

from collections import OrderedDict
from typing import Mapping

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


_a			=       logging.get_logger(__name__)

_a			=       {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}






class 							A_			(		snake_case__ ):
     _lowercase      :					Optional[int]       =     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     _lowercase      :					Optional[int]       =     'squeezebert'


     def __init__(							self      :		Tuple      ,  UpperCAmelCase      :		Any=3_0_5_2_2      ,  UpperCAmelCase      :		List[Any]=7_6_8      ,  UpperCAmelCase      :		int=1_2      ,  UpperCAmelCase      :		Tuple=1_2      ,  UpperCAmelCase      :		Union[str, Any]=3_0_7_2      ,  UpperCAmelCase      :		List[str]="gelu"      ,  UpperCAmelCase      :		Dict=0.1      ,  UpperCAmelCase      :		List[Any]=0.1      ,  UpperCAmelCase      :		List[str]=5_1_2      ,  UpperCAmelCase      :		str=2      ,  UpperCAmelCase      :		int=0.02      ,  UpperCAmelCase      :		Any=1E-12      ,  UpperCAmelCase      :		int=0      ,  UpperCAmelCase      :		Dict=7_6_8      ,  UpperCAmelCase      :		List[Any]=4      ,  UpperCAmelCase      :		int=4      ,  UpperCAmelCase      :		Union[str, Any]=4      ,  UpperCAmelCase      :		Tuple=1      ,  UpperCAmelCase      :		Optional[int]=4      ,  UpperCAmelCase      :		Any=4      ,  **UpperCAmelCase      :		Optional[int]      ,  )							->     Dict:
      super().__init__(pad_token_id=UpperCAmelCase      ,  **UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				vocab_size
      __lowerCAmelCase:       int            =				hidden_size
      __lowerCAmelCase:       Optional[int]            =				num_hidden_layers
      __lowerCAmelCase:       Any            =				num_attention_heads
      __lowerCAmelCase:       int            =				hidden_act
      __lowerCAmelCase:       Any            =				intermediate_size
      __lowerCAmelCase:       Tuple            =				hidden_dropout_prob
      __lowerCAmelCase:       List[Any]            =				attention_probs_dropout_prob
      __lowerCAmelCase:       Dict            =				max_position_embeddings
      __lowerCAmelCase:       Any            =				type_vocab_size
      __lowerCAmelCase:       Dict            =				initializer_range
      __lowerCAmelCase:       Optional[Any]            =				layer_norm_eps
      __lowerCAmelCase:       List[Any]            =				embedding_size
      __lowerCAmelCase:       List[Any]            =				q_groups
      __lowerCAmelCase:       Tuple            =				k_groups
      __lowerCAmelCase:       Dict            =				v_groups
      __lowerCAmelCase:       Dict            =				post_attention_groups
      __lowerCAmelCase:       List[str]            =				intermediate_groups
      __lowerCAmelCase:       Optional[Any]            =				output_groups







class 							A_			(		snake_case__ ):


     @property
     def   UpperCAmelCase  (							self      :		Dict )							->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       __lowerCAmelCase:       int            =				{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       __lowerCAmelCase:       Optional[Any]            =				{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ] )








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a			=       ['''UtteranceLevel''']







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_SNAKE_CASE   , map_location='cpu'		)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  _a			=       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.''')
  _a			=       parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





