





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase       (						a__  ):
       _UpperCamelCase					:       Dict       = (VQDiffusionScheduler,)

       def lowercase			(		self:			List[Any]       , **_SCREAMING_SNAKE_CASE:			Optional[int]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	{
                  "num_vec_classes": 4097,
                  "num_train_timesteps": 100,
              }

              config.update(**_SCREAMING_SNAKE_CASE		)
              return config

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	8
              UpperCamelCase_ 							=	8

              UpperCamelCase_ 							=	torch.randint(0       , _SCREAMING_SNAKE_CASE       , (batch_size, height * width)		)

              return sample

       @property
       def lowercase			(		self:			List[str]		)							->			Any:




              """simple docstring"""


              assert False

       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			Union[str, Any]:




              """simple docstring"""


              def model(_SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			List[str]       , *_SCREAMING_SNAKE_CASE:			Optional[int]		):
                     UpperCamelCase_							,			UpperCamelCase_ 							=	sample.shape
                     UpperCamelCase_ 							=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)		)
                     UpperCamelCase_ 							=	F.log_softmax(logits.double()       , dim=1		).float()
                     return return_value

              return model

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




              """simple docstring"""


              for timesteps in [2, 5, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Any		)							->			Dict:




              """simple docstring"""


              for num_vec_classes in [5, 100, 1000, 4000]:
                     self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Tuple		)							->			Optional[Any]:




              """simple docstring"""


              for t in [0, 50, 99]:
                     self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			Dict		)							->			Optional[int]:




              """simple docstring"""


              pass





'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							convert_xvector(__A ,     __A ,     __A       )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''       )

      if hf_config.use_weighted_layer_sum:
            UpperCamelCase__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			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.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





