



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						WavLMForSequenceClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["projector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["projector.bias"]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.post_net.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.post_net.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["model.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						downstream_dict["model.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMForXVector.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						downstream_dict["connector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
  __SCREAMING_SNAKE_CASE							:     str							=						downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  str		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						torch.load(__lowerCamelCase		,						map_location="cpu"  )

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						checkpoint["Downstream"]

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMConfig.from_pretrained(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						WavaVecaFeatureExtractor.from_pretrained(
     __lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						do_normalize=__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"  ):
  __SCREAMING_SNAKE_CASE							:     Tuple							=						convert_classification(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 elif arch.endswith("ForAudioFrameClassification"  ):
  __SCREAMING_SNAKE_CASE							:     int							=						convert_diarization(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 elif arch.endswith("ForXVector"  ):
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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							:     int							=						checkpoint["Featurizer"]["weights"]

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


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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)