





'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_					:    Any      	=			logging.get_logger(__name__)






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Any						)	->	List[str]:
				_a								= WavaVecaForSequenceClassification.from_pretrained(a_    ,							config=a_						)
				_a								= downstream_dict["projector.weight"]
				_a								= downstream_dict["projector.bias"]
				_a								= downstream_dict["model.post_net.linear.weight"]
				_a								= downstream_dict["model.post_net.linear.bias"]
				return model






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= WavaVecaForAudioFrameClassification.from_pretrained(a_    ,							config=a_						)
				_a								= downstream_dict["model.linear.weight"]
				_a								= downstream_dict["model.linear.bias"]
				return model






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[Any]						)	->	int:
				_a								= WavaVecaForXVector.from_pretrained(a_    ,							config=a_						)
				_a								= downstream_dict["connector.weight"]
				_a								= downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
								_a								= downstream_dict[
								    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
								]
								_a								= downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

				_a								= downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
				_a								= downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
				_a								= downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
				_a								= downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
				_a								= downstream_dict["objective.W"]
				return model






@torch.no_grad()
def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Tuple						)	->	int:
				_a								= torch.load(a_    ,							map_location="cpu"						)

				_a								= checkpoint["Downstream"]

				_a								= WavaVecaConfig.from_pretrained(a_						)
				_a								= WavaVecaFeatureExtractor.from_pretrained(
				    a_    ,							return_attention_mask=a_    ,							do_normalize=a_						)

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

				if hf_config.use_weighted_layer_sum:
								_a								= checkpoint["Featurizer"]["weights"]

				hf_feature_extractor.save_pretrained(a_						)
				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase_					:    List[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.')
					lowerCAmelCase_					:    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_tf_available, is_torch_available


lowercase  							=					{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

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





