code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )
  
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import AutoImageProcessor
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=13					,       SCREAMING_SNAKE_CASE_=32					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=3					,       SCREAMING_SNAKE_CASE_=16					,       SCREAMING_SNAKE_CASE_=[1, 2, 1]					,       SCREAMING_SNAKE_CASE_=[2, 2, 4]					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=2.0					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=0.0					,       SCREAMING_SNAKE_CASE_=0.0					,       SCREAMING_SNAKE_CASE_=0.1					,       SCREAMING_SNAKE_CASE_="gelu"					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=0.02					,       SCREAMING_SNAKE_CASE_=1e-5					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=10					,       SCREAMING_SNAKE_CASE_=8					,       )       ->   None:
            UpperCamelCase :Optional[Any]										=     parent
            UpperCamelCase :Optional[Any]										=     batch_size
            UpperCamelCase :List[Any]										=     image_size
            UpperCamelCase :List[Any]										=     patch_size
            UpperCamelCase :List[Any]										=     num_channels
            UpperCamelCase :Optional[Any]										=     embed_dim
            UpperCamelCase :List[Any]										=     depths
            UpperCamelCase :List[Any]										=     num_heads
            UpperCamelCase :Tuple										=     window_size
            UpperCamelCase :Optional[int]										=     mlp_ratio
            UpperCamelCase :int										=     qkv_bias
            UpperCamelCase :str										=     hidden_dropout_prob
            UpperCamelCase :Dict										=     attention_probs_dropout_prob
            UpperCamelCase :Dict										=     drop_path_rate
            UpperCamelCase :Optional[Any]										=     hidden_act
            UpperCamelCase :int										=     use_absolute_embeddings
            UpperCamelCase :Any										=     patch_norm
            UpperCamelCase :str										=     layer_norm_eps
            UpperCamelCase :List[Any]										=     initializer_range
            UpperCamelCase :str										=     is_training
            UpperCamelCase :Union[str, Any]										=     scope
            UpperCamelCase :List[str]										=     use_labels
            UpperCamelCase :Union[str, Any]										=     type_sequence_label_size
            UpperCamelCase :int										=     encoder_stride
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            UpperCamelCase :Dict										=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
  
            UpperCamelCase :List[Any]										=     None
            if self.use_labels:
                UpperCamelCase :Tuple										=     ids_tensor([self.batch_size]					,       self.type_sequence_label_size				)
  
            UpperCamelCase :List[str]										=     self.get_config()
  
            return config, pixel_values, labels
        def 					UpperCAmelCase		(       self				)       ->   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					,       )
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            UpperCamelCase :int										=     TFSwinModel(config=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :str										=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
            UpperCamelCase :Union[str, Any]										=     int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)
  
            self.parent.assertEqual(result.last_hidden_state.shape					,       (self.batch_size, expected_seq_len, expected_dim)				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
            UpperCamelCase :int										=     TFSwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[str]										=     model(SCREAMING_SNAKE_CASE_				)
            self.parent.assertEqual(
                result.logits.shape					,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)
  
            # test greyscale images
            UpperCamelCase :Optional[int]										=     1
            UpperCamelCase :int										=     TFSwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :str										=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
            UpperCamelCase :str										=     model(SCREAMING_SNAKE_CASE_				)
            self.parent.assertEqual(result.logits.shape					,       (self.batch_size, 1, self.image_size, self.image_size)				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            UpperCamelCase :Optional[Any]										=     self.type_sequence_label_size
            UpperCamelCase :Optional[int]										=     TFSwinForImageClassification(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_					,       labels=SCREAMING_SNAKE_CASE_				)
            self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.type_sequence_label_size)				)
  
            # test greyscale images
            UpperCamelCase :Union[str, Any]										=     1
            UpperCamelCase :Tuple										=     TFSwinForImageClassification(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Any										=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
            UpperCamelCase :Tuple										=     model(SCREAMING_SNAKE_CASE_				)
            self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.type_sequence_label_size)				)
  
  
  
  
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :List[str]										=     self.prepare_config_and_inputs()
            UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :Optional[Any]										=     config_and_inputs
            UpperCamelCase :Any										=     {'''pixel_values''': pixel_values}
            return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class     UpperCAmelCase_		(      lowercase,			lowercase,			unittest.TestCase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      int     =(
            (
                TFSwinModel,
                TFSwinForImageClassification,
                TFSwinForMaskedImageModeling,
            )
            if is_tf_available()
            else ()
        )
        UpperCamelCase_       :      Any     =(
            {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
            if is_tf_available()
            else {}
        )
  
        UpperCamelCase_       :      Optional[int]     =False
        UpperCamelCase_       :      int     =False
        UpperCamelCase_       :      Optional[int]     =False
        UpperCamelCase_       :      Any     =False
        def 					UpperCAmelCase		(       self				)       ->   Tuple:
            UpperCamelCase :str										=     TFSwinModelTester(self				)
            UpperCamelCase :str										=     ConfigTester(self					,       config_class=SCREAMING_SNAKE_CASE_					,       embed_dim=37				)
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            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				)       ->   Union[str, Any]:
            return
        def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
            UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            UpperCamelCase :Optional[int]										=     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
            UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_				)
        @unittest.skip(reason='''Swin does not use inputs_embeds'''				)
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            pass
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            UpperCamelCase							,     UpperCamelCase :int										=     self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
                UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
                self.assertIsInstance(model.get_input_embeddings()					,       tf.keras.layers.Layer				)
                UpperCamelCase :Union[str, Any]										=     model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_					,       tf.keras.layers.Dense				)				)
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase							,     UpperCamelCase :str										=     self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
                UpperCamelCase :Union[str, Any]										=     model_class(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Union[str, Any]										=     inspect.signature(model.call				)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCamelCase :Dict										=     [*signature.parameters.keys()]
  
                UpperCamelCase :Tuple										=     ['''pixel_values''']
                self.assertListEqual(arg_names[:1]					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   List[Any]:
            UpperCamelCase							,     UpperCamelCase :Optional[int]										=     self.model_tester.prepare_config_and_inputs_for_common()
            UpperCamelCase :str										=     True
  
            for model_class in self.all_model_classes:
                UpperCamelCase :List[Any]										=     True
                UpperCamelCase :Optional[Any]										=     False
                UpperCamelCase :List[str]										=     True
                UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Union[str, Any]										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
                UpperCamelCase :List[Any]										=     outputs.attentions
                UpperCamelCase :int										=     len(self.model_tester.depths				)
                self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
  
                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                UpperCamelCase :Optional[Any]										=     True
                UpperCamelCase :List[Any]										=     config.window_size**2
                UpperCamelCase :Dict										=     model_class(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Dict										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
                UpperCamelCase :List[Any]										=     outputs.attentions
                self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
  
                self.assertListEqual(
                    list(attentions[0].shape[-3:]				)					,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,       )
                UpperCamelCase :List[Any]										=     len(SCREAMING_SNAKE_CASE_				)
  
                # Check attention is always last and order is fine
                UpperCamelCase :int										=     True
                UpperCamelCase :List[str]										=     True
                UpperCamelCase :List[str]										=     model_class(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Tuple										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
  
                if hasattr(self.model_tester					,       '''num_hidden_states_types'''				):
                    UpperCamelCase :Union[str, Any]										=     self.model_tester.num_hidden_states_types
                else:
                    # also another +1 for reshaped_hidden_states
                    UpperCamelCase :Optional[int]										=     2
                self.assertEqual(out_len + added_hidden_states					,       len(SCREAMING_SNAKE_CASE_				)				)
  
                UpperCamelCase :Optional[int]										=     outputs.attentions
  
                self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_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					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
            UpperCamelCase :Optional[int]										=     model_class(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Any										=     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)				)
            UpperCamelCase :Union[str, Any]										=     outputs.hidden_states
  
            UpperCamelCase :Any										=     getattr(
                self.model_tester					,       '''expected_num_hidden_layers'''					,       len(self.model_tester.depths				) + 1				)
            self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
  
            # Swin has a different seq_length
            UpperCamelCase :str										=     to_atuple(config.patch_size				)
  
            UpperCamelCase :Optional[int]										=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]				)					,       [num_patches, self.model_tester.embed_dim]					,       )
  
            UpperCamelCase :str										=     outputs.reshaped_hidden_states
            self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :Optional[int]										=     reshaped_hidden_states[0].shape
  
            UpperCamelCase :int										=     tf.reshape(reshaped_hidden_states[0]					,       (batch_size, num_channels, height * width)				)
            UpperCamelCase :Dict										=     tf.transpose(SCREAMING_SNAKE_CASE_					,       (0, 2, 1)				)
  
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]				)					,       [num_patches, self.model_tester.embed_dim]					,       )
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase							,     UpperCamelCase :int										=     self.model_tester.prepare_config_and_inputs_for_common()
  
            UpperCamelCase :List[Any]										=     to_atuple(self.model_tester.image_size				)
  
            for model_class in self.all_model_classes:
                UpperCamelCase :List[Any]										=     True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCamelCase :List[str]										=     True
  
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
            UpperCamelCase							,     UpperCamelCase :List[Any]										=     self.model_tester.prepare_config_and_inputs_for_common()
            UpperCamelCase :List[Any]										=     3
  
            UpperCamelCase :Any										=     to_atuple(self.model_tester.image_size				)
            UpperCamelCase :List[Any]										=     to_atuple(config.patch_size				)
  
            UpperCamelCase :Dict										=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            UpperCamelCase :List[str]										=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
            for model_class in self.all_model_classes:
                UpperCamelCase :List[Any]										=     True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       (padded_height, padded_width)				)
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCamelCase :str										=     True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       (padded_height, padded_width)				)
  
  
  
  
        @slow
        def 					UpperCAmelCase		(       self				)       ->   List[Any]:
            for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCamelCase :Dict										=     TFSwinModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
  
  @require_vision
  @require_tf
  class     UpperCAmelCase_		(      unittest.TestCase				):
  
  
  
        """simple docstring"""
        @cached_property
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            return (
                AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''				)
                if is_vision_available()
                else None
            )
  
  
  
  
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            UpperCamelCase :Tuple										=     TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''				)
            UpperCamelCase :int										=     self.default_image_processor
  
            UpperCamelCase :Optional[int]										=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
            UpperCamelCase :List[str]										=     image_processor(images=SCREAMING_SNAKE_CASE_					,       return_tensors='''tf'''				)
  
            # forward pass
            UpperCamelCase :Optional[Any]										=     model(SCREAMING_SNAKE_CASE_				)
  
            # verify the logits
            UpperCamelCase :Tuple										=     tf.TensorShape((1, 1000)				)
            self.assertEqual(outputs.logits.shape					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[Any]										=     tf.constant([-0.0948, -0.6454, -0.0921]				)
            self.assertTrue(np.allclose(outputs.logits[0, :3]					,       SCREAMING_SNAKE_CASE_					,       atol=1e-4				)				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            UpperCamelCase :Tuple										=     value
            UpperCamelCase :Node | None										=     None
            UpperCamelCase :Node | None										=     None
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      int
        def __init__(       self				)       ->   None:
            UpperCamelCase :List[Any]										=     0
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if node is None:
                return
  
            if path_sum == self.target:
                self.paths += 1
  
            if node.left:
                self.depth_first_search(node.left					,       path_sum + node.left.value				)
            if node.right:
                self.depth_first_search(node.right					,       path_sum + node.right.value				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None				)       ->   int:
            if node is None:
                return 0
            if target is not None:
                UpperCamelCase :Tuple										=     target
  
            self.depth_first_search(SCREAMING_SNAKE_CASE_					,       node.value				)
            self.path_sum(node.left				)
            self.path_sum(node.right				)
  
            return self.paths
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  