


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE :					int			=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :					List[Any]			=	["""UtteranceLevel"""]







@torch.no_grad()
def 						_lowercase       (     __lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Union[str, Any]			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     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}'			)

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

     UpperCamelCase__							:  Any								=     HubertConfig.from_pretrained(__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     HubertForSequenceClassification.from_pretrained(__lowerCamelCase		,config=__lowerCamelCase			)
     UpperCamelCase__							:  Tuple								=     WavaVecaFeatureExtractor.from_pretrained(
         __lowerCamelCase		,return_attention_mask=__lowerCamelCase		,do_normalize=__lowerCamelCase			)

     if hf_congfig.use_weighted_layer_sum:
          UpperCamelCase__							:  Union[str, Any]								=     checkpoint['''Featurizer''']['''weights''']

     UpperCamelCase__							:  int								=     downstream_dict['''projector.weight''']
     UpperCamelCase__							:  str								=     downstream_dict['''projector.bias''']
     UpperCamelCase__							:  List[Any]								=     downstream_dict['''model.post_net.linear.weight''']
     UpperCamelCase__							:  List[str]								=     downstream_dict['''model.post_net.linear.bias''']

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE :					List[str]			=	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[int]			=	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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE :					Tuple			=	{
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Any			=	["""MarianTokenizer"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	[
       """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MarianForCausalLM""",
       """MarianModel""",
       """MarianMTModel""",
       """MarianPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

   _SCREAMING_SNAKE_CASE :					List[Any]			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




