


'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    lowercase_	(       a__  ):


    def __init__(	self    ,   a    ,   a       ):
          super().__init__()
          self.register_modules(unet=a    ,   scheduler=a       )


    @torch.no_grad()
    def __call__(	self    ,   a = 1    ,   a = None    ,   a = 10_00    ,   a = "pil"    ,   a = True    ,   ):
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size    ,   a       ):
                UpperCamelCase__      =							(
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size,
                    self.unet.config.sample_size,
                )
          else:
                UpperCamelCase__      =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
                # randn does not work reproducibly on mps
                UpperCamelCase__      =							randn_tensor(a    ,   generator=a       )
                UpperCamelCase__      =							image.to(self.device       )
          else:
                UpperCamelCase__      =							randn_tensor(a    ,   generator=a    ,   device=self.device       )

          # set step values
          self.scheduler.set_timesteps(a       )

          for t in self.progress_bar(self.scheduler.timesteps       ):
                # 1. predict noise model_output
                UpperCamelCase__      =							self.unet(a    ,   a       ).sample

                # 2. compute previous image: x_t -> x_t-1
                UpperCamelCase__      =							self.scheduler.step(a    ,   a    ,   a    ,   generator=a       ).prev_sample

          UpperCamelCase__      =							(image / 2 + 0.5).clamp(0    ,   1       )
          UpperCamelCase__      =							image.cpu().permute(0    ,   2    ,   3    ,   1       ).numpy()
          if output_type == "pil":
                UpperCamelCase__      =							self.numpy_to_pil(a       )

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=a       )









'''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)





