




'''simple docstring'''


import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase       			= logging.get_logger(__name__)
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase     )       ->      List[str]:
     A:						str						=						UniSpeechSatForSequenceClassification.from_pretrained(__lowercase						,							config=__lowercase     )
     A:						List[Any]						=						downstream_dict['''projector.weight''']
     A:						Optional[Any]						=						downstream_dict['''projector.bias''']
     A:						List[Any]						=						downstream_dict['''model.post_net.linear.weight''']
     A:						int						=						downstream_dict['''model.post_net.linear.bias''']
     return model
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase     )       ->      Any:
     A:						List[Any]						=						UniSpeechSatForAudioFrameClassification.from_pretrained(__lowercase						,							config=__lowercase     )
     A:						Dict						=						downstream_dict['''model.linear.weight''']
     A:						Optional[Any]						=						downstream_dict['''model.linear.bias''']
     return model
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase     )       ->      Tuple:
     A:						Optional[Any]						=						UniSpeechSatForXVector.from_pretrained(__lowercase						,							config=__lowercase     )
     A:						Optional[Any]						=						downstream_dict['''connector.weight''']
     A:						List[Any]						=						downstream_dict['''connector.bias''']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
          A:						Tuple						=						downstream_dict[
              F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
          ]
          A:						Optional[int]						=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

     A:						List[Any]						=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
     A:						List[str]						=						downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
     A:						Optional[int]						=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
     A:						str						=						downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
     A:						List[Any]						=						downstream_dict['''objective.W''']
     return model
@torch.no_grad()
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase						,							__lowercase     )       ->      Optional[int]:
     A:						Dict						=						torch.load(__lowercase						,							map_location='''cpu'''     )

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

     A:						Tuple						=						UniSpeechSatConfig.from_pretrained(__lowercase     )
     A:						Tuple						=						WavaVecaFeatureExtractor.from_pretrained(
         __lowercase						,							return_attention_mask=__lowercase						,							do_normalize=__lowercase     )

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

     if hf_config.use_weighted_layer_sum:
          A:						List[Any]						=						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)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)