

"""simple docstring"""






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class      _UpperCAmelCase      (		__a     ,  unittest.TestCase):
			__a			:							Union[str, Any]					       =				PhobertTokenizer
			__a			:							Dict					       =				False
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				_UpperCAmelCase     :				Tuple          =       ["T@@", "i", "I", "R@@", "r", "e@@"]
				_UpperCAmelCase     :				Any          =       dict(zip(_A   ,		range(len(_A  )  )  )  )
				_UpperCAmelCase     :				Optional[Any]          =       ["#version: 0.2", "l à</w>"]
				_UpperCAmelCase     :				int          =       {"unk_token": "<unk>"}

				_UpperCAmelCase     :				List[str]          =       os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["""vocab_file"""]  )
				_UpperCAmelCase     :				Any          =       os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["""merges_file"""]  )

				with open(self.vocab_file   ,		"""w"""   ,		encoding="""utf-8"""  ) as fp:
					for token in vocab_tokens:
						fp.write(f'''{token} {vocab_tokens[token]}\n'''  )
				with open(self.merges_file   ,		"""w"""   ,		encoding="""utf-8"""  ) as fp:
					fp.write("""\n""".join(_A  )  )
			def 				__snake_case			(		self   ,		**_A  )     ->     Dict:






				'''simple docstring'''







				kwargs.update(self.special_tokens_map  )
				return PhobertTokenizer.from_pretrained(self.tmpdirname   ,		**_A  )
			def 				__snake_case			(		self   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       "Tôi là VinAI Research"
				_UpperCAmelCase     :				List[str]          =       "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
				return input_text, output_text
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       PhobertTokenizer(self.vocab_file   ,		self.merges_file   ,		**self.special_tokens_map  )
				_UpperCAmelCase     :				Tuple          =       "Tôi là VinAI Research"
				_UpperCAmelCase     :				int          =       "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
				_UpperCAmelCase     :				Optional[Any]          =       tokenizer.tokenize(_A  )
				print(_A  )
				self.assertListEqual(_A   ,		_A  )

				_UpperCAmelCase     :				List[str]          =       tokens + [tokenizer.unk_token]

				_UpperCAmelCase     :				List[str]          =       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )   ,		_A  )













'''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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

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

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  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)




