

'''simple docstring'''





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_lowerCAmelCase      :    Tuple						       =    ["UtteranceLevel"]






@torch.no_grad()
def  UpperCamelCase_(							_snake_case     :						Dict ,  _snake_case     :						Optional[int] ,  _snake_case     :						Optional[int] ,  _snake_case     :						List[Any]			):




			"""simple docstring"""






			__a						   =torch.load(_snake_case ,  map_location='cpu'			)
			if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
						raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'			)

			__a						   =checkpoint['Downstream']

			__a						   =HubertConfig.from_pretrained(_snake_case			)
			__a						   =HubertForSequenceClassification.from_pretrained(_snake_case ,  config=_snake_case			)
			__a						   =WavaVecaFeatureExtractor.from_pretrained(
			    _snake_case ,  return_attention_mask=_snake_case ,  do_normalize=_snake_case			)

			if hf_congfig.use_weighted_layer_sum:
						__a						   =checkpoint['Featurizer']['weights']

			__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']

			hf_feature_extractor.save_pretrained(_snake_case			)
			hf_model.save_pretrained(_snake_case			)


if __name__ == "__main__":
						_lowerCAmelCase      :    Dict						       =    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      :    Optional[int]						       =    parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCAmelCase      :    List[Any]						       =    [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase      :    List[str]						       =    get_logger(__name__)


_lowerCAmelCase      :    Tuple						       =    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def  UpperCamelCase_(							_snake_case     :						dict			):




			"""simple docstring"""






			__a						   =task_template_dict.get('task'			)
			if task_name is None:
						logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_snake_case			)}'			)
						return None
			__a						   =NAMEaTEMPLATE.get(_snake_case			)
			return template.from_dict(_snake_case			)






