

import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     Union[str, Any]       )      ->				Union[str, Any]:
				_UpperCAmelCase				:						Dict       						=			WavLMForSequenceClassification.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						List[Any]       						=			downstream_dict["projector.weight"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["projector.bias"]
				_UpperCAmelCase				:						Union[str, Any]       						=			downstream_dict["model.post_net.linear.weight"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["model.post_net.linear.bias"]
				return model

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[Any]     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Optional[Any]       )      ->				Dict:
				_UpperCAmelCase				:						Optional[int]       						=			WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						int       						=			downstream_dict["model.linear.weight"]
				_UpperCAmelCase				:						str       						=			downstream_dict["model.linear.bias"]
				return model

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[Any]     ,					lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Optional[Any]       )      ->				Optional[int]:
				_UpperCAmelCase				:						str       						=			WavLMForXVector.from_pretrained(lowerCAmelCase     ,					config=lowerCAmelCase       )
				_UpperCAmelCase				:						Tuple       						=			downstream_dict["connector.weight"]
				_UpperCAmelCase				:						str       						=			downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
								_UpperCAmelCase				:						List[str]       						=			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				:						Tuple       						=			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
				_UpperCAmelCase				:						Dict       						=			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
				_UpperCAmelCase				:						Optional[Any]       						=			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
				_UpperCAmelCase				:						Tuple       						=			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
				_UpperCAmelCase				:						Optional[int]       						=			downstream_dict["objective.W"]
				return model

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     str     ,					lowerCAmelCase:     Dict     ,					lowerCAmelCase:     int       )      ->				Optional[Any]:
				_UpperCAmelCase				:						str       						=			torch.load(lowerCAmelCase     ,					map_location="cpu"       )

				_UpperCAmelCase				:						Any       						=			checkpoint["Downstream"]

				_UpperCAmelCase				:						Optional[int]       						=			WavLMConfig.from_pretrained(lowerCAmelCase       )
				_UpperCAmelCase				:						int       						=			WavaVecaFeatureExtractor.from_pretrained(
				    lowerCAmelCase     ,					return_attention_mask=lowerCAmelCase     ,					do_normalize=lowerCAmelCase       )

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

				if hf_config.use_weighted_layer_sum:
								_UpperCAmelCase				:						List[str]       						=			checkpoint["Featurizer"]["weights"]

				hf_feature_extractor.save_pretrained(lowerCAmelCase       )
				hf_model.save_pretrained(lowerCAmelCase       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					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_						=					parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



"""simple docstring"""


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


a							=TypeVar('T', bound='TaskTemplate')






@dataclass(frozen=__lowerCAmelCase	)
class    __UpperCAmelCase						(	abc.ABC	):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				A__      :   str
				A__      :   ClassVar[Features]
				A__      :   ClassVar[Features]

				def 		_a	(      self	, _lowerCamelCase      ):
										return copy.deepcopy(self      )

				@property
				def 		_a	(      self      ):
										return Features(**self.input_schema	, **self.label_schema      )

				@property
				@abc.abstractmethod
				def 		_a	(      self      ):
										raise NotImplementedError






				@classmethod
				def 		_a	(      cls	, _lowerCamelCase      ):
										lowerCamelCase__					={f.name for f in dataclasses.fields(cls      )}
										return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )


