




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     : Optional[int]      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Union[str, Any]      =["""UtteranceLevel"""]

@torch.no_grad()
def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							Optional[Any]  ,					lowerCamelCase:							Dict    )       ->      Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					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(lowerCamelCase    )
      lowerCamelCase__   	=					HubertForSequenceClassification.from_pretrained(lowerCamelCase  ,					config=lowerCamelCase    )
      lowerCamelCase__   	=					WavaVecaFeatureExtractor.from_pretrained(
          lowerCamelCase  ,					return_attention_mask=lowerCamelCase  ,					do_normalize=lowerCamelCase    )

      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(lowerCamelCase    )
      hf_model.save_pretrained(lowerCamelCase    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =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     : Optional[Any]      =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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)