





"""simple docstring"""




import argparse

import torch

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


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





def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Dict						     =					WavLMForSequenceClassification.from_pretrained(_lowercase ,		config=_lowercase     )
						_UpperCamelCase    :      Any						     =					downstream_dict['''projector.weight''']
						_UpperCamelCase    :      Optional[int]						     =					downstream_dict['''projector.bias''']
						_UpperCamelCase    :      List[str]						     =					downstream_dict['''model.post_net.linear.weight''']
						_UpperCamelCase    :      str						     =					downstream_dict['''model.post_net.linear.bias''']
						return model





def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					WavLMForAudioFrameClassification.from_pretrained(_lowercase ,		config=_lowercase     )
						_UpperCamelCase    :      str						     =					downstream_dict['''model.linear.weight''']
						_UpperCamelCase    :      str						     =					downstream_dict['''model.linear.bias''']
						return model





def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      int						     =					WavLMForXVector.from_pretrained(_lowercase ,		config=_lowercase     )
						_UpperCamelCase    :      Union[str, Any]						     =					downstream_dict['''connector.weight''']
						_UpperCamelCase    :      Optional[int]						     =					downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
												_UpperCamelCase    :      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    :      Any						     =					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						_UpperCamelCase    :      Any						     =					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						_UpperCamelCase    :      str						     =					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						_UpperCamelCase    :      Tuple						     =					downstream_dict['''objective.W''']
						return model





@torch.no_grad()
def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[Any]						     =					torch.load(_lowercase ,		map_location='''cpu'''     )

						_UpperCamelCase    :      Any						     =					checkpoint['''Downstream''']

						_UpperCamelCase    :      int						     =					WavLMConfig.from_pretrained(_lowercase     )
						_UpperCamelCase    :      Dict						     =					WavaVecaFeatureExtractor.from_pretrained(
						    _lowercase ,		return_attention_mask=_lowercase ,		do_normalize=_lowercase     )

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

						if hf_config.use_weighted_layer_sum:
												_UpperCamelCase    :      int						     =					checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(_lowercase     )
						hf_model.save_pretrained(_lowercase     )


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










"""simple docstring"""




def  a_       (							_lowercase ,		_lowercase     ):
						sequence.append(_lowercase     )

						_UpperCamelCase    :      int						     =					0
						while sequence[index] != target:
												index += 1

						sequence.pop()

						if index == len(_lowercase     ):
												return None

						return index


if __name__ == "__main__":
						UpperCamelCase_     =input("""Enter numbers separated by comma:\n""").strip()
						UpperCamelCase_     =[int(item) for item in user_input.split(""",""")]

						UpperCamelCase_     =input("""Enter a single number to be found in the list:\n""")
						UpperCamelCase_     =int(target_input)
						UpperCamelCase_     =sentinel_linear_search(sequence, target)
						if result is not None:
												print(F"{target} found at positions: {result}")
						else:
												print("""Not found""")



