






import argparse

import torch

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


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

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   UniSpeechSatForSequenceClassification.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Tuple   =   downstream_dict['''projector.weight''']
				__magic_name__		:str   =   downstream_dict['''projector.bias''']
				__magic_name__		:Union[str, Any]   =   downstream_dict['''model.post_net.linear.weight''']
				__magic_name__		:Optional[int]   =   downstream_dict['''model.post_net.linear.bias''']
				return model

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Union[str, Any]   =   downstream_dict['''model.linear.weight''']
				__magic_name__		:List[Any]   =   downstream_dict['''model.linear.bias''']
				return model

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   UniSpeechSatForXVector.from_pretrained(snake_case,	config=snake_case			)
				__magic_name__		:Dict   =   downstream_dict['''connector.weight''']
				__magic_name__		:Optional[Any]   =   downstream_dict['''connector.bias''']
				for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
								__magic_name__		:Any   =   downstream_dict[
								    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								__magic_name__		:Any   =   downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

				__magic_name__		:Optional[int]   =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
				__magic_name__		:Dict   =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
				__magic_name__		:Tuple   =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
				__magic_name__		:Optional[Any]   =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
				__magic_name__		:Optional[int]   =   downstream_dict['''objective.W''']
				return model

@torch.no_grad()
def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   torch.load(snake_case,	map_location='''cpu'''			)

				__magic_name__		:Optional[Any]   =   checkpoint['''Downstream''']

				__magic_name__		:List[Any]   =   UniSpeechSatConfig.from_pretrained(snake_case			)
				__magic_name__		:Optional[int]   =   WavaVecaFeatureExtractor.from_pretrained(
				    snake_case,	return_attention_mask=snake_case,	do_normalize=snake_case			)

				__magic_name__		:Any   =   hf_config.architectures[0]
				if arch.endswith('''ForSequenceClassification'''			):
								__magic_name__		:Tuple   =   convert_classification(snake_case,	snake_case,	snake_case			)
				elif arch.endswith('''ForAudioFrameClassification'''			):
								__magic_name__		:int   =   convert_diarization(snake_case,	snake_case,	snake_case			)
				elif arch.endswith('''ForXVector'''			):
								__magic_name__		:Union[str, Any]   =   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:
								__magic_name__		:List[Any]   =   checkpoint['''Featurizer''']['''weights''']

				hf_feature_extractor.save_pretrained(snake_case			)
				hf_model.save_pretrained(snake_case			)


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














import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT			):
								with pytest.raises(snake_case			):
												requests.request('''GET''',	'''https://huggingface.co'''			)
								with pytest.raises(requests.exceptions.ConnectTimeout			):
												requests.request('''GET''',	'''https://huggingface.co''',	timeout=1.0			)

@pytest.mark.integration
def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.CONNECTION_FAILS			):
								with pytest.raises(requests.exceptions.ConnectionError			):
												requests.request('''GET''',	'''https://huggingface.co'''			)

def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1			):
								with pytest.raises(snake_case			):
												http_head('''https://huggingface.co'''			)






