




"""simple docstring"""





import argparse

import torch

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


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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase					,							config=UpperCamelCase				)
			__UpperCAmelCase						:			int												=       downstream_dict["projector.weight"]
			__UpperCAmelCase						:			List[Any]												=       downstream_dict["projector.bias"]
			__UpperCAmelCase						:			Optional[Any]												=       downstream_dict["model.post_net.linear.weight"]
			__UpperCAmelCase						:			List[Any]												=       downstream_dict["model.post_net.linear.bias"]
			return model

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase					,							config=UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       downstream_dict["model.linear.weight"]
			__UpperCAmelCase						:			Union[str, Any]												=       downstream_dict["model.linear.bias"]
			return model

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       WavaVecaForXVector.from_pretrained(UpperCamelCase					,							config=UpperCamelCase				)
			__UpperCAmelCase						:			Tuple												=       downstream_dict["connector.weight"]
			__UpperCAmelCase						:			str												=       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						:			Union[str, Any]												=       downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__UpperCAmelCase						:			Tuple												=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
			__UpperCAmelCase						:			Union[str, Any]												=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
			__UpperCAmelCase						:			Dict												=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
			__UpperCAmelCase						:			Union[str, Any]												=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
			__UpperCAmelCase						:			int												=       downstream_dict["objective.W"]
			return model

@torch.no_grad()
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       torch.load(UpperCamelCase					,							map_location="cpu"				)

			__UpperCAmelCase						:			Optional[Any]												=       checkpoint["Downstream"]

			__UpperCAmelCase						:			int												=       WavaVecaConfig.from_pretrained(UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       WavaVecaFeatureExtractor.from_pretrained(
			    UpperCamelCase					,							return_attention_mask=UpperCamelCase					,							do_normalize=UpperCamelCase				)

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

			if hf_config.use_weighted_layer_sum:
						__UpperCAmelCase						:			Optional[Any]												=       checkpoint["Featurizer"]["weights"]

			hf_feature_extractor.save_pretrained(UpperCamelCase				)
			hf_model.save_pretrained(UpperCamelCase				)


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




'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




