"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
_UpperCamelCase     :		str											=logging.get_logger(__name__)





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       lowercase__    :	str     					=			WavaVecaForSequenceClassification.from_pretrained(A_     ,				config=A_  )
       lowercase__    :	int     					=			downstream_dict['projector.weight']
       lowercase__    :	List[Any]     					=			downstream_dict['projector.bias']
       lowercase__    :	Tuple     					=			downstream_dict['model.post_net.linear.weight']
       lowercase__    :	Dict     					=			downstream_dict['model.post_net.linear.bias']
       return model





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			WavaVecaForAudioFrameClassification.from_pretrained(A_     ,				config=A_  )
       lowercase__    :	List[Any]     					=			downstream_dict['model.linear.weight']
       lowercase__    :	List[Any]     					=			downstream_dict['model.linear.bias']
       return model





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	str     					=			WavaVecaForXVector.from_pretrained(A_     ,				config=A_  )
       lowercase__    :	Tuple     					=			downstream_dict['connector.weight']
       lowercase__    :	str     					=			downstream_dict['connector.bias']
       for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
              lowercase__    :	Union[str, Any]     					=			downstream_dict[
                  f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
              ]
              lowercase__    :	int     					=			downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

       lowercase__    :	Dict     					=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
       lowercase__    :	Optional[Any]     					=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
       lowercase__    :	Tuple     					=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
       lowercase__    :	Dict     					=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
       lowercase__    :	Optional[Any]     					=			downstream_dict['objective.W']
       return model





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				List[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			torch.load(A_     ,				map_location='cpu'  )

       lowercase__    :	List[Any]     					=			checkpoint['Downstream']

       lowercase__    :	Dict     					=			WavaVecaConfig.from_pretrained(A_  )
       lowercase__    :	List[Any]     					=			WavaVecaFeatureExtractor.from_pretrained(
           A_     ,				return_attention_mask=A_     ,				do_normalize=A_  )

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

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

       hf_feature_extractor.save_pretrained(A_  )
       hf_model.save_pretrained(A_  )


if __name__ == "__main__":
       _UpperCamelCase     :		Optional[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.")
       _UpperCamelCase     :		str											=parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

