



import argparse

import torch

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


logging.set_verbosity_info()
snake_case     :    Optional[Any]						=    logging.get_logger(__name__)
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			WavLMForSequenceClassification.from_pretrained(_a				,  config=_a							)
						A__						:				Optional[int]         =			downstream_dict['projector.weight']
						A__						:				Optional[int]         =			downstream_dict['projector.bias']
						A__						:				Any         =			downstream_dict['model.post_net.linear.weight']
						A__						:				int         =			downstream_dict['model.post_net.linear.bias']
						return model
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Union[str, Any]:







						"""simple docstring"""

						A__						:				int         =			WavLMForAudioFrameClassification.from_pretrained(_a				,  config=_a							)
						A__						:				str         =			downstream_dict['model.linear.weight']
						A__						:				int         =			downstream_dict['model.linear.bias']
						return model
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

						A__						:				Tuple         =			WavLMForXVector.from_pretrained(_a				,  config=_a							)
						A__						:				List[str]         =			downstream_dict['connector.weight']
						A__						:				Optional[int]         =			downstream_dict['connector.bias']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
												A__						:				Union[str, Any]         =			downstream_dict[
												    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
												]
												A__						:				Any         =			downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

						A__						:				Union[str, Any]         =			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
						A__						:				Any         =			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
						A__						:				Tuple         =			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
						A__						:				Union[str, Any]         =			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
						A__						:				Optional[int]         =			downstream_dict['objective.W']
						return model
@torch.no_grad()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		List[Any]:







						"""simple docstring"""

						A__						:				Optional[Any]         =			torch.load(_a				,  map_location="cpu"							)

						A__						:				List[Any]         =			checkpoint['Downstream']

						A__						:				Union[str, Any]         =			WavLMConfig.from_pretrained(_a							)
						A__						:				int         =			WavaVecaFeatureExtractor.from_pretrained(
						    _a				,  return_attention_mask=_a				,  do_normalize=_a							)

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

						if hf_config.use_weighted_layer_sum:
												A__						:				Optional[Any]         =			checkpoint['Featurizer']['weights']

						hf_feature_extractor.save_pretrained(_a							)
						hf_model.save_pretrained(_a							)


if __name__ == "__main__":
		snake_case     :    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.')
		snake_case     :    Tuple						=    parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						bool = False      ):




    """simple docstring"""



    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(_a      )
    np.random.seed(_a      )
    torch.manual_seed(_a      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(_a      )
    elif is_npu_available():
        torch.npu.manual_seed_all(_a      )
    else:
        torch.cuda.manual_seed_all(_a      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(_a      )







def 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    if rng_type == RNGType.TORCH:
        __UpperCamelCase						:						Union[str, Any]		     =	torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __UpperCamelCase						:						int		     =	torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __UpperCamelCase						:						Dict		     =	torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __UpperCamelCase						:						Optional[Any]		     =	torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __UpperCamelCase						:						str		     =	torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __UpperCamelCase						:						List[Any]		     =	generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __UpperCamelCase						:						List[Any]		     =	AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __UpperCamelCase						:						Union[str, Any]		     =	rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __UpperCamelCase						:						str		     =	rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __UpperCamelCase						:						List[Any]		     =	rng_state.to(state.device      )
        torch.distributed.broadcast(_a					,  0      )
        __UpperCamelCase						:						Optional[Any]		     =	rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(_a					,  0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(_a      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(_a      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(_a      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(_a      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(_a      )







def 			_UpperCamelCase			(  _a						:						List[Union[str, RNGType]]					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    for rng_type in rng_types:
        synchronize_rng_state(RNGType(_a      )					,  generator=_a      )



