
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


_snake_case      = logging.get_logger(__name__)

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

_snake_case      = {
    "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",
    },
}

_snake_case      = {
    "abeja/gpt-neox-japanese-2.7b": 2_048,
}

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Tuple:


     with open(_lowercase						,     'r'						,     encoding='utf-8'			) as f:
          UpperCamelCase						    =					json.loads(f.read()			)

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

     return vocab, raw_vocab, ids_to_tokens, emoji
class       _lowerCAmelCase  (		a_							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_				:					Dict       						=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_				:					int       						=['''input_ids''', '''attention_mask''']

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






      """simple docstring"""



      super().__init__(
          unk_token=_SCREAMING_SNAKE_CASE					, pad_token=_SCREAMING_SNAKE_CASE					, bos_token=_SCREAMING_SNAKE_CASE					, eos_token=_SCREAMING_SNAKE_CASE					, do_clean_text=_SCREAMING_SNAKE_CASE					, **_SCREAMING_SNAKE_CASE					, )
      if not os.path.isfile(_SCREAMING_SNAKE_CASE   ):
           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(_SCREAMING_SNAKE_CASE   ):
           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)`'   )
      UpperCamelCase						    =					do_clean_text
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					load_vocab_and_emoji(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )
      UpperCamelCase						    =					SubWordJapaneseTokenizer(
          vocab=self.vocab					, ids_to_tokens=self.ids_to_tokens					, emoji=self.emoji   )

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






      """simple docstring"""



      return len(self.raw_vocab   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      return self.subword_tokenizer.convert_id_to_token(_SCREAMING_SNAKE_CASE   )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					''.join(_SCREAMING_SNAKE_CASE   ).strip()
      return out_string

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



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

      if len(_SCREAMING_SNAKE_CASE   ) > self.model_max_length:
           UpperCamelCase						    =					input_ids[-self.model_max_length :]
      return input_ids






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str = None   ):






      """simple docstring"""



      UpperCamelCase						    =					0
      if os.path.isdir(_SCREAMING_SNAKE_CASE   ):
           UpperCamelCase						    =					os.path.join(
               _SCREAMING_SNAKE_CASE					, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
           UpperCamelCase						    =					os.path.join(
               _SCREAMING_SNAKE_CASE					, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']   )
      else:
           UpperCamelCase						    =					(
               (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
           )
           UpperCamelCase						    =					(
               (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
           )
      with open(_SCREAMING_SNAKE_CASE					, '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!'   )
                     UpperCamelCase						    =					token_index
                writer.write(','.join(_SCREAMING_SNAKE_CASE   ) + '\n'   )
                index += 1
      with open(_SCREAMING_SNAKE_CASE					, 'w'					, encoding='utf-8'   ) as writer:
           json.dump(self.emoji					, _SCREAMING_SNAKE_CASE   )
      return vocab_file, emoji_file




class       _lowerCAmelCase  (		a_							):

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



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

 def __len__(						self			:			List[str]   ):






      """simple docstring"""



      return len(self.ids_to_tokens   )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Dict=False   ):






      """simple docstring"""



      UpperCamelCase						    =					text.replace(' '					, '<SP>'   )
      UpperCamelCase						    =					text.replace('　'					, '<SP>'   )
      UpperCamelCase						    =					text.replace('\r\n'					, '<BR>'   )
      UpperCamelCase						    =					text.replace('\n'					, '<BR>'   )
      UpperCamelCase						    =					text.replace('\r'					, '<BR>'   )
      UpperCamelCase						    =					text.replace('\t'					, '<TAB>'   )
      UpperCamelCase						    =					text.replace('—'					, 'ー'   )
      UpperCamelCase						    =					text.replace('−'					, 'ー'   )
      for k, v in self.emoji["emoji"].items():
           if k in text:
                UpperCamelCase						    =					text.replace(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE   )
      if clean:
           UpperCamelCase						    =					self.clean_text(_SCREAMING_SNAKE_CASE   )

      def check_simbol(SCREAMING_SNAKE_CASE__			:			Dict   ):
           UpperCamelCase						    =					x.encode()
           if len(_SCREAMING_SNAKE_CASE   ) == 1 and len(_SCREAMING_SNAKE_CASE   ) == 2:
                UpperCamelCase						    =					(int(e[0]   ) << 8) + int(e[1]   )
                if (
                    (c >= 0xC2A1 and c <= 0xC2BF)
                    or (c >= 0xC780 and c <= 0xC783)
                    or (c >= 0xCAB9 and c <= 0xCBBF)
                    or (c >= 0xCC80 and c <= 0xCDA2)
                ):
                     return True
           return False

      def checkuae(SCREAMING_SNAKE_CASE__			:			List[str]   ):
           UpperCamelCase						    =					x.encode()
           if len(_SCREAMING_SNAKE_CASE   ) == 1 and len(_SCREAMING_SNAKE_CASE   ) == 3:
                UpperCamelCase						    =					(int(e[0]   ) << 16) + (int(e[1]   ) << 8) + int(e[2]   )
                if c >= 0xE2_8080 and c <= 0xE2_B07F:
                     return True
           return False

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






 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[Any]="\n"   ):






      """simple docstring"""



      UpperCamelCase						    =					[]
      UpperCamelCase						    =					[]
      UpperCamelCase						    =					self.ids_to_tokens[index][0]
      if word[:6] == "<|byte" and word[-2:] == "|>":
           byte_tokens.append(int(word[6:-2]   )   )
      else:
           if len(_SCREAMING_SNAKE_CASE   ) > 0:
                words.append(bytearray(_SCREAMING_SNAKE_CASE   ).decode('utf-8'					, errors='replace'   )   )
                UpperCamelCase						    =					[]
           if word[:7] == "<|emoji" and word[-2:] == "|>":
                words.append(self.emoji['emoji_inv'][word]   )
           elif word == "<SP>":
                words.append(' '   )
           elif word == "<BR>":
                words.append(_SCREAMING_SNAKE_CASE   )
           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(_SCREAMING_SNAKE_CASE   )
      if len(_SCREAMING_SNAKE_CASE   ) > 0:
           words.append(bytearray(_SCREAMING_SNAKE_CASE   ).decode('utf-8'					, errors='replace'   )   )
      UpperCamelCase						    =					''.join(_SCREAMING_SNAKE_CASE   )
      return text





'''simple docstring'''



import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 				_snake_case					(   datasets.BuilderConfig			):
  SCREAMING_SNAKE_CASE			:       Optional[datasets.Features]				=     None



class 				_snake_case					(   datasets.ArrowBasedBuilder			):
  SCREAMING_SNAKE_CASE			:       Tuple				=     PandasConfig

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return datasets.DatasetInfo(features=self.config.features	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if not self.config.data_files:
          raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'	)
      lowerCAmelCase 						=							dl_manager.download_and_extract(self.config.data_files	)
      if isinstance(_SCREAMING_SNAKE_CASE      , (str, list, tuple)	):
          lowerCAmelCase 						=							data_files
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      , gen_kwargs={'files': files}	)]
      lowerCAmelCase 						=							[]
      for split_name, files in data_files.items():
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      , gen_kwargs={'files': files}	)	)
      return splits

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if self.config.features is not None:
          # more expensive cast to support nested features with keys in a different order
          # allows str <-> int/float or str to Audio for example
          lowerCAmelCase 						=							table_cast(_SCREAMING_SNAKE_CASE      , self.config.features.arrow_schema	)
      return pa_table


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      for i, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE	)	):
          with open(_SCREAMING_SNAKE_CASE      , 'rb'	) as f:
              lowerCAmelCase 						=							pa.Table.from_pandas(pd.read_pickle(_SCREAMING_SNAKE_CASE	)	)
              yield i, self._cast_table(_SCREAMING_SNAKE_CASE	)





