



'''simple docstring'''







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     : Dict				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : Dict				=   {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE__     : Optional[Any]				=   {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

SCREAMING_SNAKE_CASE__     : Dict				=   {"""Helsinki-NLP/opus-mt-en-de""": 5_12}
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   {}




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		Any		=      VOCAB_FILES_NAMES
			__snake_case  :		Union[str, Any]		=      PRETRAINED_VOCAB_FILES_MAP
			__snake_case  :		List[str]		=      PRETRAINED_INIT_CONFIGURATION
			__snake_case  :		Dict		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case  :		Any		=      ['input_ids', 'attention_mask']
			__snake_case  :		List[Any]		=      re.compile('>>.+<<'      )  # type: re.Pattern






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int=None      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=None      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=None      ,       SCREAMING_SNAKE_CASE  :							Any="<unk>"      ,       SCREAMING_SNAKE_CASE  :							List[str]="</s>"      ,       SCREAMING_SNAKE_CASE  :							Dict="<pad>"      ,       SCREAMING_SNAKE_CASE  :							List[Any]=512      ,       SCREAMING_SNAKE_CASE  :							Optional[Dict[str, Any]] = None      ,       SCREAMING_SNAKE_CASE  :							Dict=False      ,       **SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=SCREAMING_SNAKE_CASE      ,       target_lang=SCREAMING_SNAKE_CASE      ,       unk_token=SCREAMING_SNAKE_CASE      ,       eos_token=SCREAMING_SNAKE_CASE      ,       pad_token=SCREAMING_SNAKE_CASE      ,       model_max_length=SCREAMING_SNAKE_CASE      ,       sp_model_kwargs=self.sp_model_kwargs      ,       target_vocab_file=SCREAMING_SNAKE_CASE      ,       separate_vocabs=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE      ,       )
					assert Path(SCREAMING_SNAKE_CASE	).exists(), f'cannot find spm source {source_spm}'

					SCREAMING_SNAKE_CASE_						:List[Any]										=      separate_vocabs
					SCREAMING_SNAKE_CASE_						:str										=      load_json(SCREAMING_SNAKE_CASE	)
					if self.unk_token not in self.encoder:
							raise KeyError('<unk> token must be in vocab'	)
					assert self.pad_token in self.encoder

					if separate_vocabs:
							SCREAMING_SNAKE_CASE_						:str										=      load_json(SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {v: k for k, v in self.target_encoder.items()}
							SCREAMING_SNAKE_CASE_						:Tuple										=      []
					else:
							SCREAMING_SNAKE_CASE_						:Dict										=      {v: k for k, v in self.encoder.items()}
							SCREAMING_SNAKE_CASE_						:list										=      [k for k in self.encoder if k.startswith('>>'	) and k.endswith('<<'	)]

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      source_lang
					SCREAMING_SNAKE_CASE_						:Dict										=      target_lang
					SCREAMING_SNAKE_CASE_						:int										=      [source_spm, target_spm]

					# load SentencePiece model for pre-processing
					SCREAMING_SNAKE_CASE_						:Dict										=      load_spm(SCREAMING_SNAKE_CASE      ,       self.sp_model_kwargs	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      load_spm(SCREAMING_SNAKE_CASE      ,       self.sp_model_kwargs	)
					SCREAMING_SNAKE_CASE_						:int										=      self.spm_source
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					try:
							from sacremoses import MosesPunctNormalizer

							SCREAMING_SNAKE_CASE_						:Dict										=      MosesPunctNormalizer(self.source_lang	).normalize
					except (ImportError, FileNotFoundError):
							warnings.warn('Recommended: pip install sacremoses.'	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      lambda SCREAMING_SNAKE_CASE	: x






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					return self.punc_normalizer(SCREAMING_SNAKE_CASE	) if x else ""






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					return self.current_encoder.get(SCREAMING_SNAKE_CASE      ,       self.current_encoder[self.unk_token]	)






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.language_code_re.match(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:list										=      [match.group(0	)] if match else []
					return code, self.language_code_re.sub(''      ,       SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      self.remove_language_code(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.current_spm.encode(SCREAMING_SNAKE_CASE      ,       out_type=SCREAMING_SNAKE_CASE	)
					return code + pieces






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int	):




					"""simple docstring"""







					return self.decoder.get(SCREAMING_SNAKE_CASE      ,       self.unk_token	)






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       **SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					return super().batch_decode(SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       **SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					return super().decode(SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					SCREAMING_SNAKE_CASE_						:List[str]										=      []
					SCREAMING_SNAKE_CASE_						:str										=      ''
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									out_string += sp_model.decode_pieces(SCREAMING_SNAKE_CASE	) + token + " "
									SCREAMING_SNAKE_CASE_						:int										=      []
							else:
									current_sub_tokens.append(SCREAMING_SNAKE_CASE	)
					out_string += sp_model.decode_pieces(SCREAMING_SNAKE_CASE	)
					return out_string.strip()






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=None	):




					"""simple docstring"""







					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.spm_source
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.encoder






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.spm_target
					if self.separate_vocabs:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.target_encoder






			@property
			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					return len(self.encoder	)






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None	):




					"""simple docstring"""







					if not os.path.isdir(SCREAMING_SNAKE_CASE	):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
							return
					SCREAMING_SNAKE_CASE_						:List[Any]										=      []

					if self.separate_vocabs:
							SCREAMING_SNAKE_CASE_						:Any										=      os.path.join(
							    SCREAMING_SNAKE_CASE      ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      ,       )
							SCREAMING_SNAKE_CASE_						:List[str]										=      os.path.join(
							    SCREAMING_SNAKE_CASE      ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']      ,       )
							save_json(self.encoder      ,       SCREAMING_SNAKE_CASE	)
							save_json(self.target_encoder      ,       SCREAMING_SNAKE_CASE	)
							saved_files.append(SCREAMING_SNAKE_CASE	)
							saved_files.append(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:List[str]										=      os.path.join(
							    SCREAMING_SNAKE_CASE      ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']	)
							save_json(self.encoder      ,       SCREAMING_SNAKE_CASE	)
							saved_files.append(SCREAMING_SNAKE_CASE	)

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]      ,       self.spm_files      ,       [self.spm_source, self.spm_target]      ,       ):
							SCREAMING_SNAKE_CASE_						:int										=      os.path.join(
							    SCREAMING_SNAKE_CASE      ,       (filename_prefix + '-' if filename_prefix else '') + spm_save_filename	)
							if os.path.abspath(SCREAMING_SNAKE_CASE	) != os.path.abspath(SCREAMING_SNAKE_CASE	) and os.path.isfile(SCREAMING_SNAKE_CASE	):
									copyfile(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
									saved_files.append(SCREAMING_SNAKE_CASE	)
							elif not os.path.isfile(SCREAMING_SNAKE_CASE	):
									with open(SCREAMING_SNAKE_CASE      ,       'wb'	) as fi:
											SCREAMING_SNAKE_CASE_						:Dict										=      spm_model.serialized_model_proto()
											fi.write(SCREAMING_SNAKE_CASE	)
									saved_files.append(SCREAMING_SNAKE_CASE	)

					return tuple(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					return self.get_src_vocab()






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					return dict(self.encoder      ,       **self.added_tokens_encoder	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					return dict(self.target_encoder      ,       **self.added_tokens_decoder	)






			def __getstate__(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.__dict__.copy()
					state.update(
					    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}	)
					return state






			def __setstate__(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      d

					# for backward compatibility
					if not hasattr(self      ,       'sp_model_kwargs'	):
							SCREAMING_SNAKE_CASE_						:List[str]										=      {}

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      (load_spm(SCREAMING_SNAKE_CASE      ,       self.sp_model_kwargs	) for f in self.spm_files)
					SCREAMING_SNAKE_CASE_						:Any										=      self.spm_source
					self._setup_normalizer()






			def 				_lowercase	(   self  :							List[Any]      ,       *SCREAMING_SNAKE_CASE  :							Optional[int]      ,       **SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					return 1






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							List      ,       SCREAMING_SNAKE_CASE  :							Optional[List] = None      ,       SCREAMING_SNAKE_CASE  :							bool = False	):




					"""simple docstring"""







					if already_has_special_tokens:
							return self._special_token_mask(SCREAMING_SNAKE_CASE	)
					elif token_ids_a is None:
							return self._special_token_mask(SCREAMING_SNAKE_CASE	) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE     )
		spm.Load(SCREAMING_SNAKE_CASE     )
		return spm







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		with open(SCREAMING_SNAKE_CASE ,		'w'     ) as f:
				json.dump(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		indent=2     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		with open(SCREAMING_SNAKE_CASE ,		'r'     ) as f:
				return json.load(SCREAMING_SNAKE_CASE     )




'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

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

      if hf_config.use_weighted_layer_sum:
            UpperCamelCase__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			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__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





