"""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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> Any:
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> List[Any]:
     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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     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)


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

