

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


UpperCamelCase__  :      Union[str, Any]			=    logging.get_logger(__name__)

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

UpperCamelCase__  :      int			=    {
    "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",
    },
}

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    with open(_SCREAMING_SNAKE_CASE ,   '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(_SCREAMING_SNAKE_CASE ,   '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(_SCREAMING_SNAKE_CASE       ):
        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       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Tuple						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	Union[str, Any]						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	Dict						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	List[Any]						 =  ['input_ids', 'attention_mask']

  def __init__(					self						,		_A						,		_A						,		_A="<|endoftext|>"						,		_A="<|endoftext|>"						,		_A="<|startoftext|>"						,		_A="<|endoftext|>"						,		_A=False						,		**_A						,		):
      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)`')
      SCREAMING_SNAKE_CASE_									=						do_clean_text
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						load_vocab_and_emoji(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						SubWordJapaneseTokenizer(
          vocab=self.vocab						,		ids_to_tokens=self.ids_to_tokens						,		emoji=self.emoji)

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

  def 						lowerCAmelCase__     (					self):
      return dict(self.raw_vocab						,		**self.added_tokens_encoder)

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.subword_tokenizer.tokenize(_A						,		clean=self.do_clean_text)

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.vocab.get(_A						,		self.vocab.get(self.unk_token))

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.subword_tokenizer.convert_id_to_token(_A)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''.join(_A).strip()
      return out_string

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      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:
          SCREAMING_SNAKE_CASE_									=						input_ids[-self.model_max_length :]
      return input_ids


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						0
      if os.path.isdir(_A):
          SCREAMING_SNAKE_CASE_									=						os.path.join(
              _A						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
          SCREAMING_SNAKE_CASE_									=						os.path.join(
              _A						,		(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(_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!')
                  SCREAMING_SNAKE_CASE_									=						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       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A):
      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(_A) 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):
      return len(self.ids_to_tokens)

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=False):
      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(_A						,		_A)
      if clean:
          SCREAMING_SNAKE_CASE_									=						self.clean_text(_A)

      def check_simbol(_A):
          SCREAMING_SNAKE_CASE_									=						x.encode()
          if len(_A) == 1 and len(_A) == 2:
              SCREAMING_SNAKE_CASE_									=						(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(_A):
          SCREAMING_SNAKE_CASE_									=						x.encode()
          if len(_A) == 1 and len(_A) == 3:
              SCREAMING_SNAKE_CASE_									=						(int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
              if c >= 0xe2_8080 and c <= 0xe2_b07f:
                  return True
          return False

      SCREAMING_SNAKE_CASE_									=						0
      SCREAMING_SNAKE_CASE_									=						[]
      while pos < len(_A):
          SCREAMING_SNAKE_CASE_									=						min(len(_A)						,		pos + self.maxlen + 1) if text[pos] == '<' else pos + 3
          SCREAMING_SNAKE_CASE_									=						[]  # (token_id, token, pos)
          for e in range(_A						,		_A						,		-1):
              SCREAMING_SNAKE_CASE_									=						text[pos:e]
              if wd in self.vocab:
                  if wd[0] == "<" and len(_A) > 2:
                      SCREAMING_SNAKE_CASE_									=						[(self.vocab[wd], wd, e)]
                      break
                  else:
                      candidates.append((self.vocab[wd], wd, e))
          if len(_A) > 0:
              # the smallest token_id is adopted
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						sorted(_A						,		key=lambda _A: x[0])[0]
              result.append(_A)
              SCREAMING_SNAKE_CASE_									=						e
          else:
              SCREAMING_SNAKE_CASE_									=						pos + 1
              SCREAMING_SNAKE_CASE_									=						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)
              SCREAMING_SNAKE_CASE_									=						end
      return result


  def 						lowerCAmelCase__     (					self						,		_A						,		_A="\n"):
      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(_A) > 0:
              words.append(bytearray(_A).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(_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'))
      SCREAMING_SNAKE_CASE_									=						''.join(_A)
      return text





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


