






"""simple docstring"""







import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def _a			(		_snake_case							):







      """simple docstring"""






      return (torch.arange(state.num_processes							) + 1.0 + (state.num_processes * state.process_index)).to(state.device							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	gather(_snake_case							)
      assert gathered_tensor.tolist() == list(range(1				,	state.num_processes**2 + 1							)							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[state.process_index]
      UpperCAmelCase          =	gather_object(_snake_case							)
      assert len(_snake_case							) == state.num_processes, F'''{gathered_obj}, {len(_snake_case							)} != {state.num_processes}'''
      assert gathered_obj == list(range(state.num_processes							)							), F'''{gathered_obj} != {list(range(state.num_processes							)							)}'''

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	broadcast(_snake_case							)
      assert broadcasted_tensor.shape == torch.Size([state.num_processes]							)
      assert broadcasted_tensor.tolist() == list(range(1				,	state.num_processes + 1							)							)

def _a			(		_snake_case							):







      """simple docstring"""






      if state.is_main_process:
            UpperCAmelCase          =	torch.arange(state.num_processes + 1							).to(state.device							)
      else:
            UpperCAmelCase          =	torch.arange(state.num_processes							).to(state.device							)
      UpperCAmelCase          =	pad_across_processes(_snake_case							)
      assert padded_tensor.shape == torch.Size([state.num_processes + 1]							)
      if not state.is_main_process:
            assert padded_tensor.tolist() == list(range(0				,	state.num_processes							)							) + [0]

def _a			(		_snake_case							):







      """simple docstring"""






      if state.num_processes != 2:
            return
      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	reduce(_snake_case				,	"""sum"""							)
      UpperCAmelCase          =	torch.tensor([4.0, 6]							).to(state.device							)
      assert torch.allclose(_snake_case				,	_snake_case							), F'''{reduced_tensor} != {truth_tensor}'''

def _a			(		_snake_case							):







      """simple docstring"""






      if state.num_processes != 2:
            return
      UpperCAmelCase          =	create_tensor(_snake_case							)
      UpperCAmelCase          =	reduce(_snake_case				,	"""mean"""							)
      UpperCAmelCase          =	torch.tensor([2.0, 3]							).to(state.device							)
      assert torch.allclose(_snake_case				,	_snake_case							), F'''{reduced_tensor} != {truth_tensor}'''

def _a			(		_snake_case							):







      """simple docstring"""






      main()

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	PartialState()
      state.print(F'''State: {state}'''							)
      state.print("""testing gather"""							)
      test_gather(_snake_case							)
      state.print("""testing gather_object"""							)
      test_gather_object(_snake_case							)
      state.print("""testing broadcast"""							)
      test_broadcast(_snake_case							)
      state.print("""testing pad_across_processes"""							)
      test_pad_across_processes(_snake_case							)
      state.print("""testing reduce_sum"""							)
      test_reduce_sum(_snake_case							)
      state.print("""testing reduce_mean"""							)
      test_reduce_mean(_snake_case							)


if __name__ == "__main__":
  main()











"""simple docstring"""







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForAudioFrameClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""model.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

      UpperCAmelCase          =	hf_config.architectures[0]
      if arch.endswith("""ForSequenceClassification"""							):
            UpperCAmelCase          =	convert_classification(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForAudioFrameClassification"""							):
            UpperCAmelCase          =	convert_diarization(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForXVector"""							):
            UpperCAmelCase          =	convert_xvector(_snake_case				,	_snake_case				,	_snake_case							)
      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(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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.""")
  _UpperCamelCase					      =  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



