





"""simple docstring"""
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 lowerCamelCase__  :



   def __init__(       self      ,   snake_case      ,   snake_case=1_3      ,   snake_case=3_2      ,   snake_case=2      ,   snake_case=3      ,   snake_case=1_6      ,   snake_case=[1, 2, 1]      ,   snake_case=[2, 2, 4]      ,   snake_case=2      ,   snake_case=2.0      ,   snake_case=True      ,   snake_case=0.0      ,   snake_case=0.0      ,   snake_case=0.1      ,   snake_case="gelu"      ,   snake_case=False      ,   snake_case=True      ,   snake_case=0.02      ,   snake_case=1E-5      ,   snake_case=True      ,   snake_case=None      ,   snake_case=True      ,   snake_case=1_0      ,   snake_case=8      ,   snake_case=["stage1", "stage2"]      ,   snake_case=[1, 2]      ,   ) ->    str:







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



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

      lowercase    :			Optional[Any]  =       self.get_config()

      return config, pixel_values, labels



   def    _UpperCAmelCase	(       self	) ->    List[str]:







      """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    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Tuple:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       SwinModel(config=snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Optional[Any]  =       model(snake_case	)

      lowercase    :			Optional[int]  =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
      lowercase    :			Optional[int]  =       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      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Tuple:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       SwinBackbone(config=snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Optional[Any]  =       model(snake_case	)

      # 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
      lowercase    :			List[str]  =       None
      lowercase    :			Optional[int]  =       SwinBackbone(config=snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Dict  =       model(snake_case	)

      # 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    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    List[str]:







      """simple docstring"""
      lowercase    :			List[str]  =       SwinForMaskedImageModeling(config=snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Any  =       model(snake_case	)
      self.parent.assertEqual(
          result.logits.shape      ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

      # test greyscale images
      lowercase    :			Union[str, Any]  =       1
      lowercase    :			List[Any]  =       SwinForMaskedImageModeling(snake_case	)
      model.to(snake_case	)
      model.eval()

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



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Any:







      """simple docstring"""
      lowercase    :			Dict  =       self.type_sequence_label_size
      lowercase    :			List[Any]  =       SwinForImageClassification(snake_case	)
      model.to(snake_case	)
      model.eval()
      lowercase    :			Optional[int]  =       model(snake_case      ,   labels=snake_case	)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)	)

      # test greyscale images
      lowercase    :			List[Any]  =       1
      lowercase    :			Tuple  =       SwinForImageClassification(snake_case	)
      model.to(snake_case	)
      model.eval()

      lowercase    :			Optional[Any]  =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      lowercase    :			str  =       model(snake_case	)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)	)





   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      lowercase    :			int  =       self.prepare_config_and_inputs()
      (
          (
          lowercase		
      )  ,					(
          lowercase		
      )  ,					(
          lowercase		
      )  ,							
      )    :			List[str]  =       config_and_inputs
      lowercase    :			str  =       {"""pixel_values""": pixel_values}
      return config, inputs_dict




@require_torch
class lowerCamelCase__  (    UpperCAmelCase_  ,				UpperCAmelCase_  ,				unittest.TestCase				):
   __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    _UpperCAmelCase	(       self	) ->    List[str]:







      """simple docstring"""
      lowercase    :			str  =       SwinModelTester(self	)
      lowercase    :			Union[str, Any]  =       ConfigTester(self      ,   config_class=snake_case      ,   embed_dim=3_7	)



   def    _UpperCAmelCase	(       self	) ->    List[Any]:







      """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    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      return



   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      lowercase    :			Any  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case	)



   @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""	)
   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      pass



   def    _UpperCAmelCase	(       self	) ->    List[Any]:







      """simple docstring"""
      super().test_training_gradient_checkpointing()



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase    :			Dict  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*snake_case	)



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase    :			List[Any]  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*snake_case	)



   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      lowercase    :			Optional[int]  =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*snake_case	)



   @unittest.skip(reason="""Swin does not use inputs_embeds"""	)
   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      pass



   @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""	)
   def    _UpperCAmelCase	(       self	) ->    Tuple:







      """simple docstring"""
      pass



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase  ,					lowercase    :			Tuple  =       self.model_tester.prepare_config_and_inputs_for_common()

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



   def    _UpperCAmelCase	(       self	) ->    Tuple:







      """simple docstring"""
      lowercase  ,					lowercase    :			Any  =       self.model_tester.prepare_config_and_inputs_for_common()

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

         lowercase    :			Optional[int]  =       ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   snake_case	)



   def    _UpperCAmelCase	(       self	) ->    Tuple:







      """simple docstring"""
      lowercase  ,					lowercase    :			Tuple  =       self.model_tester.prepare_config_and_inputs_for_common()
      lowercase    :			Dict  =       True

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

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

         self.assertListEqual(
             list(attentions[0].shape[-3:]	)      ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,   )
         lowercase    :			List[str]  =       len(snake_case	)

         # Check attention is always last and order is fine
         lowercase    :			List[str]  =       True
         lowercase    :			List[str]  =       True
         lowercase    :			List[str]  =       model_class(snake_case	)
         model.to(snake_case	)
         model.eval()
         with torch.no_grad():
            lowercase    :			Tuple  =       model(**self._prepare_for_class(snake_case      ,   snake_case	)	)

         # also another +1 for reshaped_hidden_states
         lowercase    :			Optional[int]  =       1 if model_class.__name__ == """SwinBackbone""" else 2
         self.assertEqual(out_len + added_hidden_states      ,   len(snake_case	)	)

         lowercase    :			str  =       outputs.attentions

         self.assertEqual(len(snake_case	)      ,   snake_case	)

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



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Union[str, Any]:







      """simple docstring"""
      lowercase    :			Any  =       model_class(snake_case	)
      model.to(snake_case	)
      model.eval()

      with torch.no_grad():
         lowercase    :			Union[str, Any]  =       model(**self._prepare_for_class(snake_case      ,   snake_case	)	)

      lowercase    :			List[Any]  =       outputs.hidden_states

      lowercase    :			Optional[Any]  =       getattr(
          self.model_tester      ,   """expected_num_hidden_layers"""      ,   len(self.model_tester.depths	) + 1	)
      self.assertEqual(len(snake_case	)      ,   snake_case	)

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

      lowercase    :			Tuple  =       (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":
         lowercase    :			Any  =       outputs.reshaped_hidden_states
         self.assertEqual(len(snake_case	)      ,   snake_case	)

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



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      lowercase  ,					lowercase    :			Optional[Any]  =       self.model_tester.prepare_config_and_inputs_for_common()

      lowercase    :			Optional[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:
         lowercase    :			List[str]  =       True
         self.check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case      ,   snake_case	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowercase    :			int  =       True

         self.check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case      ,   snake_case	)



   def    _UpperCAmelCase	(       self	) ->    Union[str, Any]:







      """simple docstring"""
      lowercase  ,					lowercase    :			List[Any]  =       self.model_tester.prepare_config_and_inputs_for_common()
      lowercase    :			List[str]  =       3

      lowercase    :			Optional[int]  =       (
          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)
      )
      lowercase    :			Union[str, Any]  =       (
          config.patch_size
          if isinstance(config.patch_size      ,   collections.abc.Iterable	)
          else (config.patch_size, config.patch_size)
      )

      lowercase    :			Optional[Any]  =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      lowercase    :			List[Any]  =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
         lowercase    :			int  =       True
         self.check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case      ,   (padded_height, padded_width)	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowercase    :			Tuple  =       True
         self.check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case      ,   (padded_height, padded_width)	)



   @slow
   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase    :			List[str]  =       SwinModel.from_pretrained(snake_case	)
         self.assertIsNotNone(snake_case	)





   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      lowercase  ,					lowercase    :			Optional[Any]  =       self.model_tester.prepare_config_and_inputs_for_common()

      lowercase    :			Optional[int]  =       _config_zero_init(snake_case	)
      for model_class in self.all_model_classes:
         lowercase    :			int  =       model_class(config=snake_case	)
         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 lowerCamelCase__  (    unittest.TestCase				):



   @cached_property
   def    _UpperCAmelCase	(       self	) ->    int:







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





   @slow
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""	).to(snake_case	)
      lowercase    :			List[Any]  =       self.default_image_processor

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

      # forward pass
      with torch.no_grad():
         lowercase    :			List[str]  =       model(**snake_case	)

      # verify the logits
      lowercase    :			str  =       torch.Size((1, 1_0_0_0)	)
      self.assertEqual(outputs.logits.shape      ,   snake_case	)
      lowercase    :			List[Any]  =       torch.tensor([-0.09_48, -0.64_54, -0.09_21]	).to(snake_case	)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   snake_case      ,   atol=1E-4	)	)




@require_torch
class lowerCamelCase__  (    unittest.TestCase  ,				UpperCAmelCase_				):
   __UpperCAmelCase        =					(SwinBackbone,) if is_torch_available() else ()
   __UpperCAmelCase        =					SwinConfig



   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      lowercase    :			Optional[int]  =       SwinModelTester(self	)









"""simple docstring"""
from collections import defaultdict




def 			__snake_case						(      __A    )		->				dict:
   lowercase    :			defaultdict[str, int]  =       defaultdict(__A    )
   # Creating a dictionary containing count of each word
   for word in sentence.split():
      occurrence[word] += 1
   return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("INPUT STRING").items():
      print(F'{word}: {count}')


