
'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase__				:	List[Any]					=						logging.get_logger(__name__)

UpperCAmelCase__				:	Optional[int]					=						["UtteranceLevel"]







@torch.no_grad()
def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       List[str]  ,		UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       Tuple )   ->    Optional[Any]:







			'''simple docstring'''



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

			lowerCAmelCase__									=							checkpoint["Downstream"]

			lowerCAmelCase__									=							HubertConfig.from_pretrained(UpperCamelCase_ )
			lowerCAmelCase__									=							HubertForSequenceClassification.from_pretrained(UpperCamelCase_  ,		config=UpperCamelCase_ )
			lowerCAmelCase__									=							WavaVecaFeatureExtractor.from_pretrained(
			    UpperCamelCase_  ,		return_attention_mask=UpperCamelCase_  ,		do_normalize=UpperCamelCase_ )

			if hf_congfig.use_weighted_layer_sum:
						lowerCAmelCase__									=							checkpoint["Featurizer"]["weights"]

			lowerCAmelCase__									=							downstream_dict["projector.weight"]
			lowerCAmelCase__									=							downstream_dict["projector.bias"]
			lowerCAmelCase__									=							downstream_dict["model.post_net.linear.weight"]
			lowerCAmelCase__									=							downstream_dict["model.post_net.linear.bias"]

			hf_feature_extractor.save_pretrained(UpperCamelCase_ )
			hf_model.save_pretrained(UpperCamelCase_ )


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







'''simple docstring'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





