'''simple docstring'''






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

import sentencepiece as spm

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


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_	   =			{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE_	   =			{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE_	   =			{
    'AI-Sweden/gpt-sw3-126m': 20_48,
    'AI-Sweden/gpt-sw3-350m': 20_48,
    'AI-Sweden/gpt-sw3-1.6b': 20_48,
    'AI-Sweden/gpt-sw3-6.7b': 20_48,
    'AI-Sweden/gpt-sw3-20b': 20_48,
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					VOCAB_FILES_NAMES
  __lowerCAmelCase				      =					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase				      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase				      =					["""input_ids""", """attention_mask"""]







  def __init__(							self   ,    snake_case_   ,    snake_case_=False   ,    snake_case_=False   ,    snake_case_=False   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_=None   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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

    __UpperCAmelCase:					Tuple		= kwargs.get("""name_or_path"""					)
    if name_or_path is None:
      logger.warning(
          """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
          """ you are testing the model, this can safely be ignored"""					)
      __UpperCAmelCase:					Any		= """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    __UpperCAmelCase:					int		= """<|endoftext|>""" if eos_token is None else eos_token
    __UpperCAmelCase:					Optional[Any]		= """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      __UpperCAmelCase:					Any		= unk_token if pad_token is None else pad_token
      __UpperCAmelCase:					Dict		= eos_token if bos_token is None else bos_token
    else:
      __UpperCAmelCase:					int		= """<pad>""" if pad_token is None else pad_token
      __UpperCAmelCase:					Union[str, Any]		= """<s>""" if bos_token is None else bos_token

    super().__init__(
        do_lower_case=snake_case_   ,    remove_space=snake_case_   ,    keep_accents=snake_case_   ,    bos_token=snake_case_   ,    eos_token=snake_case_   ,    unk_token=snake_case_   ,    pad_token=snake_case_   ,    sp_model_kwargs=self.sp_model_kwargs   ,    **snake_case_   ,    )

    __UpperCAmelCase:					Optional[Any]		= do_lower_case
    __UpperCAmelCase:					List[str]		= remove_space
    __UpperCAmelCase:					List[str]		= keep_accents
    __UpperCAmelCase:					int		= vocab_file

    __UpperCAmelCase:					List[str]		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(snake_case_					)

    # Used for whitespace normalization in input texts
    # fmt : off
    __UpperCAmelCase:					List[Any]		= {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    __UpperCAmelCase:					Dict		= re.compile(
        F'''[{''.join(map(snake_case_   ,    list(range(0   ,    9					)					) + list(range(11   ,    32					)					) + list(range(127   ,    160					)					) + [160, 173, 8203]					)					)}]'''					)







  def __getstate__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.__dict__.copy()
    __UpperCAmelCase:					List[Any]		= None
    return state







  def __setstate__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= d

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

    __UpperCAmelCase:					Any		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(self.vocab_file					)







  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def    lowercase_  (							self					):

    '''simple docstring'''







    return len(self.sp_model					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.non_printing_characters_re.sub(""""""   ,    snake_case_					)

    # Normalize whitespaces
    __UpperCAmelCase:					List[Any]		= """""".join([char if char not in self.whitespaces else """ """ for char in text]					)

    # NFC Unicode normalization
    __UpperCAmelCase:					List[str]		= unicodedata.normalize("""NFC"""   ,    snake_case_					)
    return text







  def    lowercase_  (							self   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.preprocess_text(snake_case_					)
    return self.sp_model.encode(snake_case_   ,    out_type=snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.PieceToId(snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.IdToPiece(snake_case_					)







  @staticmethod
  def    lowercase_  (							snake_case_					):

    '''simple docstring'''







    return out_string







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= []
    __UpperCAmelCase:					Any		= """"""
    __UpperCAmelCase:					str		= False
    for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
        if not prev_is_special:
          out_string += " "

        out_string += self.sp_model.decode(snake_case_					) + token
        __UpperCAmelCase:					Dict		= True
        __UpperCAmelCase:					int		= []
      else:
        current_sub_tokens.append(snake_case_					)
        __UpperCAmelCase:					int		= False
    out_string += self.sp_model.decode(snake_case_					)

    return out_string







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= {self.convert_ids_to_tokens(snake_case_					): i for i in range(self.vocab_size					)}
    vocab.update(self.added_tokens_encoder					)
    return vocab







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None					):

    '''simple docstring'''







    if not os.path.isdir(snake_case_					):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
      return
    __UpperCAmelCase:					str		= os.path.join(
        snake_case_   ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

    if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case_					) and os.path.isfile(self.vocab_file					):
      copyfile(self.vocab_file   ,    snake_case_					)
    elif not os.path.isfile(self.vocab_file					):
      with open(snake_case_   ,    """wb"""					) as fi:
        __UpperCAmelCase:					Optional[Any]		= self.sp_model.serialized_model_proto()
        fi.write(snake_case_					)

    return (out_vocab_file,)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = False					):

    '''simple docstring'''







    if isinstance(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					List[Any]		= self.preprocess_text(snake_case_					)
      __UpperCAmelCase:					int		= self.sp_model.encode(snake_case_					)
    else:
      __UpperCAmelCase:					str		= [self.preprocess_text(snake_case_					) for t in text]
      __UpperCAmelCase:					Optional[Any]		= self.sp_model.encode(snake_case_					)

    if return_tensors is True or return_tensors == "pt":
      __UpperCAmelCase:					Any		= torch.tensor(snake_case_					)

    return token_ids







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return self.sp_model.decode(snake_case_					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
    __UpperCAmelCase:					str		= (
        F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(snake_case_					) + F'''{self.bos_token}Bot:'''
    )
    return self.encode(text=snake_case_					)



'''simple docstring'''






import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]=7     )							->	Union[str, Any]:
  __UpperCAmelCase:					Optional[int]		= None
  if token is not None:
    __UpperCAmelCase:					List[Any]		= {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}

  # The id of a workflow (not of a workflow run)
  __UpperCAmelCase:					List[str]		= """636036"""

  __UpperCAmelCase:					Optional[int]		= F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

  __UpperCAmelCase:					List[Any]		= requests.get(_lowercase   ,    headers=_lowercase     ).json()

  return result["workflow_runs"]






def  UpperCamelCase__					(					_lowercase    :				Optional[int]     )							->	List[str]:
  __UpperCAmelCase:					str		= get_daily_ci_runs(_lowercase     )
  __UpperCAmelCase:					str		= None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      __UpperCAmelCase:					Optional[int]		= workflow_run["""id"""]
      break

  return workflow_run_id






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Dict:
  __UpperCAmelCase:					List[Any]		= get_last_daily_ci_runs(_lowercase     )
  if workflow_run_id is not None:
    __UpperCAmelCase:					List[Any]		= get_artifacts_links(worflow_run_id=_lowercase   ,    token=_lowercase     )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        __UpperCAmelCase:					str		= artifacts_links[artifact_name]
        download_artifact(
            artifact_name=_lowercase   ,    artifact_url=_lowercase   ,    output_dir=_lowercase   ,    token=_lowercase     )







def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Tuple   ,    _lowercase    :				Tuple     )							->	int:
  get_last_daily_ci_artifacts(_lowercase   ,    _lowercase   ,    _lowercase     )

  __UpperCAmelCase:					int		= {}
  for artifact_name in artifact_names:
    __UpperCAmelCase:					Optional[Any]		= os.path.join(_lowercase   ,    F'''{artifact_name}.zip'''     )
    if os.path.isfile(_lowercase     ):
      __UpperCAmelCase:					Any		= {}
      with zipfile.ZipFile(_lowercase     ) as z:
        for filename in z.namelist():
          if not os.path.isdir(_lowercase     ):
            # read the file
            with z.open(_lowercase     ) as f:
              __UpperCAmelCase:					Dict		= f.read().decode("""UTF-8"""     )

  return results