code: 
  
  
  
  
  """simple docstring"""
  
  
  
  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_						:
  
  
  
  
      def __init__(					self		: Tuple			,				snake_case_		: List[str]			,				snake_case_		: int=13			,				snake_case_		: int=32			,				snake_case_		: str=2			,				snake_case_		: Optional[Any]=3			,				snake_case_		: str=16			,				snake_case_		: Any=[1, 2, 1]			,				snake_case_		: Optional[int]=[2, 2, 4]			,				snake_case_		: Any=2			,				snake_case_		: Optional[Any]=2.0			,				snake_case_		: Any=True			,				snake_case_		: Tuple=0.0			,				snake_case_		: Any=0.0			,				snake_case_		: Any=0.1			,				snake_case_		: Optional[Any]="gelu"			,				snake_case_		: Union[str, Any]=False			,				snake_case_		: Tuple=True			,				snake_case_		: Dict=0.02			,				snake_case_		: List[str]=1e-5			,				snake_case_		: Optional[int]=True			,				snake_case_		: Tuple=None			,				snake_case_		: Any=True			,				snake_case_		: str=10			,				snake_case_		: Dict=8			,				)   ->							None:
  
  
  
  
       '''simple docstring'''
  
       A__ =       parent
       A__ =       batch_size
       A__ =       image_size
       A__ =       patch_size
       A__ =       num_channels
       A__ =       embed_dim
       A__ =       depths
       A__ =       num_heads
       A__ =       window_size
       A__ =       mlp_ratio
       A__ =       qkv_bias
       A__ =       hidden_dropout_prob
       A__ =       attention_probs_dropout_prob
       A__ =       drop_path_rate
       A__ =       hidden_act
       A__ =       use_absolute_embeddings
       A__ =       patch_norm
       A__ =       layer_norm_eps
       A__ =       initializer_range
       A__ =       is_training
       A__ =       scope
       A__ =       use_labels
       A__ =       type_sequence_label_size
       A__ =       encoder_stride
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
       A__ =       None
       if self.use_labels:
        A__ =       ids_tensor([self.batch_size]			,				self.type_sequence_label_size   )
  
       A__ =       self.get_config()
  
       return config, pixel_values, labels
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[Any]:
  
  
  
  
       '''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			,				)
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Any			,				snake_case_		: Optional[Any]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       TFSwinModel(config=snake_case_   )
       A__ =       model(snake_case_   )
  
       A__ =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
       A__ =       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 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: Optional[int]			,				snake_case_		: List[Any]			,				snake_case_		: List[Any]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       TFSwinForMaskedImageModeling(config=snake_case_   )
       A__ =       model(snake_case_   )
       self.parent.assertEqual(
           result.logits.shape			,				(self.batch_size, self.num_channels, self.image_size, self.image_size)   )
  
       # test greyscale images
       A__ =       1
       A__ =       TFSwinForMaskedImageModeling(snake_case_   )
  
       A__ =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       A__ =       model(snake_case_   )
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, 1, self.image_size, self.image_size)   )
  
  
  
  
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: str			,				snake_case_		: Optional[Any]			,				snake_case_		: str   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.type_sequence_label_size
       A__ =       TFSwinForImageClassification(snake_case_   )
       A__ =       model(snake_case_			,				labels=snake_case_   )
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size)   )
  
       # test greyscale images
       A__ =       1
       A__ =       TFSwinForImageClassification(snake_case_   )
       A__ =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       A__ =       model(snake_case_   )
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size)   )
  
  
  
  
      def 							__magic_name__							(					self		: Tuple   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.prepare_config_and_inputs()
       A__,   A__,   A__ =       config_and_inputs
       A__ =       {"pixel_values": pixel_values}
       return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						(		A_,     A_,     unittest.TestCase							):
      lowercase__		      =   (
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      lowercase__		      =   (
          {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )
  
      lowercase__		      =   False
      lowercase__		      =   False
      lowercase__		      =   False
      lowercase__		      =   False
  
  
  
  
      def 							__magic_name__							(					self		: int   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       TFSwinModelTester(self   )
       A__ =       ConfigTester(self			,				config_class=snake_case_			,				embed_dim=37   )
  
  
  
  
      def 							__magic_name__							(					self		: int   )   ->							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 							__magic_name__							(					self		: Optional[Any]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       return
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       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 							__magic_name__							(					self		: Optional[int]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       pass
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__,   A__ =       self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
        A__ =       model_class(snake_case_   )
        self.assertIsInstance(model.get_input_embeddings()			,				tf.keras.layers.Layer   )
        A__ =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(snake_case_			,				tf.keras.layers.Dense   )   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__,   A__ =       self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
        A__ =       model_class(snake_case_   )
        A__ =       inspect.signature(model.call   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        A__ =       [*signature.parameters.keys()]
  
        A__ =       ["pixel_values"]
        self.assertListEqual(arg_names[:1]			,				snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: str   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__,   A__ =       self.model_tester.prepare_config_and_inputs_for_common()
       A__ =       True
  
       for model_class in self.all_model_classes:
        A__ =       True
        A__ =       False
        A__ =       True
        A__ =       model_class(snake_case_   )
        A__ =       model(**self._prepare_for_class(snake_case_			,				snake_case_   )   )
        A__ =       outputs.attentions
        A__ =       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"]
        A__ =       True
        A__ =       config.window_size**2
        A__ =       model_class(snake_case_   )
        A__ =       model(**self._prepare_for_class(snake_case_			,				snake_case_   )   )
        A__ =       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]			,				)
        A__ =       len(snake_case_   )
  
        # Check attention is always last and order is fine
        A__ =       True
        A__ =       True
        A__ =       model_class(snake_case_   )
        A__ =       model(**self._prepare_for_class(snake_case_			,				snake_case_   )   )
  
        if hasattr(self.model_tester			,				"num_hidden_states_types"   ):
         A__ =       self.model_tester.num_hidden_states_types
        else:
         # also another +1 for reshaped_hidden_states
         A__ =       2
        self.assertEqual(out_len + added_hidden_states			,				len(snake_case_   )   )
  
        A__ =       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 							__magic_name__							(					self		: Any			,				snake_case_		: int			,				snake_case_		: Optional[Any]			,				snake_case_		: Any			,				snake_case_		: Optional[Any]   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       model_class(snake_case_   )
       A__ =       model(**self._prepare_for_class(snake_case_			,				snake_case_   )   )
       A__ =       outputs.hidden_states
  
       A__ =       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
       A__ =       to_atuple(config.patch_size   )
  
       A__ =       (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]			,				)
  
       A__ =       outputs.reshaped_hidden_states
       self.assertEqual(len(snake_case_   )			,				snake_case_   )
  
       A__,   A__,   A__,   A__ =       reshaped_hidden_states[0].shape
  
       A__ =       tf.reshape(reshaped_hidden_states[0]			,				(batch_size, num_channels, height * width)   )
       A__ =       tf.transpose(snake_case_			,				(0, 2, 1)   )
  
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]   )			,				[num_patches, self.model_tester.embed_dim]			,				)
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__,   A__ =       self.model_tester.prepare_config_and_inputs_for_common()
  
       A__ =       to_atuple(self.model_tester.image_size   )
  
       for model_class in self.all_model_classes:
        A__ =       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"]
        A__ =       True
  
        self.check_hidden_states_output(snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__,   A__ =       self.model_tester.prepare_config_and_inputs_for_common()
       A__ =       3
  
       A__ =       to_atuple(self.model_tester.image_size   )
       A__ =       to_atuple(config.patch_size   )
  
       A__ =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       A__ =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
       for model_class in self.all_model_classes:
        A__ =       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"]
        A__ =       True
        self.check_hidden_states_output(snake_case_			,				snake_case_			,				snake_case_			,				(padded_height, padded_width)   )
  
  
  
  
      @slow
      def 							__magic_name__							(					self		: Optional[int]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        A__ =       TFSwinModel.from_pretrained(snake_case_   )
        self.assertIsNotNone(snake_case_   )
  
  
  
  
  
  
  @require_vision
  @require_tf
  class 			UpperCAmelCase_						(		unittest.TestCase							):
  
  
  
  
      @cached_property
      def 							__magic_name__							(					self		: Any   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       return (
           AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   )
           if is_vision_available()
           else None
       )
  
  
  
  
      @slow
      def 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   )
       A__ =       self.default_image_processor
  
       A__ =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
       A__ =       image_processor(images=snake_case_			,				return_tensors="tf"   )
  
       # forward pass
       A__ =       model(snake_case_   )
  
       # verify the logits
       A__ =       tf.TensorShape((1, 1_000)   )
       self.assertEqual(outputs.logits.shape			,				snake_case_   )
       A__ =       tf.constant([-0.0948, -0.6454, -0.0921]   )
       self.assertTrue(np.allclose(outputs.logits[0, :3]			,				snake_case_			,				atol=1e-4   )   )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument(
            "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
        )
        parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
        parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
        parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  