'''simple docstring'''
def  _A		(     snake_case					)      ->     int:
   # if the collection is empty, returns empty
   if collection == []:
      return []

   # get some information about the collection
   _lowercase      :				Dict      					=       len(__a					)
   _lowercase      :				Optional[int]      					=       max(__a					)
   _lowercase      :				Optional[Any]      					=       min(__a					)

   # create the counting array
   _lowercase      :				Any      					=       coll_max + 1 - coll_min
   _lowercase      :				str      					=       [0] * counting_arr_length

   # count how much a number appears in the collection
   for number in collection:
      counting_arr[number - coll_min] += 1

   # sum each position with it's predecessors. now, counting_arr[i] tells
   # us how many elements <= i has in the collection
   for i in range(1		,							__a					):
      _lowercase      :				Optional[int]      					=       counting_arr[i] + counting_arr[i - 1]

   # create the output collection
   _lowercase      :				List[Any]      					=       [0] * coll_len

   # place the elements in the output, respecting the original order (stable
   # sort) from end to begin, updating counting_arr
   for i in reversed(range(0		,							__a					)					):
      _lowercase      :				int      					=       collection[i]
      counting_arr[collection[i] - coll_min] -= 1

   return ordered







def  _A		(     snake_case					)      ->     Any:
   return "".join([chr(__a					) for i in counting_sort([ord(__a					) for c in string]					)]					)


if __name__ == "__main__":
    # Test string sort
    assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

    _snake_case         =  input('Enter numbers separated by a comma:\n').strip()
    _snake_case         =  [int(item) for item in user_input.split(',')]
    print(counting_sort(unsorted))









'''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


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {'vocab_file': 'spiece.model'}

_snake_case         =  {
    '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'
        ),
    }
}

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


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							['input_ids', 'attention_mask']
   _SCREAMING_SNAKE_CASE   :					List[int]     	=							[]


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase="<unk>"      ,    _UpperCamelCase="<s>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="<pad>"      ,    _UpperCamelCase="[SEP]"      ,    _UpperCamelCase="[MASK]"      ,    _UpperCamelCase="[CLS]"      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else bos_token
      _lowercase      :				Optional[int]      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else eos_token
      _lowercase      :				Optional[Any]      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else unk_token
      _lowercase      :				Union[str, Any]      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else pad_token
      _lowercase      :				Any      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else cls_token
      _lowercase      :				str      					=       AddedToken(_UpperCamelCase      ,    lstrip=_UpperCamelCase      ,    rstrip=_UpperCamelCase  ) if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) else sep_token

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

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

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

      _lowercase      :				str      					=       vocab_file

      _lowercase      :				List[Any]      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self.sp_model.get_piece_size()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				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  ):





      """simple docstring"""





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


   def __setstate__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       d

      # for backward compatibility
      if not hasattr(self      ,    "sp_model_kwargs"  ):
         _lowercase      :				Optional[Any]      					=       {}

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.sp_model.piece_to_id(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self.sp_model.IdToPiece(_UpperCamelCase  )
      return token


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       []
      _lowercase      :				int      					=       ""
      _lowercase      :				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
            _lowercase      :				Union[str, Any]      					=       True
            _lowercase      :				Optional[int]      					=       []
         else:
            current_sub_tokens.append(_UpperCamelCase  )
            _lowercase      :				str      					=       False
      out_string += self.sp_model.decode(_UpperCamelCase  )
      return out_string.strip()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Any      					=       kwargs.pop("use_source_tokenizer"      ,    _UpperCamelCase  )

      _lowercase      :				Dict      					=       self.convert_ids_to_tokens(_UpperCamelCase      ,    skip_special_tokens=_UpperCamelCase  )

      # 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
      _lowercase      :				Dict      					=       []
      _lowercase      :				Any      					=       []
      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(_UpperCamelCase  )  )
               _lowercase      :				Optional[Any]      					=       []
            sub_texts.append(_UpperCamelCase  )
         else:
            current_sub_text.append(_UpperCamelCase  )
      if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase  )  )

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

      _lowercase      :				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:
         _lowercase      :				Tuple      					=       self.clean_up_tokenization(_UpperCamelCase  )
         return clean_text
      else:
         return text


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





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

      return (out_vocab_file,)


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _lowercase      :				str      					=       [self.cls_token_id]
      _lowercase      :				List[str]      					=       [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      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 None:
         return [1] + ([0] * len(_UpperCamelCase  )) + [1]
      return [1] + ([0] * len(_UpperCamelCase  )) + [1] + ([0] * len(_UpperCamelCase  )) + [1]






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None  ):





      """simple docstring"""





      _lowercase      :				str      					=       [self.sep_token_id]
      _lowercase      :				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]






