'''simple docstring'''





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE_:					Any  =    logging.getLogger(__name__)





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser(
         description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."      )
     parser.add_argument(
         "--dataset_name" ,   type=a__ ,   default="wikitext" ,   help="Name of the training. Explore datasets at: hf.co/datasets." ,   )
     parser.add_argument(
         "--dataset_config" ,   type=a__ ,   default="wikitext-103-raw-v1" ,   help="Configuration name of the dataset."      )
     parser.add_argument(
         "--tokenizer_name_or_path" ,   type=a__ ,   default="sayakpaul/unigram-tokenizer-wikitext" ,   help="Tokenizer identifier. Can be a local filepath or a Hub identifier." ,   )
     parser.add_argument(
         "--shard_size" ,   type=a__ ,   default=10_00 ,   help="Number of entries to go in a single shard." ,   )
     parser.add_argument("--split" ,   type=a__ ,   default="train" ,   choices=["train", "test", "validation"]      )
     parser.add_argument(
         "--limit" ,   default=a__ ,   type=a__ ,   help="Limit the number of shards (used for debugging)." ,   )
     parser.add_argument(
         "--max_length" ,   type=a__ ,   default=5_12 ,   help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
         " sequence length that is a multiple of 8." ,   )
     parser.add_argument(
         "--output_dir" ,   default="tf-tpu" ,   type=a__ ,   help="Output directory where the TFRecord shards will be saved. If the"
         " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
         " shards will be directly saved to a Google Cloud Storage bucket." ,   )

     UpperCAmelCase_	   =       parser.parse_args()
     return args





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				Tuple:


     '''simple docstring'''




     def fn(snake_case_	:			Union[str, Any]      ):
          return tokenizer(examples["text"]      )

     return fn





def      lowerCAmelCase_    ( snake_case_	:			List[Any]      )   ->				Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     for i in range(len(tokenized_data["input_ids"]      )      ):
          UpperCAmelCase_	   =       {
              "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]      )      ),
              "attention_mask": tf.train.Feature(
                  intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]      )      ),
          }
          UpperCAmelCase_	   =       tf.train.Features(feature=a__      )
          UpperCAmelCase_	   =       tf.train.Example(features=a__      )
          UpperCAmelCase_	   =       example.SerializeToString()
          records.append(a__      )
     return records





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


     '''simple docstring'''




     UpperCAmelCase_	   =       datasets.load_dataset(args.dataset_name ,   args.dataset_config ,   split=args.split      )

     if args.limit is not None:
          UpperCAmelCase_	   =       min(len(a__      ) ,   args.limit      )
          UpperCAmelCase_	   =       dataset.select(range(a__      )      )
          print(f"""Limiting the dataset to {args.limit} entries."""      )

     UpperCAmelCase_	   =       AutoTokenizer.from_pretrained(args.tokenizer_name_or_path      )

     # Handle output directory creation.
     # For serializing into a Google Cloud Storage Bucket, one needs to first
     # create a bucket.
     if "gs" not in args.output_dir:
          if not os.path.exists(args.output_dir      ):
               os.makedirs(args.output_dir      )
          UpperCAmelCase_	   =       os.path.join(args.output_dir ,   args.split      )
          if not os.path.exists(a__      ):
               os.makedirs(a__      )
     else:
          UpperCAmelCase_	   =       os.path.join(args.output_dir ,   args.split      )

     # Tokenize the whole dataset at once.
     UpperCAmelCase_	   =       tokenize_function(a__      )
     UpperCAmelCase_	   =       dataset.map(a__ ,   batched=a__ ,   num_proc=4 ,   remove_columns=["text"]      )

     # We need to concatenate all our texts together, and then split the result
     # into chunks of a fixed size, which we will call block_size. To do this, we
     # will use the map method again, with the option batched=True. When we use batched=True,
     # the function we pass to map() will be passed multiple inputs at once, allowing us
     # to group them into more or fewer examples than we had in the input.
     # This allows us to create our new fixed-length samples. The advantage of this
     # method is that we don't lose a whole lot of content from the dataset compared to the
     # case where we simply tokenize with a pre-defined max_length.

     def group_texts(snake_case_	:			Optional[Any]      ):
          # Concatenate all texts.
          UpperCAmelCase_	   =       {k: sum(examples[k] ,   []      ) for k in examples.keys()}
          UpperCAmelCase_	   =       len(concatenated_examples[list(examples.keys()      )[0]]      )
          # We drop the small remainder, though you could add padding instead if the model supports it
          # In this, as in all things, we advise you to follow your heart 🫀
          UpperCAmelCase_	   =       (total_length // args.max_length) * args.max_length
          # Split by chunks of max_len.
          UpperCAmelCase_	   =       {
              k: [t[i : i + args.max_length] for i in range(0 ,   a__ ,   args.max_length      )]
              for k, t in concatenated_examples.items()
          }
          return result

     UpperCAmelCase_	   =       dataset_tokenized.map(a__ ,   batched=a__ ,   batch_size=10_00 ,   num_proc=4      )

     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     for shard in range(0 ,   len(a__      ) ,   args.shard_size      ):
          UpperCAmelCase_	   =       grouped_dataset[shard : shard + args.shard_size]
          UpperCAmelCase_	   =       len(dataset_snapshot["input_ids"]      )
          UpperCAmelCase_	   =       os.path.join(a__ ,   f"""dataset-{shard_count}-{records_containing}.tfrecord"""      )
          UpperCAmelCase_	   =       get_serialized_examples(a__      )

          with tf.io.TFRecordWriter(a__      ) as out_file:
               for i in range(len(a__      )      ):
                    UpperCAmelCase_	   =       serialized_examples[i]
                    out_file.write(a__      )
               print("Wrote file {} containing {} records".format(a__ ,   a__      )      )

          shard_count += 1
          total_records += records_containing

     with open(f"""split-{args.split}-records-count.txt""" ,   "w"      ) as f:
          print(f"""Total {args.split} records: {total_records}""" ,   file=a__      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					int  =    parse_args()
    main(args)





'''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


SCREAMING_SNAKE_CASE_:					Tuple  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[str]  ={'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={
    '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',
    },
}

SCREAMING_SNAKE_CASE_:					Optional[int]  ={
    'abeja/gpt-neox-japanese-2.7b': 20_48,
}





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Tuple      )   ->				Union[str, Any]:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     UpperCAmelCase_	   =       collections.OrderedDict()
     with open(snake_case_ ,   "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(snake_case_      ):
          UpperCAmelCase_	   =       b
          UpperCAmelCase_	   =       idx
          for wd in b:
               UpperCAmelCase_	   =       idx

     return vocab, raw_vocab, ids_to_tokens, emoji




class 	__A				(			UpperCamelCase__     ):
    a__	:						List[str]	       =  VOCAB_FILES_NAMES
    a__	:						Tuple	       =  PRETRAINED_VOCAB_FILES_MAP
    a__	:						Union[str, Any]	       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]	       =  ["""input_ids""", """attention_mask"""]



    def __init__(self   :   Any       ,      __a   :   List[Any]       ,      __a   :   Dict       ,      __a   :   int="<|endoftext|>"       ,      __a   :   Union[str, Any]="<|endoftext|>"       ,      __a   :   int="<|startoftext|>"       ,      __a   :   Tuple="<|endoftext|>"       ,      __a   :   Optional[int]=False       ,      **__a   :   int       ,      ):
         super().__init__(
             unk_token=__a       ,      pad_token=__a       ,      bos_token=__a       ,      eos_token=__a       ,      do_clean_text=__a       ,      **__a       ,      )
         if not os.path.isfile(__a			):
              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(__a			):
              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(__a       ,      __a			)
         UpperCAmelCase_	   =       SubWordJapaneseTokenizer(
             vocab=self.vocab       ,      ids_to_tokens=self.ids_to_tokens       ,      emoji=self.emoji			)



    @property
    def  _lowercase						(self   :   Optional[Any]			):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab			)



    def  _lowercase						(self   :   List[Any]			):
         return dict(self.raw_vocab       ,      **self.added_tokens_encoder			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   int			):
         return self.subword_tokenizer.tokenize(__a       ,      clean=self.do_clean_text			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   List[str]			):
         return self.vocab.get(__a       ,      self.vocab.get(self.unk_token			)			)



    def  _lowercase						(self   :   int       ,      __a   :   List[Any]			):
         return self.subword_tokenizer.convert_id_to_token(__a			)



    def  _lowercase						(self   :   Dict       ,      __a   :   str			):
         UpperCAmelCase_	   =       "".join(__a			).strip()
         return out_string



    def  _lowercase						(self   :   int       ,      __a   :   "Conversation"			):
         UpperCAmelCase_	   =       []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a       ,      add_special_tokens=__a			) + [self.eos_token_id]			)

         if len(__a			) > self.model_max_length:
              UpperCAmelCase_	   =       input_ids[-self.model_max_length :]
         return input_ids






    def  _lowercase						(self   :   int       ,      __a   :   str       ,      __a   :   Optional[str] = None			):
         UpperCAmelCase_	   =       0
         if os.path.isdir(__a			):
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
              UpperCAmelCase_	   =       os.path.join(
                  __a       ,      (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(__a       ,      "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(__a			) + "\n"			)
                   index += 1
         with open(__a       ,      "w"       ,      encoding="utf-8"			) as writer:
              json.dump(self.emoji       ,      __a			)
         return vocab_file, emoji_file




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[Any]       ,      __a   :   Dict       ,      __a   :   Any       ,      __a   :   int			):
         UpperCAmelCase_	   =       vocab  # same as swe
         UpperCAmelCase_	   =       ids_to_tokens  # same as bpe
         UpperCAmelCase_	   =       emoji
         UpperCAmelCase_	   =       np.max([len(__a			) 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   :   Dict			):
         return len(self.ids_to_tokens			)



    def  _lowercase						(self   :   str       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.content_repattera.sub("<URL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<EMAIL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<TEL>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<DATE>"       ,      __a			)
         UpperCAmelCase_	   =       self.content_repattera.sub("<PRICE>"       ,      __a			)
         UpperCAmelCase_	   =       content.translate(self.content_transa			)
         while "<BLOCK><BLOCK>" in content:
              UpperCAmelCase_	   =       content.replace("<BLOCK><BLOCK>"       ,      "<BLOCK>"			)
         return content



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Union[str, Any]       ,      __a   :   str=False			):
         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(__a       ,      __a			)
         if clean:
              UpperCAmelCase_	   =       self.clean_text(__a			)

         def check_simbol(__a   :   List[Any]			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 2:
                   UpperCAmelCase_	   =       (int(e[0]			) << 8) + int(e[1]			)
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(__a   :   Tuple			):
              UpperCAmelCase_	   =       x.encode()
              if len(__a			) == 1 and len(__a			) == 3:
                   UpperCAmelCase_	   =       (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        return True
              return False

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






    def  _lowercase						(self   :   int       ,      __a   :   Optional[Any]       ,      __a   :   Optional[int]="\n"			):
         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(__a			) > 0:
                   words.append(bytearray(__a			).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(__a			)
              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(__a			)
         if len(__a			) > 0:
              words.append(bytearray(__a			).decode("utf-8"       ,      errors="replace"			)			)
         UpperCAmelCase_	   =       "".join(__a			)
         return text




