



import argparse

import torch

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


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

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		WavLMForSequenceClassification.from_pretrained(__a			,		config=__a		)
				a__					   =		downstream_dict["""projector.weight"""]
				a__					   =		downstream_dict["""projector.bias"""]
				a__					   =		downstream_dict["""model.post_net.linear.weight"""]
				a__					   =		downstream_dict["""model.post_net.linear.bias"""]
				return model

def       A_   (						__a  :					str			,		__a  :					List[str]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		WavLMForAudioFrameClassification.from_pretrained(__a			,		config=__a		)
				a__					   =		downstream_dict["""model.linear.weight"""]
				a__					   =		downstream_dict["""model.linear.bias"""]
				return model

def       A_   (						__a  :					Dict			,		__a  :					int			,		__a  :					Optional[Any]		):

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

				a__					   =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				a__					   =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				a__					   =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				a__					   =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				a__					   =		downstream_dict["""objective.W"""]
				return model

@torch.no_grad()
def       A_   (						__a  :					int			,		__a  :					str			,		__a  :					List[Any]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		torch.load(__a			,		map_location="""cpu"""		)

				a__					   =		checkpoint["""Downstream"""]

				a__					   =		WavLMConfig.from_pretrained(__a		)
				a__					   =		WavaVecaFeatureExtractor.from_pretrained(
				    __a			,		return_attention_mask=__a			,		do_normalize=__a		)

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

				if hf_config.use_weighted_layer_sum:
								a__					   =		checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(__a		)
				hf_model.save_pretrained(__a		)


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)



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

												if images is None and text is None:
																	raise ValueError('''You need to specify either an `images` or `text` input to process.''')

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												warnings.warn(
												    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
												    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
												    '''your images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




