

'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Dict:
      '''simple docstring'''


      lowercase_     =      UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["projector.weight"]
      lowercase_     =      downstream_dict["projector.bias"]
      lowercase_     =      downstream_dict["model.post_net.linear.weight"]
      lowercase_     =      downstream_dict["model.post_net.linear.bias"]
      return model


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Any				)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["model.linear.weight"]
      lowercase_     =      downstream_dict["model.linear.bias"]
      return model


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						str				)	->							Any:
      '''simple docstring'''


      lowercase_     =      UniSpeechSatForXVector.from_pretrained(__lowerCamelCase ,		config=__lowerCamelCase				)
      lowercase_     =      downstream_dict["connector.weight"]
      lowercase_     =      downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
            lowercase_     =      downstream_dict[
                f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
            ]
            lowercase_     =      downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

      lowercase_     =      downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      lowercase_     =      downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      lowercase_     =      downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      lowercase_     =      downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      lowercase_     =      downstream_dict["objective.W"]
      return model


@torch.no_grad()
def __a (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


      lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      checkpoint["Downstream"]

      lowercase_     =      UniSpeechSatConfig.from_pretrained(__lowerCamelCase				)
      lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(
          __lowerCamelCase ,		return_attention_mask=__lowerCamelCase ,		do_normalize=__lowerCamelCase				)

      lowercase_     =      hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"				):
            lowercase_     =      convert_classification(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
      elif arch.endswith("ForAudioFrameClassification"				):
            lowercase_     =      convert_diarization(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
      elif arch.endswith("ForXVector"				):
            lowercase_     =      convert_xvector(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
      else:
            raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'				)

      if hf_config.use_weighted_layer_sum:
            lowercase_     =      checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__lowerCamelCase				)
      hf_model.save_pretrained(__lowerCamelCase				)


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







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






