






"""simple docstring"""





import argparse

import torch

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


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






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							List[str]         =     WavLMForSequenceClassification.from_pretrained(snake_case_		,  config=snake_case_						)
       lowercase__:							Optional[Any]         =     downstream_dict["projector.weight"]
       lowercase__:							Dict         =     downstream_dict["projector.bias"]
       lowercase__:							int         =     downstream_dict["model.post_net.linear.weight"]
       lowercase__:							Union[str, Any]         =     downstream_dict["model.post_net.linear.bias"]
       return model






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     WavLMForAudioFrameClassification.from_pretrained(snake_case_		,  config=snake_case_						)
       lowercase__:							Dict         =     downstream_dict["model.linear.weight"]
       lowercase__:							str         =     downstream_dict["model.linear.bias"]
       return model






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





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

       lowercase__:							Optional[Any]         =     downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       lowercase__:							Union[str, Any]         =     downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       lowercase__:							List[Any]         =     downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       lowercase__:							Union[str, Any]         =     downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       lowercase__:							Tuple         =     downstream_dict["objective.W"]
       return model






@torch.no_grad()
def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     torch.load(snake_case_		,  map_location="cpu"						)

       lowercase__:							List[Any]         =     checkpoint["Downstream"]

       lowercase__:							str         =     WavLMConfig.from_pretrained(snake_case_						)
       lowercase__:							int         =     WavaVecaFeatureExtractor.from_pretrained(
           snake_case_		,  return_attention_mask=snake_case_		,  do_normalize=snake_case_						)

       lowercase__:							List[Any]         =     hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"						):
              lowercase__:							Dict         =     convert_classification(snake_case_		,  snake_case_		,  snake_case_						)
       elif arch.endswith("ForAudioFrameClassification"						):
              lowercase__:							Optional[Any]         =     convert_diarization(snake_case_		,  snake_case_		,  snake_case_						)
       elif arch.endswith("ForXVector"						):
              lowercase__:							Optional[int]         =     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:
              lowercase__:							int         =     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)










"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowercase          = logging.getLogger(__name__)






@dataclass
class 		_A    :







    """simple docstring"""




    UpperCAmelCase			:				str      =			field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
    UpperCAmelCase			:				bool      =			field(default=_a       ,metadata={"""help""": """Set this flag to use fast tokenization."""}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCAmelCase			:				Optional[str]      =			field(
        default=_a       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)






def 	lowercase     (							)-> Dict:
  '''simple docstring'''







  a		:						Tuple 				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

  if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    a				,						a				,						a		:						Any 				=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
  else:
    a				,						a				,						a		:						List[str] 				=     parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir			)
      and os.listdir(training_args.output_dir			)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        " --overwrite_output_dir to overcome."			)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,  )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,  training_args.local_rank    ,  training_args.device    ,  training_args.n_gpu    ,  bool(training_args.local_rank != -1			)    ,  training_args.fpaa    ,  )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"    ,  A_			)

  # Prepare Question-Answering task
  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  a		:						str 				=     AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  )
  a		:						str 				=     AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,  cache_dir=model_args.cache_dir    ,  use_fast=A_    ,  )
  a		:						List[Any] 				=     AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path    ,  from_tf=bool(".ckpt" in model_args.model_name_or_path			)    ,  config=A_    ,  cache_dir=model_args.cache_dir    ,  )

  # Get datasets
  a		:						int 				=     hasattr(model.config    ,  "lang2id"			)
  a		:						Any 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir			)
      if training_args.do_train
      else None
  )
  a		:						Tuple 				=     (
      SquadDataset(
          A_    ,  tokenizer=A_    ,  mode="dev"    ,  is_language_sensitive=A_    ,  cache_dir=model_args.cache_dir    ,  )
      if training_args.do_eval
      else None
  )

  # Data collator
  a		:						str 				=     DataCollatorWithPadding(A_    ,  pad_to_multiple_of=8			) if training_args.fpaa else None

  # Initialize our Trainer
  a		:						Tuple 				=     Trainer(
      model=A_    ,  args=A_    ,  train_dataset=A_    ,  eval_dataset=A_    ,  data_collator=A_    ,  )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir			)


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()






