



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__      					=     logging.get_logger(__name__)

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

SCREAMING_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",
    },
}

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




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict       ):





    '''simple docstring'''


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

    lowercase__		       =						collections.OrderedDict()
    lowercase__		       =						collections.OrderedDict()
    lowercase__		       =						collections.OrderedDict()
    with open(_snake_case     ,"r"     ,encoding="utf-8"       ) as f:
        lowercase__		       =						f.readlines()
    lowercase__		       =						[[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       ):
        lowercase__		       =						b
        lowercase__		       =						idx
        for wd in b:
            lowercase__		       =						idx

    return vocab, raw_vocab, ids_to_tokens, emoji


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Tuple						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :List[Any]						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Optional[int]						   =					["input_ids", "attention_mask"]



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|startoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_=False     ,**UpperCAmelCase_     ,)  ->							Tuple:
           super().__init__(
               unk_token=UpperCAmelCase_     ,pad_token=UpperCAmelCase_     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,do_clean_text=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           if not os.path.isfile(UpperCAmelCase_      ):
               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(UpperCAmelCase_      ):
               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)`"      )
           lowercase__		       =						do_clean_text
           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						load_vocab_and_emoji(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						SubWordJapaneseTokenizer(
               vocab=self.vocab     ,ids_to_tokens=self.ids_to_tokens     ,emoji=self.emoji      )



       @property
       def 							_a      (				self      )  ->							List[str]:
           # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
           return len(self.raw_vocab      )



       def 							_a      (				self      )  ->							Any:
           return dict(self.raw_vocab     ,**self.added_tokens_encoder      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           return self.subword_tokenizer.tokenize(UpperCAmelCase_     ,clean=self.do_clean_text      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[Any]:
           return self.vocab.get(UpperCAmelCase_     ,self.vocab.get(self.unk_token      )      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           return self.subword_tokenizer.convert_id_to_token(UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						"".join(UpperCAmelCase_      ).strip()
           return out_string



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[int]:
           lowercase__		       =						[]
           for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      ) + [self.eos_token_id]      )

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







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple[str]:
           lowercase__		       =						0
           if os.path.isdir(UpperCAmelCase_      ):
               lowercase__		       =						os.path.join(
                   UpperCAmelCase_     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
               lowercase__		       =						os.path.join(
                   UpperCAmelCase_     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]      )
           else:
               lowercase__		       =						(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
               )
               lowercase__		       =						(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
               )
           with open(UpperCAmelCase_     ,"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!"      )
                       lowercase__		       =						token_index
                   writer.write(",".join(UpperCAmelCase_      ) + "\n"      )
                   index += 1
           with open(UpperCAmelCase_     ,"w"     ,encoding="utf-8"      ) as writer:
               json.dump(self.emoji     ,UpperCAmelCase_      )
           return vocab_file, emoji_file





class 				snake_case (UpperCamelCase   ):



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



       def __len__(				self      )  ->							int:
           return len(self.ids_to_tokens      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						self.content_repattera.sub("<URL>"     ,UpperCAmelCase_      )
           lowercase__		       =						self.content_repattera.sub("<EMAIL>"     ,UpperCAmelCase_      )
           lowercase__		       =						self.content_repattera.sub("<TEL>"     ,UpperCAmelCase_      )
           lowercase__		       =						self.content_repattera.sub("<DATE>"     ,UpperCAmelCase_      )
           lowercase__		       =						self.content_repattera.sub("<DATE>"     ,UpperCAmelCase_      )
           lowercase__		       =						self.content_repattera.sub("<PRICE>"     ,UpperCAmelCase_      )
           lowercase__		       =						content.translate(self.content_transa      )
           while "<BLOCK><BLOCK>" in content:
               lowercase__		       =						content.replace("<BLOCK><BLOCK>"     ,"<BLOCK>"      )
           return content



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False      )  ->							Optional[Any]:
           lowercase__		       =						text.replace(" "     ,"<SP>"      )
           lowercase__		       =						text.replace("　"     ,"<SP>"      )
           lowercase__		       =						text.replace("\r\n"     ,"<BR>"      )
           lowercase__		       =						text.replace("\n"     ,"<BR>"      )
           lowercase__		       =						text.replace("\r"     ,"<BR>"      )
           lowercase__		       =						text.replace("\t"     ,"<TAB>"      )
           lowercase__		       =						text.replace("—"     ,"ー"      )
           lowercase__		       =						text.replace("−"     ,"ー"      )
           for k, v in self.emoji["emoji"].items():
               if k in text:
                   lowercase__		       =						text.replace(UpperCAmelCase_     ,UpperCAmelCase_      )
           if clean:
               lowercase__		       =						self.clean_text(UpperCAmelCase_      )

           def check_simbol(UpperCAmelCase_      ):
               lowercase__		       =						x.encode()
               if len(UpperCAmelCase_      ) == 1 and len(UpperCAmelCase_      ) == 2:
                   lowercase__		       =						(int(e[0]      ) << 8) + int(e[1]      )
                   if (
                       (c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
                       or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
                       or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
                       or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
                   ):
                       return True
               return False

           def checkuae(UpperCAmelCase_      ):
               lowercase__		       =						x.encode()
               if len(UpperCAmelCase_      ) == 1 and len(UpperCAmelCase_      ) == 3:
                   lowercase__		       =						(int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
                   if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
                       return True
               return False

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







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









'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           lowercase__		       =						params

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array([len(UpperCAmelCase_      ) for t in data]      )

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()



       def __getitem__(				self     ,UpperCAmelCase_      )  ->							Optional[int]:
           return (self.token_ids[index], self.lengths[index])



       def __len__(				self      )  ->							Union[str, Any]:
           return len(self.lengths      )



       def 							_a      (				self      )  ->							int:
           assert len(self.token_ids      ) == len(self.lengths      )
           assert all(self.lengths[i] == len(self.token_ids[i]      ) for i in range(len(self.lengths      )      )      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						self.params.max_model_input_size
           lowercase__		       =						self.lengths > max_len
           logger.info(F'''Splitting {sum(UpperCAmelCase_      )} too long sequences.'''      )

           def divide_chunks(UpperCAmelCase_     ,UpperCAmelCase_      ):
               return [l[i : i + n] for i in range(0     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )]

           lowercase__		       =						[]
           lowercase__		       =						[]
           if self.params.mlm:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
           else:
               lowercase__       ,       lowercase__		       =						self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

           for seq_, len_ in zip(self.token_ids     ,self.lengths      ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                   new_tok_ids.append(seq_      )
                   new_lengths.append(len_      )
               else:
                   lowercase__		       =						[]
                   for sub_s in divide_chunks(seq_     ,max_len - 2      ):
                       if sub_s[0] != cls_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,0     ,UpperCAmelCase_      )
                       if sub_s[-1] != sep_id:
                           lowercase__		       =						np.insert(UpperCAmelCase_     ,len(UpperCAmelCase_      )     ,UpperCAmelCase_      )
                       assert len(UpperCAmelCase_      ) <= max_len
                       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                       sub_seqs.append(UpperCAmelCase_      )

                   new_tok_ids.extend(UpperCAmelCase_      )
                   new_lengths.extend([len(UpperCAmelCase_      ) for l in sub_seqs]      )

           lowercase__		       =						np.array(UpperCAmelCase_      )
           lowercase__		       =						np.array(UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						len(self      )
           lowercase__		       =						self.lengths > 11
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''      )



       def 							_a      (				self      )  ->							List[Any]:
           if "unk_token" not in self.params.special_tok_ids:
               return
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						len(self      )
           lowercase__		       =						np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
           lowercase__		       =						(unk_occs / self.lengths) < 0.5
           lowercase__		       =						self.token_ids[indices]
           lowercase__		       =						self.lengths[indices]
           lowercase__		       =						len(self      )
           logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''      )



       def 							_a      (				self      )  ->							Optional[int]:
           if not self.params.is_master:
               return
           logger.info(F'''{len(self      )} sequences'''      )
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[t[0] for t in batch]
           lowercase__		       =						[t[1] for t in batch]
           assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

           # Max for paddings
           lowercase__		       =						max(UpperCAmelCase_      )

           # Pad token ids
           if self.params.mlm:
               lowercase__		       =						self.params.special_tok_ids["pad_token"]
           else:
               lowercase__		       =						self.params.special_tok_ids["unk_token"]
           lowercase__		       =						[list(t.astype(UpperCAmelCase_      )      ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase_      )) for t in token_ids]
           assert len(tk_      ) == len(UpperCAmelCase_      )
           assert all(len(UpperCAmelCase_      ) == max_seq_len_ for t in tk_      )

           lowercase__		       =						torch.tensor(tk_      )  # (bs, max_seq_len_)
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )  # (bs)
           return tk_t, lg_t




