


import argparse

import torch

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


logging.set_verbosity_info()
__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Dict     ):







	"""simple docstring"""



	UpperCamelCase__					:    Dict          =   WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    Optional[int]          =   downstream_dict['''projector.weight''']
	UpperCamelCase__					:    Optional[int]          =   downstream_dict['''projector.bias''']
	UpperCamelCase__					:    Dict          =   downstream_dict['''model.post_net.linear.weight''']
	UpperCamelCase__					:    Optional[Any]          =   downstream_dict['''model.post_net.linear.bias''']
	return model

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







	"""simple docstring"""



	UpperCamelCase__					:    str          =   WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    List[Any]          =   downstream_dict['''model.linear.weight''']
	UpperCamelCase__					:    Tuple          =   downstream_dict['''model.linear.bias''']
	return model

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[Any]     ):







	"""simple docstring"""



	UpperCamelCase__					:    Dict          =   WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE    , config=SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    Tuple          =   downstream_dict['''connector.weight''']
	UpperCamelCase__					:    Any          =   downstream_dict['''connector.bias''']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
		UpperCamelCase__					:    int          =   downstream_dict[
		    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
		]
		UpperCamelCase__					:    int          =   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

	UpperCamelCase__					:    List[Any]          =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
	UpperCamelCase__					:    Any          =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
	UpperCamelCase__					:    Dict          =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
	UpperCamelCase__					:    Any          =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
	UpperCamelCase__					:    Union[str, Any]          =   downstream_dict['''objective.W''']
	return model

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Any     ):







	"""simple docstring"""



	UpperCamelCase__					:    List[str]          =   torch.load(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )

	UpperCamelCase__					:    Dict          =   checkpoint['''Downstream''']

	UpperCamelCase__					:    Optional[Any]          =   WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
	UpperCamelCase__					:    Tuple          =   WavaVecaFeatureExtractor.from_pretrained(
	    SCREAMING_SNAKE_CASE    , return_attention_mask=SCREAMING_SNAKE_CASE    , do_normalize=SCREAMING_SNAKE_CASE     )

	UpperCamelCase__					:    List[str]          =   hf_config.architectures[0]
	if arch.endswith('''ForSequenceClassification'''     ):
		UpperCamelCase__					:    Dict          =   convert_classification(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
	elif arch.endswith('''ForAudioFrameClassification'''     ):
		UpperCamelCase__					:    Union[str, Any]          =   convert_diarization(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
	elif arch.endswith('''ForXVector'''     ):
		UpperCamelCase__					:    Any          =   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__					:    str          =   checkpoint['''Featurizer''']['''weights''']

	hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE     )
	hf_model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							__UpperCamelCase       :       Dict						       =				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       :       Dict						       =				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       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

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


