




'''simple docstring'''




import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


snake_case__ 							=    logging.get_logger(__name__)






class      UpperCamelCase_  (_lowerCamelCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'hybrid-clip'
						_lowerCAmelCase         =       True







						def __init__(  self   :							str				,			_lowerCamelCase   :							List[str]=512				,			**_lowerCamelCase   :							Optional[int]     ):





											"""simple docstring"""







											super().__init__(**lowerCAmelCase_     )

											if "text_config" not in kwargs:
																raise ValueError('''`text_config` can not be `None`.'''     )

											if "vision_config" not in kwargs:
																raise ValueError('''`vision_config` can not be `None`.'''     )

											A_			:  Any			=				kwargs.pop('''text_config'''     )
											A_			:  int			=				kwargs.pop('''vision_config'''     )

											A_			:  Optional[Any]			=				text_config.pop('''model_type'''     )
											A_			:  Optional[int]			=				vision_config.pop('''model_type'''     )

											from transformers import AutoConfig

											A_			:  List[str]			=				AutoConfig.for_model(lowerCAmelCase_				,			**lowerCAmelCase_     )

											if vision_model_type == "clip":
																A_			:  Optional[int]			=				AutoConfig.for_model(lowerCAmelCase_				,			**lowerCAmelCase_     ).vision_config
											elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																A_			:  str			=				CLIPVisionConfig(**lowerCAmelCase_     )
											else:
																A_			:  str			=				AutoConfig.for_model(lowerCAmelCase_				,			**lowerCAmelCase_     )

											A_			:  Any			=				projection_dim
											A_			:  Optional[Any]			=				1.0







						@classmethod
						def    _a     (  cls   :							int				,			_lowerCamelCase   :							int				,			_lowerCamelCase   :							Any				,			**_lowerCamelCase   :							Union[str, Any]     ):





											"""simple docstring"""







											return cls(text_config=text_config.to_dict()				,			vision_config=vision_config.to_dict()				,			**lowerCAmelCase_     )







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											A_			:  List[Any]			=				copy.deepcopy(self.__dict__     )
											A_			:  Dict			=				self.text_config.to_dict()
											A_			:  Optional[int]			=				self.vision_config.to_dict()
											A_			:  int			=				self.__class__.model_type
											return output










import argparse

import torch

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


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

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

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

					hf_feature_extractor.save_pretrained(UpperCamelCase__    )
					hf_model.save_pretrained(UpperCamelCase__    )


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)



