





import os
import unicodedata
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				:       List[str]	   =				logging.get_logger(__name__)
_UpperCAmelCase				:       Union[str, Any]	   =				{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase				:       Dict	   =				{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

_UpperCAmelCase				:       List[Any]	   =				{
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

_UpperCAmelCase				:       Optional[int]	   =				"""▁"""

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      List[str]=True			,     UpperCAmelCase  :      str=False			,     UpperCAmelCase  :      str="[CLS]"			,     UpperCAmelCase  :      Any="[SEP]"			,     UpperCAmelCase  :      Union[str, Any]="<unk>"			,     UpperCAmelCase  :      int="[SEP]"			,     UpperCAmelCase  :      str="<pad>"			,     UpperCAmelCase  :      Dict="[CLS]"			,     UpperCAmelCase  :      str="[MASK]"			,     UpperCAmelCase  :      Optional[Dict[str, Any]] = None			,     **UpperCAmelCase  :      Optional[int]			,     )			->	None:
     # 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.
     lowerCamelCase__    :      int					 =		(
         AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase			,     normalized=UpperCAmelCase	)
         if isinstance(UpperCAmelCase			,     UpperCAmelCase	)
         else mask_token
     )

     lowerCamelCase__    :      Dict					 =		{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=UpperCAmelCase			,     remove_space=UpperCAmelCase			,     keep_accents=UpperCAmelCase			,     bos_token=UpperCAmelCase			,     eos_token=UpperCAmelCase			,     unk_token=UpperCAmelCase			,     sep_token=UpperCAmelCase			,     pad_token=UpperCAmelCase			,     cls_token=UpperCAmelCase			,     mask_token=UpperCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **UpperCAmelCase			,     )

     lowerCamelCase__    :      Optional[Any]					 =		do_lower_case
     lowerCamelCase__    :      List[str]					 =		remove_space
     lowerCamelCase__    :      Any					 =		keep_accents
     lowerCamelCase__    :      Dict					 =		vocab_file

     lowerCamelCase__    :      List[Any]					 =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(UpperCAmelCase	)
 @property
 def      A_	(      self  :      Any	)			->	Optional[int]:
     return len(self.sp_model	)
 def      A_	(      self  :      List[str]	)			->	Optional[Any]:
     lowerCamelCase__    :      Dict					 =		{self.convert_ids_to_tokens(UpperCAmelCase	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab
 def __getstate__(      self  :      Optional[int]	)			->	int:
     lowerCamelCase__    :      List[Any]					 =		self.__dict__.copy()
     lowerCamelCase__    :      List[Any]					 =		None
     return state
 def __setstate__(      self  :      List[str]			,     UpperCAmelCase  :      Any	)			->	Optional[int]:
     lowerCamelCase__    :      List[Any]					 =		d

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

     lowerCamelCase__    :      Union[str, Any]					 =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(self.vocab_file	)
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Union[str, Any]	)			->	Tuple:
     if self.remove_space:
         lowerCamelCase__    :      int					 =		' '.join(inputs.strip().split()	)
     else:
         lowerCamelCase__    :      int					 =		inputs
     lowerCamelCase__    :      List[Any]					 =		outputs.replace('``'			,     '"'	).replace('\'\''			,     '"'	)

     if not self.keep_accents:
         lowerCamelCase__    :      List[Any]					 =		unicodedata.normalize('NFKD'			,     UpperCAmelCase	)
         lowerCamelCase__    :      Optional[Any]					 =		''.join([c for c in outputs if not unicodedata.combining(UpperCAmelCase	)]	)
     if self.do_lower_case:
         lowerCamelCase__    :      Any					 =		outputs.lower()

     return outputs
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str	)			->	List[str]:
     lowerCamelCase__    :      Optional[int]					 =		self.preprocess_text(UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		self.sp_model.encode(UpperCAmelCase			,     out_type=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		[]
     for piece in pieces:
         if len(UpperCAmelCase	) > 1 and piece[-1] == str(','	) and piece[-2].isdigit():
             lowerCamelCase__    :      str					 =		self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase			,     ''	)	)
             if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]	) == 1:
                     lowerCamelCase__    :      List[str]					 =		cur_pieces[1:]
                 else:
                     lowerCamelCase__    :      List[Any]					 =		cur_pieces[0][1:]
             cur_pieces.append(piece[-1]	)
             new_pieces.extend(UpperCAmelCase	)
         else:
             new_pieces.append(UpperCAmelCase	)

     return new_pieces
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Optional[int]	)			->	List[Any]:
     return self.sp_model.PieceToId(UpperCAmelCase	)
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      Optional[Any]	)			->	Any:
     return self.sp_model.IdToPiece(UpperCAmelCase	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Dict	)			->	Optional[int]:
     lowerCamelCase__    :      str					 =		[]
     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(UpperCAmelCase	) + token
             lowerCamelCase__    :      List[Any]					 =		True
             lowerCamelCase__    :      str					 =		[]
         else:
             current_sub_tokens.append(UpperCAmelCase	)
             lowerCamelCase__    :      List[str]					 =		False
     out_string += self.sp_model.decode(UpperCAmelCase	)
     return out_string.strip()
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Optional[Any]					 =		[self.sep_token_id]
     lowerCamelCase__    :      List[str]					 =		[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None			,     UpperCAmelCase  :      bool = False	)			->	List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase			,     token_ids_a=UpperCAmelCase			,     already_has_special_tokens=UpperCAmelCase	)

     if token_ids_a is not None:
         return [1] + ([0] * len(UpperCAmelCase	)) + [1] + ([0] * len(UpperCAmelCase	)) + [1]
     return [1] + ([0] * len(UpperCAmelCase	)) + [1]
 def      A_	(      self  :      str			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Optional[int]					 =		[self.sep_token_id]
     lowerCamelCase__    :      Union[str, Any]					 =		[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]







 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     if not os.path.isdir(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
         return
     lowerCamelCase__    :      Dict					 =		os.path.join(
         UpperCAmelCase			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

     if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCAmelCase	) and os.path.isfile(self.vocab_file	):
         copyfile(self.vocab_file			,     UpperCAmelCase	)
     elif not os.path.isfile(self.vocab_file	):
         with open(UpperCAmelCase			,     'wb'	) as fi:
             lowerCamelCase__    :      List[str]					 =		self.sp_model.serialized_model_proto()
             fi.write(UpperCAmelCase	)

     return (out_vocab_file,)












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				:       Optional[int]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    lowerCamelCase__    :      str					 =		set()
    lowerCamelCase__    :      Any					 =		[]

    def parse_line(_UpperCAmelCase ):
        for line in fp:
            if isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
                lowerCamelCase__    :      Any					 =		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(_UpperCAmelCase ) > 0:
                    lowerCamelCase__    :      str					 =		'\n'.join(_UpperCAmelCase )
                    # Only keep the warnings specified in `targets`
                    if any(F""": {x}: """ in warning for x in targets ):
                        selected_warnings.add(_UpperCAmelCase )
                    buffer.clear()
                continue
            else:
                lowerCamelCase__    :      List[str]					 =		line.strip()
                buffer.append(_UpperCAmelCase )

    if from_gh:
        for filename in os.listdir(_UpperCAmelCase ):
            lowerCamelCase__    :      Dict					 =		os.path.join(_UpperCAmelCase						,	_UpperCAmelCase )
            if not os.path.isdir(_UpperCAmelCase ):
                # read the file
                if filename != "warnings.txt":
                    continue
                with open(_UpperCAmelCase ) as fp:
                    parse_line(_UpperCAmelCase )
    else:
        try:
            with zipfile.ZipFile(_UpperCAmelCase ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_UpperCAmelCase ):
                        # read the file
                        if filename != "warnings.txt":
                            continue
                        with z.open(_UpperCAmelCase ) as fp:
                            parse_line(_UpperCAmelCase )
        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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		set()

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

    return selected_warnings


if __name__ == "__main__":


    def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
        return values.split(',' )

    _UpperCAmelCase				:       List[str]	   =				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				:       Union[str, Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       Dict	   =				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				:       Union[str, Any]	   =				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				:       Dict	   =				extract_warnings(args.output_dir, args.targets)
    _UpperCAmelCase				:       Optional[Any]	   =				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)





