




"""simple docstring"""






import argparse

import torch

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


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





def 							UpperCAmelCase		(		snake_case			:					Tuple      ,       snake_case			:					Tuple      ,       snake_case			:					List[str]    ):
					_lowerCAmelCase:Union[str, Any]									=  WavaVecaForSequenceClassification.from_pretrained(snake_case      ,       config=snake_case    )
					_lowerCAmelCase:int									=  downstream_dict['''projector.weight''']
					_lowerCAmelCase:List[str]									=  downstream_dict['''projector.bias''']
					_lowerCAmelCase:List[Any]									=  downstream_dict['''model.post_net.linear.weight''']
					_lowerCAmelCase:Dict									=  downstream_dict['''model.post_net.linear.bias''']
					return model





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Tuple      ,       snake_case			:					int    ):
					_lowerCAmelCase:Any									=  WavaVecaForAudioFrameClassification.from_pretrained(snake_case      ,       config=snake_case    )
					_lowerCAmelCase:Dict									=  downstream_dict['''model.linear.weight''']
					_lowerCAmelCase:Union[str, Any]									=  downstream_dict['''model.linear.bias''']
					return model





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Tuple      ,       snake_case			:					Union[str, Any]    ):
					_lowerCAmelCase:Union[str, Any]									=  WavaVecaForXVector.from_pretrained(snake_case      ,       config=snake_case    )
					_lowerCAmelCase:Tuple									=  downstream_dict['''connector.weight''']
					_lowerCAmelCase:str									=  downstream_dict['''connector.bias''']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_lowerCAmelCase:int									=  downstream_dict[
										    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
										]
										_lowerCAmelCase:Optional[int]									=  downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

					_lowerCAmelCase:List[Any]									=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
					_lowerCAmelCase:int									=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
					_lowerCAmelCase:List[str]									=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
					_lowerCAmelCase:Optional[Any]									=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
					_lowerCAmelCase:List[Any]									=  downstream_dict['''objective.W''']
					return model





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					Tuple      ,       snake_case			:					int      ,       snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Optional[int]									=  torch.load(snake_case      ,       map_location='''cpu'''    )

					_lowerCAmelCase:str									=  checkpoint['''Downstream''']

					_lowerCAmelCase:Union[str, Any]									=  WavaVecaConfig.from_pretrained(snake_case    )
					_lowerCAmelCase:Optional[Any]									=  WavaVecaFeatureExtractor.from_pretrained(
					    snake_case      ,       return_attention_mask=snake_case      ,       do_normalize=snake_case    )

					_lowerCAmelCase:Tuple									=  hf_config.architectures[0]
					if arch.endswith('''ForSequenceClassification'''    ):
										_lowerCAmelCase:Optional[int]									=  convert_classification(snake_case      ,       snake_case      ,       snake_case    )
					elif arch.endswith('''ForAudioFrameClassification'''    ):
										_lowerCAmelCase:Union[str, Any]									=  convert_diarization(snake_case      ,       snake_case      ,       snake_case    )
					elif arch.endswith('''ForXVector'''    ):
										_lowerCAmelCase:int									=  convert_xvector(snake_case      ,       snake_case      ,       snake_case    )
					else:
										raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'    )

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

					hf_feature_extractor.save_pretrained(snake_case    )
					hf_model.save_pretrained(snake_case    )


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"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=			{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			['''ConvBertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''ConvBertForMaskedLM''',
				    '''ConvBertForMultipleChoice''',
				    '''ConvBertForQuestionAnswering''',
				    '''ConvBertForSequenceClassification''',
				    '''ConvBertForTokenClassification''',
				    '''ConvBertLayer''',
				    '''ConvBertModel''',
				    '''ConvBertPreTrainedModel''',
				    '''load_tf_weights_in_convbert''',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFConvBertForMaskedLM''',
				    '''TFConvBertForMultipleChoice''',
				    '''TFConvBertForQuestionAnswering''',
				    '''TFConvBertForSequenceClassification''',
				    '''TFConvBertForTokenClassification''',
				    '''TFConvBertLayer''',
				    '''TFConvBertModel''',
				    '''TFConvBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
				from .tokenization_convbert import ConvBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_convbert_fast import ConvBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convbert import (
								    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvBertForMaskedLM,
								    ConvBertForMultipleChoice,
								    ConvBertForQuestionAnswering,
								    ConvBertForSequenceClassification,
								    ConvBertForTokenClassification,
								    ConvBertLayer,
								    ConvBertModel,
								    ConvBertPreTrainedModel,
								    load_tf_weights_in_convbert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convbert import (
								    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFConvBertForMaskedLM,
								    TFConvBertForMultipleChoice,
								    TFConvBertForQuestionAnswering,
								    TFConvBertForSequenceClassification,
								    TFConvBertForTokenClassification,
								    TFConvBertLayer,
								    TFConvBertModel,
								    TFConvBertPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






