

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


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{"""vocab_file""": """spiece.model"""}

__UpperCAmelCase								=			{
    """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"""
        ),
    }
}

__UpperCAmelCase								=			{
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=VOCAB_FILES_NAMES
  lowerCamelCase   :      List[str]										=PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase   :      int										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase   :      str										=["input_ids", "attention_mask"]
  lowerCamelCase   :      List[int]										=[]







  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int="<unk>"	,							lowerCAmelCase			:   Union[str, Any]="<s>"	,							lowerCAmelCase			:   Dict="</s>"	,							lowerCAmelCase			:   Dict="<pad>"	,							lowerCAmelCase			:   Optional[int]="[SEP]"	,							lowerCAmelCase			:   Tuple="[MASK]"	,							lowerCAmelCase			:   Optional[int]="[CLS]"	,							lowerCAmelCase			:   Optional[Dict[str, Any]] = None	,							**lowerCAmelCase			:   str	,							)							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else bos_token
     __lowerCAmelCase  :      Any    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else eos_token
     __lowerCAmelCase  :      Optional[int]    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else unk_token
     __lowerCAmelCase  :      Tuple    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else pad_token
     __lowerCAmelCase  :      Optional[Any]    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else cls_token
     __lowerCAmelCase  :      Optional[Any]    =						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
     __lowerCAmelCase  :      Optional[int]    =						AddedToken(lowerCAmelCase	,							lstrip=lowerCAmelCase	,							rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase	,							lowerCAmelCase   ) else mask_token

     __lowerCAmelCase  :      Union[str, Any]    =						{} 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	,							)

     __lowerCAmelCase  :      Any    =						vocab_file

     __lowerCAmelCase  :      Any    =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(lowerCAmelCase   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     return self.sp_model.get_piece_size()







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						{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			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.__dict__.copy()
     __lowerCAmelCase  :      Tuple    =						None
     return state







  def __setstate__(      self			:   List[str]	,							lowerCAmelCase			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						d

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

     __lowerCAmelCase  :      Optional[int]    =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str   )							->     List[str]:






     """simple docstring"""







     return self.sp_model.encode(lowerCAmelCase	,							out_type=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     return self.sp_model.piece_to_id(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.sp_model.IdToPiece(lowerCAmelCase   )
     return token







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						[]
     __lowerCAmelCase  :      Tuple    =						""""""
     __lowerCAmelCase  :      Tuple    =						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
           __lowerCAmelCase  :      Dict    =						True
           __lowerCAmelCase  :      Union[str, Any]    =						[]
        else:
           current_sub_tokens.append(lowerCAmelCase   )
           __lowerCAmelCase  :      Dict    =						False
     out_string += self.sp_model.decode(lowerCAmelCase   )
     return out_string.strip()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   List[int]	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   bool = None	,							lowerCAmelCase			:   bool = True	,							**lowerCAmelCase			:   Union[str, Any]	,							)							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						kwargs.pop("""use_source_tokenizer"""	,							lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						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
     __lowerCAmelCase  :      List[str]    =						[]
     __lowerCAmelCase  :      Dict    =						[]
     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   )   )
              __lowerCAmelCase  :      List[str]    =						[]
           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:
        __lowerCAmelCase  :      Union[str, Any]    =						re.sub(r""" (\[(MASK|SEP)\])"""	,							r"""\1"""	,							""" """.join(lowerCAmelCase   )   )
     else:
        __lowerCAmelCase  :      Union[str, Any]    =						"""""".join(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        __lowerCAmelCase  :      List[str]    =						self.clean_up_tokenization(lowerCAmelCase   )
        return clean_text
     else:
        return text







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[str] = None   )							->     Tuple[str]:






     """simple docstring"""







     if not os.path.isdir(lowerCAmelCase   ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
        return
     __lowerCAmelCase  :      Optional[int]    =						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:
           __lowerCAmelCase  :      str    =						self.sp_model.serialized_model_proto()
           fi.write(lowerCAmelCase   )

     return (out_vocab_file,)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   List[int]	,							lowerCAmelCase			:   Optional[List[int]] = None   )							->     List[int]:






     """simple docstring"""







     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase  :      List[Any]    =						[self.cls_token_id]
     __lowerCAmelCase  :      int    =						[self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[int]	,							lowerCAmelCase			:   Optional[List[int]] = None	,							lowerCAmelCase			:   bool = False   )							->     List[int]:






     """simple docstring"""







     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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[int]	,							lowerCAmelCase			:   Optional[List[int]] = None   )							->     List[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						[self.sep_token_id]
     __lowerCAmelCase  :      List[str]    =						[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]





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      str    =						line.decode("""UTF-8"""					)
         if "warnings summary (final)" in line:
            continue
         # This means we are outside the body of a warning
         elif not line.startswith(""" """					):
            # process a single warning and move it to `selected_warnings`.
            if len(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\n""".join(__A					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(__A					)
               buffer.clear()
            continue
         else:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      except Exception:
         logger.warning(
             f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''					)

   return selected_warnings





def      snake_case_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
   parser.add_argument(
       """--output_dir""",
       type=str,
       required=True,
       help="""Where to store the downloaded artifacts and other result files.""",
   )
   parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
   # optional parameters
   parser.add_argument(
       """--targets""",
       default="""DeprecationWarning,UserWarning,FutureWarning""",
       type=list_str,
       help="""Comma-separated list of target warning(s) which we want to extract.""",
   )
   parser.add_argument(
       """--from_gh""",
       action="""store_true""",
       help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   __UpperCAmelCase								=			args.from_gh
   if from_gh:
      # The artifacts have to be downloaded using `actions/download-artifact@v3`
      pass
   else:
      os.makedirs(args.output_dir, exist_ok=True)

      # get download links
      __UpperCAmelCase								=			get_artifacts_links(args.workflow_run_id, token=args.token)
      with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
         json.dump(artifacts, fp, ensure_ascii=False, indent=4)

      # download artifacts
      for idx, (name, url) in enumerate(artifacts.items()):
         print(name)
         print(url)
         print("""=""" * 80)
         download_artifact(name, url, args.output_dir, args.token)
         # Be gentle to GitHub
         time.sleep(1)

    # extract warnings from artifacts
   __UpperCAmelCase								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			sorted(selected_warnings)
   with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


