import torch

from transformers import AutoTokenizer




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		Any	)			->				str:
        _UpperCamelCase  :							List[str]	    =	AutoTokenizer.from_pretrained(lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		List[str]	)			->				str:

        if isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							Any	    =	None
               for l in x:
                      _UpperCamelCase  :							List[str]	    =	self.tokenizer(
                          lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )
                      _UpperCamelCase  :							Optional[Any]	    =	torch.tensor([len(lowerCamelCase	)]	)
                      if d is not None:
                             for k in d.keys():
                                    _UpperCamelCase  :							Dict	    =	torch.cat((d[k], t[k])							,      0	)
                      else:
                             _UpperCamelCase  :							Optional[int]	    =	t

               _UpperCamelCase  :							Optional[int]	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'	)	)
               _UpperCamelCase  :							Dict	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'	)	)

        elif isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							int	    =	self.tokenizer(
                   lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )

        else:
               raise Exception(
                   'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                   ' strings` for supports are supported.'	)

        return d

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]=0.7_0	)			->				Optional[int]:
        _UpperCamelCase  :							Union[str, Any]	    =	[]
        for idx in range(len(W_query['input_ids']	)	):
               _UpperCamelCase  :							List[Any]	    =	range(p_start.shape[1]	)

               _UpperCamelCase  :							List[str]	    =	[]
               for start_id in start_indexes:
                      for end_id in end_indexes:
                             if start_id < end_id:
                                    output.append(
                                        (
                                            start_id,
                                            end_id,
                                            p_start[idx][start_id].item(),
                                            p_end[idx][end_id].item(),
                                        )	)

               output.sort(key=lambda lowerCamelCase	: (tup[2] * tup[3])							,      reverse=lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	[]
               for k in range(len(lowerCamelCase	)	):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	output[k][0], output[k][1]
                             _UpperCamelCase  :							Optional[Any]	    =	self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]	)
                             temp.append((decoded, output[k][2] * output[k][3])	)

               final_outputs.append(lowerCamelCase	)

        return final_outputs










'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







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

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	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"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							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.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

