



"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

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


A:   List[str]								=		logging.get_logger(__name__)



@dataclass
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Tuple						=						[
       'no_inference',
       'no_cuda',
       'no_tpu',
       'no_speed',
       'no_memory',
       'no_env_print',
       'no_multi_process',
   ]

   def __init__(   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    for deprecated_arg in self.deprecated_args:
     if deprecated_arg in kwargs:
      UpperCAmelCase	:					int	=	deprecated_arg[3:]
      setattr(self      ,     _SCREAMING_SNAKE_CASE      ,     not kwargs.pop(_SCREAMING_SNAKE_CASE						)						)
      logger.warning(
          F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
          F" {positive_arg}={kwargs[positive_arg]}"						)

    UpperCAmelCase	:					Any	=	kwargs.pop("""torchscript"""      ,     self.torchscript						)
    UpperCAmelCase	:					str	=	kwargs.pop("""torch_xla_tpu_print_metrics"""      ,     self.torch_xla_tpu_print_metrics						)
    UpperCAmelCase	:					Optional[Any]	=	kwargs.pop("""fp16_opt_level"""      ,     self.fpaa_opt_level						)
    super().__init__(**_SCREAMING_SNAKE_CASE						)

   __lowerCAmelCase  :   bool						=						field(default=UpperCAmelCase__      ,			metadata={'help': 'Trace the models using torchscript'} )
   __lowerCAmelCase  :   bool						=						field(default=UpperCAmelCase__      ,			metadata={'help': 'Print Xla/PyTorch tpu metrics'} )
   __lowerCAmelCase  :   str						=						field(
       default='O1'      ,			metadata={
           'help': (
               'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
               'See details at https://nvidia.github.io/apex/amp.html'
           )
       }      ,			)

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple["torch.device", int]:





    '''simple docstring'''




    requires_backends(self      ,     ["""torch"""]						)
    logger.info("""PyTorch: setting up devices"""						)
    if not self.cuda:
     UpperCAmelCase	:					Optional[int]	=	torch.device("""cpu"""						)
     UpperCAmelCase	:					Union[str, Any]	=	0
    elif is_torch_tpu_available():
     UpperCAmelCase	:					Tuple	=	xm.xla_device()
     UpperCAmelCase	:					Any	=	0
    else:
     UpperCAmelCase	:					Any	=	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
     UpperCAmelCase	:					Any	=	torch.cuda.device_count()
    return device, n_gpu

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return is_torch_tpu_available() and self.tpu

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    requires_backends(self      ,     ["""torch"""]						)
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      "torch.device":





    '''simple docstring'''




    requires_backends(self      ,     ["""torch"""]						)
    return self._setup_devices[0]

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    requires_backends(self      ,     ["""torch"""]						)
    return self._setup_devices[1]




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





    '''simple docstring'''




    return self.n_gpu > 0






import argparse

import torch

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


logging.set_verbosity_info()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

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


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      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					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

