
import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase_						: Any         =  logging.get_logger(__name__)







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Any	=						UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase     ,    config=lowerCamelCase						)
				UpperCamelCase_:							Dict	=						downstream_dict["""projector.weight"""]
				UpperCamelCase_:							List[str]	=						downstream_dict["""projector.bias"""]
				UpperCamelCase_:							Dict	=						downstream_dict["""model.post_net.linear.weight"""]
				UpperCamelCase_:							Optional[Any]	=						downstream_dict["""model.post_net.linear.bias"""]
				return model







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[Any]:
				UpperCamelCase_:							List[str]	=						UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase     ,    config=lowerCamelCase						)
				UpperCamelCase_:							Any	=						downstream_dict["""model.linear.weight"""]
				UpperCamelCase_:							Tuple	=						downstream_dict["""model.linear.bias"""]
				return model







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				UpperCamelCase_:							Optional[int]	=						UniSpeechSatForXVector.from_pretrained(lowerCamelCase     ,    config=lowerCamelCase						)
				UpperCamelCase_:							Union[str, Any]	=						downstream_dict["""connector.weight"""]
				UpperCamelCase_:							Tuple	=						downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
								UpperCamelCase_:							int	=						downstream_dict[
								    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								UpperCamelCase_:							List[str]	=						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

				UpperCamelCase_:							str	=						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				UpperCamelCase_:							Tuple	=						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				UpperCamelCase_:							List[Any]	=						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				UpperCamelCase_:							int	=						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				UpperCamelCase_:							Dict	=						downstream_dict["""objective.W"""]
				return model







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Optional[Any]	=						torch.load(lowerCamelCase     ,    map_location="""cpu"""						)

				UpperCamelCase_:							Any	=						checkpoint["""Downstream"""]

				UpperCamelCase_:							str	=						UniSpeechSatConfig.from_pretrained(lowerCamelCase						)
				UpperCamelCase_:							Any	=						WavaVecaFeatureExtractor.from_pretrained(
				    lowerCamelCase     ,    return_attention_mask=lowerCamelCase     ,    do_normalize=lowerCamelCase						)

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

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

				hf_feature_extractor.save_pretrained(lowerCamelCase						)
				hf_model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, 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)








import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCamelCase_						: Optional[int]         =  HUGGINGFACE_HUB_CACHE


lowerCamelCase_						: List[str]         =  """config.json"""
lowerCamelCase_						: Any         =  """diffusion_pytorch_model.bin"""
lowerCamelCase_						: Union[str, Any]         =  """diffusion_flax_model.msgpack"""
lowerCamelCase_						: Dict         =  """model.onnx"""
lowerCamelCase_						: List[Any]         =  """diffusion_pytorch_model.safetensors"""
lowerCamelCase_						: Optional[Any]         =  """weights.pb"""
lowerCamelCase_						: Optional[Any]         =  """https://huggingface.co"""
lowerCamelCase_						: Union[str, Any]         =  default_cache_path
lowerCamelCase_						: Tuple         =  """diffusers_modules"""
lowerCamelCase_						: Optional[Any]         =  os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowerCamelCase_						: str         =  ["""fp16""", """non-ema"""]
lowerCamelCase_						: List[Any]         =  """.self_attn"""






