'''simple docstring'''





import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def       lowerCAmelCase_							(   snake_case_			:					Features     )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  np.inf

     def set_batch_size(snake_case_			:					FeatureType     ) -> None:
          nonlocal batch_size
          if isinstance(snake_case_   ,       snake_case_     ):
               UpperCAmelCase_			       =  min(snake_case_   ,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
          elif isinstance(snake_case_   ,       snake_case_     ):
               UpperCAmelCase_			       =  min(snake_case_   ,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
          elif isinstance(snake_case_   ,       snake_case_     ) and feature.dtype == "binary":
               UpperCAmelCase_			       =  min(snake_case_   ,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

     _visit(snake_case_   ,       snake_case_     )

     return None if batch_size is np.inf else batch_size




class 			__A (					UpperCamelCase__      ):



    def __init__(self       :       Tuple  ,     __a       :       NestedDataStructureLike[PathLike]  ,     __a       :       Optional[NamedSplit] = None  ,     __a       :       Optional[Features] = None  ,     __a       :       str = None  ,     __a       :       bool = False  ,     __a       :       bool = False  ,     __a       :       Optional[int] = None  ,     **__a       :       Dict  ,     ):
         super().__init__(
             __a  ,     split=__a  ,     features=__a  ,     cache_dir=__a  ,     keep_in_memory=__a  ,     streaming=__a  ,     num_proc=__a  ,     **__a  ,     )
         UpperCAmelCase_			       =  path_or_paths if isinstance(__a  ,     __a					) else {self.split: path_or_paths}
         UpperCAmelCase_			       =  _PACKAGED_DATASETS_MODULES["parquet"][1]
         UpperCAmelCase_			       =  Parquet(
             cache_dir=__a  ,     data_files=__a  ,     features=__a  ,     hash=__a  ,     **__a  ,     )






    def    _lowercase		(self       :       str					):
         # Build iterable dataset
         if self.streaming:
              UpperCAmelCase_			       =  self.builder.as_streaming_dataset(split=self.split					)
         # Build regular (map-style) dataset
         else:
              UpperCAmelCase_			       =  None
              UpperCAmelCase_			       =  None
              UpperCAmelCase_			       =  None
              UpperCAmelCase_			       =  None

              self.builder.download_and_prepare(
                  download_config=__a  ,     download_mode=__a  ,     verification_mode=__a  ,     base_path=__a  ,     num_proc=self.num_proc  ,     )
              UpperCAmelCase_			       =  self.builder.as_dataset(
                  split=self.split  ,     verification_mode=__a  ,     in_memory=self.keep_in_memory					)
         return dataset




class 			__A :



    def __init__(self       :       List[Any]  ,     __a       :       Dataset  ,     __a       :       Union[PathLike, BinaryIO]  ,     __a       :       Optional[int] = None  ,     **__a       :       Optional[int]  ,     ):
         UpperCAmelCase_			       =  dataset
         UpperCAmelCase_			       =  path_or_buf
         UpperCAmelCase_			       =  batch_size or get_writer_batch_size(dataset.features					)
         UpperCAmelCase_			       =  parquet_writer_kwargs



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf  ,     (str, bytes, os.PathLike)					):
              with open(self.path_or_buf  ,     "wb+"					) as buffer:
                   UpperCAmelCase_			       =  self._write(file_obj=__a  ,     batch_size=__a  ,     **self.parquet_writer_kwargs					)
         else:
              UpperCAmelCase_			       =  self._write(file_obj=self.path_or_buf  ,     batch_size=__a  ,     **self.parquet_writer_kwargs					)
         return written






    def    _lowercase		(self       :       int  ,     __a       :       BinaryIO  ,     __a       :       int  ,     **__a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  0
         UpperCAmelCase_			       =  parquet_writer_kwargs.pop("path_or_buf"  ,     __a					)
         UpperCAmelCase_			       =  self.dataset.features.arrow_schema

         UpperCAmelCase_			       =  pq.ParquetWriter(__a  ,     schema=__a  ,     **__a					)

         for offset in logging.tqdm(
             range(0  ,     len(self.dataset					)  ,     __a					)  ,     unit="ba"  ,     disable=not logging.is_progress_bar_enabled()  ,     desc="Creating parquet from Arrow format"  ,     ):
              UpperCAmelCase_			       =  query_table(
                  table=self.dataset._data  ,     key=slice(__a  ,     offset + batch_size					)  ,     indices=self.dataset._indices if self.dataset._indices is not None else None  ,     )
              writer.write_table(__a					)
              written += batch.nbytes
         writer.close()
         return written











'''simple docstring'''

import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


a   	=    logging.get_logger(__name__)

a   	=    {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

a   	=    {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

a   	=    {
    "abeja/gpt-neox-japanese-2.7b": 2048,
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   with open(__UpperCAmelCase				,						"""r"""				,						encoding="""utf-8"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							json.loads(f.read()   )

   __SCREAMING_SNAKE_CASE   				=							collections.OrderedDict()
   __SCREAMING_SNAKE_CASE   				=							collections.OrderedDict()
   __SCREAMING_SNAKE_CASE   				=							collections.OrderedDict()
   with open(__UpperCAmelCase				,						"""r"""				,						encoding="""utf-8"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.readlines()
   __SCREAMING_SNAKE_CASE   				=							[[t.rstrip("""\n"""   )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""   ).split(""","""   ) for t in token]
   for idx, b in enumerate(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							b
      __SCREAMING_SNAKE_CASE   				=							idx
      for wd in b:
         __SCREAMING_SNAKE_CASE   				=							idx

   return vocab, raw_vocab, ids_to_tokens, emoji







class __a			( _snake_case      ):
   __UpperCamelCase :			Tuple						   =						VOCAB_FILES_NAMES
   __UpperCamelCase :			Tuple						   =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase :			Any						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase :			Union[str, Any]						   =						['input_ids', 'attention_mask']





   def __init__(       self					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      str="<|endoftext|>"					,lowerCamelCase					:      List[Any]="<|endoftext|>"					,lowerCamelCase					:      str="<|startoftext|>"					,lowerCamelCase					:      Dict="<|endoftext|>"					,lowerCamelCase					:      int=False					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      super().__init__(
          unk_token=lowerCamelCase					,pad_token=lowerCamelCase					,bos_token=lowerCamelCase					,eos_token=lowerCamelCase					,do_clean_text=lowerCamelCase					,**lowerCamelCase					,)
      if not os.path.isfile(lowerCamelCase     ):
         raise ValueError(
             f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
             """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""     )
      if not os.path.isfile(lowerCamelCase     ):
         raise ValueError(
             f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
             """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""     )
      __SCREAMING_SNAKE_CASE   				=							do_clean_text
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							load_vocab_and_emoji(lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							SubWordJapaneseTokenizer(
          vocab=self.vocab					,ids_to_tokens=self.ids_to_tokens					,emoji=self.emoji     )





   @property
   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return len(self.raw_vocab     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return dict(self.raw_vocab					,**self.added_tokens_encoder     )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      return self.subword_tokenizer.tokenize(lowerCamelCase					,clean=self.do_clean_text     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      return self.vocab.get(lowerCamelCase					,self.vocab.get(self.unk_token     )     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      return self.subword_tokenizer.convert_id_to_token(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""""".join(lowerCamelCase     ).strip()
      return out_string





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      "Conversation"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCamelCase					,add_special_tokens=lowerCamelCase     ) + [self.eos_token_id]     )

      if len(lowerCamelCase     ) > self.model_max_length:
         __SCREAMING_SNAKE_CASE   				=							input_ids[-self.model_max_length :]
      return input_ids





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[str] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0
      if os.path.isdir(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							os.path.join(
             lowerCamelCase					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(
             lowerCamelCase					,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]     )
      else:
         __SCREAMING_SNAKE_CASE   				=							(
             (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
         )
         __SCREAMING_SNAKE_CASE   				=							(
             (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
         )
      with open(lowerCamelCase					,"""w"""					,encoding="""utf-8"""     ) as writer:
         for token_index, token in self.ids_to_tokens.items():
            if index != token_index:
               logger.warning(
                   f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                   """ Please check that the vocabulary is not corrupted!"""     )
               __SCREAMING_SNAKE_CASE   				=							token_index
            writer.write(""",""".join(lowerCamelCase     ) + """\n"""     )
            index += 1
      with open(lowerCamelCase					,"""w"""					,encoding="""utf-8"""     ) as writer:
         json.dump(self.emoji					,lowerCamelCase     )
      return vocab_file, emoji_file







class __a			( _snake_case      ):





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							vocab  # same as swe
      __SCREAMING_SNAKE_CASE   				=							ids_to_tokens  # same as bpe
      __SCREAMING_SNAKE_CASE   				=							emoji
      __SCREAMING_SNAKE_CASE   				=							np.max([len(lowerCamelCase     ) for w in self.vocab.keys()]     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(
          r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(
          r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""     )
      __SCREAMING_SNAKE_CASE   				=							re.compile(
          r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""     )
      __SCREAMING_SNAKE_CASE   				=							"""─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
      __SCREAMING_SNAKE_CASE   				=							"""▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
      __SCREAMING_SNAKE_CASE   				=							str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}     )





   def __len__(       self					:      List[str]     ):

      '''simple docstring'''

      return len(self.ids_to_tokens     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<URL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<EMAIL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<TEL>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<DATE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<DATE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							self.content_repattera.sub("""<PRICE>"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							content.translate(self.content_transa     )
      while "<BLOCK><BLOCK>" in content:
         __SCREAMING_SNAKE_CASE   				=							content.replace("""<BLOCK><BLOCK>"""					,"""<BLOCK>"""     )
      return content





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							text.replace(""" """					,"""<SP>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""　"""					,"""<SP>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\r\n"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\n"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\r"""					,"""<BR>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""\t"""					,"""<TAB>"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""—"""					,"""ー"""     )
      __SCREAMING_SNAKE_CASE   				=							text.replace("""−"""					,"""ー"""     )
      for k, v in self.emoji["emoji"].items():
         if k in text:
            __SCREAMING_SNAKE_CASE   				=							text.replace(lowerCamelCase					,lowerCamelCase     )
      if clean:
         __SCREAMING_SNAKE_CASE   				=							self.clean_text(lowerCamelCase     )

      def check_simbol(lowerCamelCase					:      List[str]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 2:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 8) + int(e[1]     )
            if (
                (c >= 0xC_2A1 and c <= 0xC_2BF)
                or (c >= 0xC_780 and c <= 0xC_783)
                or (c >= 0xC_AB9 and c <= 0xC_BBF)
                or (c >= 0xC_C80 and c <= 0xC_DA2)
            ):
               return True
         return False

      def checkuae(lowerCamelCase					:      Union[str, Any]     ):
         __SCREAMING_SNAKE_CASE   				=							x.encode()
         if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 3:
            __SCREAMING_SNAKE_CASE   				=							(int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
            if c >= 0xE28_080 and c <= 0xE2B_07F:
               return True
         return False

      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							[]
      while pos < len(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							min(len(lowerCamelCase     )					,pos + self.maxlen + 1     ) if text[pos] == """<""" else pos + 3
         __SCREAMING_SNAKE_CASE   				=							[]  # (token_id, token, pos)
         for e in range(lowerCamelCase					,lowerCamelCase					,-1     ):
            __SCREAMING_SNAKE_CASE   				=							text[pos:e]
            if wd in self.vocab:
               if wd[0] == "<" and len(lowerCamelCase     ) > 2:
                  __SCREAMING_SNAKE_CASE   				=							[(self.vocab[wd], wd, e)]
                  break
               else:
                  candidates.append((self.vocab[wd], wd, e)     )
         if len(lowerCamelCase     ) > 0:
            # the smallest token_id is adopted
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							sorted(lowerCamelCase					,key=lambda lowerCamelCase     : x[0]     )[0]
            result.append(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							e
         else:
            __SCREAMING_SNAKE_CASE   				=							pos + 1
            __SCREAMING_SNAKE_CASE   				=							text[pos:end]
            if check_simbol(lowerCamelCase     ):
               result.append("""<KIGOU>"""     )
            elif checkuae(lowerCamelCase     ):
               result.append("""<U2000U2BFF>"""     )
            else:
               for i in wd.encode("""utf-8"""     ):
                  result.append("""<|byte%d|>""" % i     )
            __SCREAMING_SNAKE_CASE   				=							end
      return result





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]="\n"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							self.ids_to_tokens[index][0]
      if word[:6] == "<|byte" and word[-2:] == "|>":
         byte_tokens.append(int(word[6:-2]     )     )
      else:
         if len(lowerCamelCase     ) > 0:
            words.append(bytearray(lowerCamelCase     ).decode("""utf-8"""					,errors="""replace"""     )     )
            __SCREAMING_SNAKE_CASE   				=							[]
         if word[:7] == "<|emoji" and word[-2:] == "|>":
            words.append(self.emoji["""emoji_inv"""][word]     )
         elif word == "<SP>":
            words.append(""" """     )
         elif word == "<BR>":
            words.append(lowerCamelCase     )
         elif word == "<TAB>":
            words.append("""\t"""     )
         elif word == "<BLOCK>":
            words.append("""▀"""     )
         elif word == "<KIGOU>":
            words.append("""ǀ"""     )
         elif word == "<U2000U2BFF>":
            words.append("""‖"""     )
         else:
            words.append(lowerCamelCase     )
      if len(lowerCamelCase     ) > 0:
         words.append(bytearray(lowerCamelCase     ).decode("""utf-8"""					,errors="""replace"""     )     )
      __SCREAMING_SNAKE_CASE   				=							"""""".join(lowerCamelCase     )
      return text





