




"""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   )   )











"""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)





