

"""simple docstring"""



import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE__:str			=	{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[str]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			int           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			List[int]           =		[]


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="[SEP]"  ,						lowerCamelCase="[MASK]"  ,						lowerCamelCase="[CLS]"  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else bos_token
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else eos_token
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else unk_token
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else pad_token
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else cls_token
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        __a     				=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file

        __a     				=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowerCamelCase		)


 @property
 def 		a__ (				self		):
        return self.sp_model.get_piece_size()


 def 		a__ (				self		):
        __a     				=  {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab


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


 def __setstate__(				self  ,						lowerCamelCase		):
        __a     				=  d

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

        __a     				=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.sp_model.encode(lowerCamelCase  ,						out_type=lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.sp_model.piece_to_id(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.sp_model.IdToPiece(lowerCamelCase		)
        return token


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        __a     				=  ""
        __a     				=  False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(lowerCamelCase		) + token
                      __a     				=  True
                      __a     				=  []
               else:
                      current_sub_tokens.append(lowerCamelCase		)
                      __a     				=  False
        out_string += self.sp_model.decode(lowerCamelCase		)
        return out_string.strip()


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  kwargs.pop("use_source_tokenizer"  ,						lowerCamelCase		)

        __a     				=  self.convert_ids_to_tokens(lowerCamelCase  ,						skip_special_tokens=lowerCamelCase		)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        __a     				=  []
        __a     				=  []
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(lowerCamelCase		)		)
                             __a     				=  []
                      sub_texts.append(lowerCamelCase		)
               else:
                      current_sub_text.append(lowerCamelCase		)
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowerCamelCase		)		)

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               __a     				=  re.sub(R" (\[(MASK|SEP)\])"  ,						R"\1"  ,						" ".join(lowerCamelCase		)		)
        else:
               __a     				=  "".join(lowerCamelCase		)

        __a     				=  (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               __a     				=  self.clean_up_tokenization(lowerCamelCase		)
               return clean_text
        else:
               return text


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)
        elif not os.path.isfile(self.vocab_file		):
               with open(lowerCamelCase  ,						"wb"		) as fi:
                      __a     				=  self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase		)

        return (out_vocab_file,)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False		):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase  ,						token_ids_a=lowerCamelCase  ,						already_has_special_tokens=lowerCamelCase		)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1]
        return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		)) + [1]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]

"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __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"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

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

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

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__: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.""")
   SCREAMING_SNAKE_CASE__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
