any]=[1, 2]    ,					):







        '''simple docstring'''



        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      :						str):







        '''simple docstring'''



        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      :						Optional[int]):







        '''simple docstring'''



        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      :						Dict    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						int    ,					lowercase__      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinModel(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__  			=      model(lowercase__)

        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      :						List[str]    ,					lowercase__      :						Dict    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinBackbone(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__  			=      model(lowercase__)

        # 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], 16, 16])

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

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

        # 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      :						Tuple    ,					lowercase__      :						Optional[int]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinForMaskedImageModeling(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__  			=      model(lowercase__)
        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(lowercase__)
        model.to(lowercase__)
        model.eval()

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




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						Tuple    ,					lowercase__      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.type_sequence_label_size
        lowerCAmelCase__  			=      SwinForImageClassification(lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__  			=      model(lowercase__    ,					labels=lowercase__)
        self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      SwinForImageClassification(lowercase__)
        model.to(lowercase__)
        model.eval()

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




 def 	__snake_case		(    self      :						Union[str, Any]):







        '''simple docstring'''



        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'''






 UpperCAmelCase_							=      (
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase_							=      (
     {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase_							=      True

 UpperCAmelCase_							=      False
 UpperCAmelCase_							=      False
 UpperCAmelCase_							=      False




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinModelTester(self)
        lowerCAmelCase__  			=      ConfigTester(self    ,					config_class=lowercase__    ,					embed_dim=37)




 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        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      :						Any):







        '''simple docstring'''



        return




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      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 	__snake_case		(    self      :						int):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        super().test_training_gradient_checkpointing()




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase__)




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__)




 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase__)




 @unittest.skip(reason='Swin does not use inputs_embeds')
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        pass




 @unittest.skip(reason='Swin Transformer does not use feedforward chunking')
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs_for_common()

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




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__  			=      model_class(lowercase__)
               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]    ,					lowercase__)




 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        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(lowercase__)
               model.to(lowercase__)
               model.eval()
               with torch.no_grad():
                      lowerCAmelCase__  			=      model(**self._prepare_for_class(lowercase__    ,					lowercase__))
               lowerCAmelCase__  			=      outputs.attentions
               lowerCAmelCase__  			=      len(self.model_tester.depths)
               self.assertEqual(len(lowercase__)    ,					lowercase__)

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

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

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

               lowerCAmelCase__  			=      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 	__snake_case		(    self      :						List[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      model_class(lowercase__)
        model.to(lowercase__)
        model.eval()

        with torch.no_grad():
               lowerCAmelCase__  			=      model(**self._prepare_for_class(lowercase__    ,					lowercase__))

        lowerCAmelCase__  			=      outputs.hidden_states

        lowerCAmelCase__  			=      getattr(
            self.model_tester    ,					'expected_num_hidden_layers'    ,					len(self.model_tester.depths) + 1)
        self.assertEqual(len(lowercase__)    ,					lowercase__)

        # 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(lowercase__)    ,					lowercase__)

               lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__  			=      reshaped_hidden_states[0].shape
               lowerCAmelCase__  			=      (
                   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 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



        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(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__)

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

               self.check_hidden_states_output(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__)




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        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(lowercase__    ,					lowercase__    ,					lowercase__    ,					(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(lowercase__    ,					lowercase__    ,					lowercase__    ,					(padded_height, padded_width))




 @slow
 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__  			=      SwinModel.from_pretrained(lowercase__)
               self.assertIsNotNone(lowercase__)




 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        lowerCAmelCase__			,      lowerCAmelCase__  			=      self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__  			=      _config_zero_init(lowercase__)
        for model_class in self.all_model_classes:
               lowerCAmelCase__  			=      model_class(config=lowercase__)
               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      :						Tuple):







        '''simple docstring'''



        return (
            AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224')
            if is_vision_available()
            else None
        )




 @slow
 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224').to(lowercase__)
        lowerCAmelCase__  			=      self.default_image_processor

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

        # forward pass
        with torch.no_grad():
               lowerCAmelCase__  			=      model(**lowercase__)

        # verify the logits
        lowerCAmelCase__  			=      torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape    ,					lowercase__)
        lowerCAmelCase__  			=      torch.tensor([-0.0_948, -0.6_454, -0.0_921]).to(lowercase__)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,					lowercase__    ,					atol=1e-4))






@require_torch
class        a_				(		unittest.TestCase ,  __lowerCamelCase		):






 '''simple docstring'''






 UpperCAmelCase_							=      (SwinBackbone,) if is_torch_available() else ()
 UpperCAmelCase_							=      SwinConfig




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      SwinModelTester(self)

def    __lowerCamelCase	(		lowerCAmelCase__			):
       return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
