





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_           =	logging.get_logger(__name__)
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_				,	config=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   downstream_dict['''projector.weight''']
   _UpperCAmelCase           =   downstream_dict['''projector.bias''']
   _UpperCAmelCase           =   downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.post_net.linear.bias''']
   return model
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]						)  ->  Dict:






   """simple docstring"""







   _UpperCAmelCase           =   WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_				,	config=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   downstream_dict['''model.linear.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.linear.bias''']
   return model
def      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      Union[str, Any]						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE_				,	config=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   downstream_dict['''connector.weight''']
   _UpperCAmelCase           =   downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      _UpperCAmelCase           =   downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _UpperCAmelCase           =   downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase           =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase           =   downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   torch.load(SCREAMING_SNAKE_CASE_				,	map_location='''cpu'''						)

   _UpperCAmelCase           =   checkpoint['''Downstream''']

   _UpperCAmelCase           =   WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_				,	return_attention_mask=SCREAMING_SNAKE_CASE_				,	do_normalize=SCREAMING_SNAKE_CASE_						)

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

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

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   UpperCAmelCase_           =	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.")
   UpperCAmelCase_           =	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_tf_available, is_torch_available


UpperCAmelCase_           =	{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "XLMForMultipleChoice",
       "XLMForQuestionAnswering",
       "XLMForQuestionAnsweringSimple",
       "XLMForSequenceClassification",
       "XLMForTokenClassification",
       "XLMModel",
       "XLMPreTrainedModel",
       "XLMWithLMHeadModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFXLMForMultipleChoice",
       "TFXLMForQuestionAnsweringSimple",
       "TFXLMForSequenceClassification",
       "TFXLMForTokenClassification",
       "TFXLMMainLayer",
       "TFXLMModel",
       "TFXLMPreTrainedModel",
       "TFXLMWithLMHeadModel",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   UpperCAmelCase_           =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)