




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

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


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					WavaVecaForAudioFrameClassification.from_pretrained(lowerCamelCase_				,       config=lowerCamelCase_							)
			__UpperCAmelCase	: Tuple		=					downstream_dict['model.linear.weight']
			__UpperCAmelCase	: List[str]		=					downstream_dict['model.linear.bias']
			return model

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


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

			__UpperCAmelCase	: Any		=					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	: List[str]		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			__UpperCAmelCase	: str		=					downstream_dict['objective.W']
			return model

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


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					torch.load(lowerCamelCase_				,       map_location="cpu"							)

			__UpperCAmelCase	: Optional[int]		=					checkpoint['Downstream']

			__UpperCAmelCase	: int		=					WavaVecaConfig.from_pretrained(lowerCamelCase_							)
			__UpperCAmelCase	: str		=					WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_				,       return_attention_mask=lowerCamelCase_				,       do_normalize=lowerCamelCase_							)

			__UpperCAmelCase	: List[str]		=					hf_config.architectures[0]
			if arch.endswith("ForSequenceClassification"							):
						__UpperCAmelCase	: int		=					convert_classification(lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_							)
			elif arch.endswith("ForAudioFrameClassification"							):
						__UpperCAmelCase	: Union[str, Any]		=					convert_diarization(lowerCamelCase_				,       lowerCamelCase_				,       lowerCamelCase_							)
			elif arch.endswith("ForXVector"							):
						__UpperCAmelCase	: Optional[int]		=					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	: Optional[int]		=					checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(lowerCamelCase_							)
			hf_model.save_pretrained(lowerCamelCase_							)


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"""





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class      a__				:
			lowercase_    =	None


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)
						__UpperCAmelCase	: Any		=					json.loads(feat_extract.to_json_string())
						for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Dict		=					os.path.join(UpperCamelCase_      ,  "feat_extract.json")
									feat_extract_first.to_json_file(UpperCamelCase_)
									__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class.from_json_file(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Tuple		=					feat_extract_first.save_pretrained(UpperCamelCase_)[0]
									check_json_file_has_correct_format(UpperCamelCase_)
									__UpperCAmelCase	: List[Any]		=					self.feature_extraction_class.from_pretrained(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


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


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.feature_extraction_class()
						self.assertIsNotNone(UpperCamelCase_)


