






"""simple docstring"""







import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

_UpperCamelCase					      =  {
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

_UpperCamelCase					      =  {
    """vocab_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
    },
    """sentencepiece_model_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
    },
}

_UpperCamelCase					      =  {
    """ernie-m-base""": 514,
    """ernie-m-large""": 514,
}

_UpperCamelCase					      =  {
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ["input_ids"]

    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      RESOURCE_FILES_NAMES






    def __init__(			self     ,A     ,A=None     ,A=False     ,A="utf8"     ,A="[UNK]"     ,A="[SEP]"     ,A="[PAD]"     ,A="[CLS]"     ,A="[MASK]"     ,A = None     ,**A     ,):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.

          UpperCAmelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs
          super().__init__(
              do_lower_case=A     ,unk_token=A     ,sep_token=A     ,pad_token=A     ,cls_token=A     ,mask_token=A     ,vocab_file=A     ,encoding=A     ,sp_model_kwargs=self.sp_model_kwargs     ,**A     ,)
          UpperCAmelCase          =	do_lower_case
          UpperCAmelCase          =	sentencepiece_model_ckpt
          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(A )

          # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
          if vocab_file is not None:
                UpperCAmelCase          =	self.load_vocab(filepath=A )
          else:
                UpperCAmelCase          =	{self.sp_model.id_to_piece(A ): id for id in range(self.sp_model.get_piece_size() )}
          UpperCAmelCase          =	{v: k for k, v in self.vocab.items()}






    def     _UpperCamelCase							(			self     ,A ):
          if text is None:
                return None

          UpperCAmelCase          =	self.tokenize(A )
          UpperCAmelCase      , UpperCAmelCase          =	"""""", []

          for i, ch in enumerate(A ):
                if ch in self.SP_CHAR_MAPPING:
                      UpperCAmelCase          =	self.SP_CHAR_MAPPING.get(A )
                else:
                      UpperCAmelCase          =	unicodedata.normalize("""NFKC"""     ,A )
                if self.is_whitespace(A ):
                      continue
                normalized_text += ch
                char_mapping.extend([i] * len(A ) )

          UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	normalized_text, [], 0

          if self.do_lower_case:
                UpperCAmelCase          =	text.lower()

          for token in split_tokens:
                if token[:1] == "▁":
                      UpperCAmelCase          =	token[1:]
                UpperCAmelCase          =	text[offset:].index(A ) + offset
                UpperCAmelCase          =	start + len(A )

                token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
                UpperCAmelCase          =	end
          return token_mapping






    @property
    def     _UpperCamelCase							(			self ):
          return len(self.vocab )






    def     _UpperCamelCase							(			self ):
          return dict(self.vocab     ,**self.added_tokens_encoder )






    def __getstate__(			self ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

          # for backward compatibility
          if not hasattr(self     ,"""sp_model_kwargs""" ):
                UpperCAmelCase          =	{}

          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.sentencepiece_model_ckpt )






    def     _UpperCamelCase							(			self     ,A ):
          return "".join((self.SP_CHAR_MAPPING.get(A     ,A ) for c in text) )






    def     _UpperCamelCase							(			self     ,A     ,A=False     ,A=64     ,A=0.1 ):

          if self.sp_model_kwargs.get("""enable_sampling""" ) is True:
                UpperCAmelCase          =	True
          if self.sp_model_kwargs.get("""alpha""" ) is not None:
                UpperCAmelCase          =	self.sp_model_kwargs.get("""alpha""" )
          if self.sp_model_kwargs.get("""nbest_size""" ) is not None:
                UpperCAmelCase          =	self.sp_model_kwargs.get("""nbest_size""" )

          if not enable_sampling:
                UpperCAmelCase          =	self.sp_model.EncodeAsPieces(A )
          else:
                UpperCAmelCase          =	self.sp_model.SampleEncodeAsPieces(A     ,A     ,A )
          UpperCAmelCase          =	[]
          for pi, piece in enumerate(A ):
                if piece == SPIECE_UNDERLINE:
                      if not pieces[pi + 1].startswith(A ) and pi != 0:
                            new_pieces.append(A )
                            continue
                      else:
                            continue
                UpperCAmelCase          =	0
                for i, chunk in enumerate(A ):
                      if chunk == SPIECE_UNDERLINE:
                            continue
                      if self.is_ch_char(A ) or self.is_punct(A ):
                            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                  new_pieces.append(piece[lst_i:i] )
                            new_pieces.append(A )
                            UpperCAmelCase          =	i + 1
                      elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                  new_pieces.append(piece[lst_i:i] )
                            UpperCAmelCase          =	i
                      elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                  new_pieces.append(piece[lst_i:i] )
                            UpperCAmelCase          =	i
                if len(A ) > lst_i:
                      new_pieces.append(piece[lst_i:] )
          return new_pieces






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.convert_ids_to_tokens(A )
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A ):
          return self.vocab.get(A     ,self.vocab.get(self.unk_token ) )






    def     _UpperCamelCase							(			self     ,A ):
          return self.reverse_vocab.get(A     ,self.unk_token )






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase          =	[self.cls_token_id]
          UpperCAmelCase          =	[self.sep_token_id]
          return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if offset_mapping_a is None:
                return [(0, 0)] + offset_mapping_a + [(0, 0)]

          return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]






    def     _UpperCamelCase							(			self     ,A     ,A=None     ,A=False ):

          if already_has_special_tokens:
                if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model.""" )
                return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
                return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
          return [1] + ([0] * len(A )) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
          if token_ids_a is None:
                # [CLS] X [SEP]
                return (len(A ) + 2) * [0]

          # [CLS] A [SEP] [SEP] B [SEP]
          return [0] * (len(A ) + 1) + [1] * (len(A ) + 3)






    def     _UpperCamelCase							(			self     ,A ):
          if "\u4e00" <= char <= "\u9fff":
                return True
          return False






    def     _UpperCamelCase							(			self     ,A ):
          if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                return True
          return False






    def     _UpperCamelCase							(			self     ,A ):
          if char in ",;:.?!~，；：。？！《》【】":
                return True
          return False






    def     _UpperCamelCase							(			self     ,A ):
          if char == " " or char == "\t" or char == "\n" or char == "\r":
                return True
          if len(A ) == 1:
                UpperCAmelCase          =	unicodedata.category(A )
                if cat == "Zs":
                      return True
          return False






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	{}
          with io.open(A     ,"""r"""     ,encoding="""utf-8""" ) as f:
                for index, line in enumerate(A ):
                      UpperCAmelCase          =	line.rstrip("""\n""" )
                      UpperCAmelCase          =	int(A )

          return token_to_idx






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	0
          if os.path.isdir(A ):
                UpperCAmelCase          =	os.path.join(
                    A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
          else:
                UpperCAmelCase          =	(filename_prefix + """-""" if filename_prefix else """""") + save_directory
          with open(A     ,"""w"""     ,encoding="""utf-8""" ) as writer:
                for token, token_index in sorted(self.vocab.items()     ,key=lambda A : kv[1] ):
                      if index != token_index:
                            logger.warning(
                                F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                                """ Please check that the vocabulary is not corrupted!""" )
                            UpperCAmelCase          =	token_index
                      writer.write(token + """\n""" )
                      index += 1

          UpperCAmelCase          =	os.path.join(A     ,"""sentencepiece.bpe.model""" )
          with open(A     ,"""wb""" ) as fi:
                UpperCAmelCase          =	self.sp_model.serialized_model_proto()
                fi.write(A )

          return (vocab_file,)











"""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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      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 _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForAudioFrameClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""model.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

      UpperCAmelCase          =	hf_config.architectures[0]
      if arch.endswith("""ForSequenceClassification"""							):
            UpperCAmelCase          =	convert_classification(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForAudioFrameClassification"""							):
            UpperCAmelCase          =	convert_diarization(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForXVector"""							):
            UpperCAmelCase          =	convert_xvector(_snake_case				,	_snake_case				,	_snake_case							)
      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(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


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)



