






"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()
__lowerCamelCase :      Tuple										=				logging.get_logger(__name__)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Optional[int]					,  snake_case_ :							List[Any]       ):
	snake_case__			:				int       =  UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase					,  config=_UpperCAmelCase       )
	snake_case__			:				Any       =  downstream_dict['projector.weight']
	snake_case__			:				Tuple       =  downstream_dict['projector.bias']
	snake_case__			:				str       =  downstream_dict['model.post_net.linear.weight']
	snake_case__			:				Dict       =  downstream_dict['model.post_net.linear.bias']
	return model







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							Optional[Any]					,  snake_case_ :							Union[str, Any]       ):
	snake_case__			:				Optional[Any]       =  UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase					,  config=_UpperCAmelCase       )
	snake_case__			:				List[Any]       =  downstream_dict['model.linear.weight']
	snake_case__			:				Optional[int]       =  downstream_dict['model.linear.bias']
	return model







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]					,  snake_case_ :							Union[str, Any]					,  snake_case_ :							List[Any]       ):
	snake_case__			:				Any       =  UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase					,  config=_UpperCAmelCase       )
	snake_case__			:				List[Any]       =  downstream_dict['connector.weight']
	snake_case__			:				Optional[Any]       =  downstream_dict['connector.bias']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
		snake_case__			:				Optional[Any]       =  downstream_dict[
		    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
		]
		snake_case__			:				Dict       =  downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

	snake_case__			:				List[str]       =  downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
	snake_case__			:				List[Any]       =  downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
	snake_case__			:				str       =  downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
	snake_case__			:				int       =  downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
	snake_case__			:				List[str]       =  downstream_dict['objective.W']
	return model







@torch.no_grad()
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							Optional[int]					,  snake_case_ :							Union[str, Any]					,  snake_case_ :							str       ):
	snake_case__			:				Any       =  torch.load(_UpperCAmelCase					,  map_location="cpu"       )

	snake_case__			:				Union[str, Any]       =  checkpoint['Downstream']

	snake_case__			:				int       =  UniSpeechSatConfig.from_pretrained(_UpperCAmelCase       )
	snake_case__			:				Dict       =  WavaVecaFeatureExtractor.from_pretrained(
	    _UpperCAmelCase					,  return_attention_mask=_UpperCAmelCase					,  do_normalize=_UpperCAmelCase       )

	snake_case__			:				Union[str, Any]       =  hf_config.architectures[0]
	if arch.endswith("ForSequenceClassification"       ):
		snake_case__			:				Optional[int]       =  convert_classification(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase       )
	elif arch.endswith("ForAudioFrameClassification"       ):
		snake_case__			:				Any       =  convert_diarization(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase       )
	elif arch.endswith("ForXVector"       ):
		snake_case__			:				Union[str, 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:
		snake_case__			:				Tuple       =  checkpoint['Featurizer']['weights']

	hf_feature_extractor.save_pretrained(_UpperCAmelCase       )
	hf_model.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
	__lowerCamelCase :      Optional[int]										=				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[str]										=				parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		PhobertTokenizer
					a_     =		False



					def 	_lowercase					( self					:  List[str]	):
						super().setUp()

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

						snake_case__			:				Tuple       =  os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["vocab_file"]	)
						snake_case__			:				Union[str, 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 	_lowercase					( self					:  List[str]							,		**__A					:  Any	):
						kwargs.update(self.special_tokens_map	)
						return PhobertTokenizer.from_pretrained(self.tmpdirname							,		**__A	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[Any]	):
						snake_case__			:				str       =  "Tôi là VinAI Research"
						snake_case__			:				int       =  "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
						return input_text, output_text






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

						snake_case__			:				Any       =  tokens + [tokenizer.unk_token]

						snake_case__			:				Any       =  [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	)



