


import os
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


__lowerCAmelCase		       =							logging.get_logger(__name__)

__lowerCAmelCase		       =							{'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowerCAmelCase		       =							{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__lowerCAmelCase		       =							{
    '''camembert-base''': 5_12,
}

__lowerCAmelCase		       =							'''▁'''





class   __a			(    __UpperCamelCase      ):
 __lowercase :							int        = VOCAB_FILES_NAMES
 __lowercase :							Dict        = PRETRAINED_VOCAB_FILES_MAP
 __lowercase :							List[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase :							Union[str, Any]        = ['input_ids', 'attention_mask']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__="<s>"				, lowerCAmelCase__="</s>"				, lowerCAmelCase__="</s>"				, lowerCAmelCase__="<s>"				, lowerCAmelCase__="<unk>"				, lowerCAmelCase__="<pad>"				, lowerCAmelCase__="<mask>"				, lowerCAmelCase__=["<s>NOTUSED", "</s>NOTUSED"]				, lowerCAmelCase__ = None				, **lowerCAmelCase__				, )					->				None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:   Union[str, Any]  			=   AddedToken(lowerCAmelCase__				, lstrip=lowerCAmelCase__				, rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ) else mask_token

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

        super().__init__(
            bos_token=lowerCAmelCase__				, eos_token=lowerCAmelCase__				, unk_token=lowerCAmelCase__				, sep_token=lowerCAmelCase__				, cls_token=lowerCAmelCase__				, pad_token=lowerCAmelCase__				, mask_token=lowerCAmelCase__				, additional_special_tokens=lowerCAmelCase__				, sp_model_kwargs=self.sp_model_kwargs				, **lowerCAmelCase__				, )
        lowercase__:   Tuple  			=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(str(lowerCAmelCase__ ) )
        lowercase__:   int  			=   vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        lowercase__:   Optional[int]  			=   {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
        lowercase__:   Dict  			=   len(self.fairseq_tokens_to_ids )
        lowercase__:   Any  			=   len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
        lowercase__:   Optional[Any]  			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				List[int]:




        '''simple docstring'''

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase__:   List[Any]  			=   [self.cls_token_id]
        lowercase__:   Dict  			=   [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 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, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				List[int]:




        '''simple docstring'''
        lowercase__:   List[str]  			=   [self.sep_token_id]
        lowercase__:   Tuple  			=   [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 + sep + token_ids_a + sep ) * [0]
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__				, out_type=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(lowerCAmelCase__ ) == 0:
               # Convert sentence piece unk token to fairseq unk token index
               return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   List[str]  			=   []
        lowercase__:   Optional[int]  			=   ''
        lowercase__:   Optional[Any]  			=   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
                      lowercase__:   int  			=   True
                      lowercase__:   Tuple  			=   []
               else:
                      current_sub_tokens.append(lowerCAmelCase__ )
                      lowercase__:   Optional[Any]  			=   False
        out_string += self.sp_model.decode(lowerCAmelCase__ )
        return out_string.strip()
 def __getstate__(   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Dict  			=   self.__dict__.copy()
        lowercase__:   Dict  			=   None
        return state
 def __setstate__(   self				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   str  			=   d

        # for backward compatibility
        if not hasattr(self				, 'sp_model_kwargs' ):
               lowercase__:   Optional[int]  			=   {}

        lowercase__:   Dict  			=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__ ):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
               return
        lowercase__:   List[str]  			=   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:
                      lowercase__:   Dict  			=   self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__ )

        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


__lowerCAmelCase		       =							logging.get_logger(__name__)




def    snake_case_     (  snake_case			,							snake_case     )  ->				List[str]:
       lowercase__:   List[str]  			=   set()
       lowercase__:   List[Any]  			=   []

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

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

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

       return selected_warnings


if __name__ == "__main__":




 def    snake_case_     (  snake_case     )  ->				str:
        return values.split(','     )

 __lowerCAmelCase		       =							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.''',
 )

 __lowerCAmelCase		       =							parser.parse_args()

 __lowerCAmelCase		       =							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
  __lowerCAmelCase		       =							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
 __lowerCAmelCase		       =							extract_warnings(args.output_dir, args.targets)
 __lowerCAmelCase		       =							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)


