


import argparse

import torch

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


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






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase,      config=__lowerCamelCase				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					downstream_dict['projector.weight']
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					downstream_dict['projector.bias']
			SCREAMING_SNAKE_CASE_		:     int					=					downstream_dict['model.post_net.linear.weight']
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					downstream_dict['model.post_net.linear.bias']
			return model






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase,      config=__lowerCamelCase				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					downstream_dict['model.linear.weight']
			SCREAMING_SNAKE_CASE_		:     List[str]					=					downstream_dict['model.linear.bias']
			return model






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					UniSpeechSatForXVector.from_pretrained(__lowerCamelCase,      config=__lowerCamelCase				)
			SCREAMING_SNAKE_CASE_		:     Any					=					downstream_dict['connector.weight']
			SCREAMING_SNAKE_CASE_		:     Any					=					downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						SCREAMING_SNAKE_CASE_		:     Any					=					downstream_dict[
						    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			SCREAMING_SNAKE_CASE_		:     Tuple					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			SCREAMING_SNAKE_CASE_		:     str					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			SCREAMING_SNAKE_CASE_		:     List[str]					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					downstream_dict['objective.W']
			return model






@torch.no_grad()
def 						a__			(				A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.load(__lowerCamelCase,      map_location='cpu'				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					checkpoint['Downstream']

			SCREAMING_SNAKE_CASE_		:     List[str]					=					UniSpeechSatConfig.from_pretrained(__lowerCamelCase				)
			SCREAMING_SNAKE_CASE_		:     Any					=					WavaVecaFeatureExtractor.from_pretrained(
			    __lowerCamelCase,      return_attention_mask=__lowerCamelCase,      do_normalize=__lowerCamelCase				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					hf_config.architectures[0]
			if arch.endswith('ForSequenceClassification'				):
						SCREAMING_SNAKE_CASE_		:     Dict					=					convert_classification(__lowerCamelCase,      __lowerCamelCase,      __lowerCamelCase				)
			elif arch.endswith('ForAudioFrameClassification'				):
						SCREAMING_SNAKE_CASE_		:     List[str]					=					convert_diarization(__lowerCamelCase,      __lowerCamelCase,      __lowerCamelCase				)
			elif arch.endswith('ForXVector'				):
						SCREAMING_SNAKE_CASE_		:     List[str]					=					convert_xvector(__lowerCamelCase,      __lowerCamelCase,      __lowerCamelCase				)
			else:
						raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''				)

			if hf_config.use_weighted_layer_sum:
						SCREAMING_SNAKE_CASE_		:     Any					=					checkpoint['Featurizer']['weights']

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


if __name__ == "__main__":
				lowerCAmelCase__			:      Tuple								=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__			:      Any								=parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







'''simple docstring'''



from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip
