

'''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


lowercase__         =      logging.get_logger(__name__)

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

lowercase__         =      {
    "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",
    },
}

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 with open(UpperCAmelCase_  ,	'r'  ,	encoding='utf-8'     ) as f:
  UpperCAmelCase	:     Union[str, Any]						 =			json.loads(f.read()     )

 UpperCAmelCase	:     List[str]						 =			collections.OrderedDict()
 UpperCAmelCase	:     Tuple						 =			collections.OrderedDict()
 UpperCAmelCase	:     Tuple						 =			collections.OrderedDict()
 with open(UpperCAmelCase_  ,	'r'  ,	encoding='utf-8'     ) as f:
  UpperCAmelCase	:     Optional[Any]						 =			f.readlines()
 UpperCAmelCase	:     Dict						 =			[[t.rstrip('\n'     )] if (t == ',' or ',' not in t) else t.rstrip('\n'     ).split(','     ) for t in token]
 for idx, b in enumerate(UpperCAmelCase_     ):
  UpperCAmelCase	:     List[Any]						 =			b
  UpperCAmelCase	:     int						 =			idx
  for wd in b:
   UpperCAmelCase	:     Optional[int]						 =			idx

 return vocab, raw_vocab, ids_to_tokens, emoji




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     Tuple  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     List[str]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     Optional[int]  		=  ["""input_ids""", """attention_mask"""]


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


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


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


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


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Union[str, Any]	)						->     Dict:
      return self.vocab.get(lowercase_					,      self.vocab.get(self.unk_token	)	)


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Any	)						->     Union[str, Any]:
      return self.subword_tokenizer.convert_id_to_token(lowercase_	)


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Union[str, Any]	)						->     Optional[Any]:
      UpperCAmelCase	:     Dict						 =			''.join(lowercase_	).strip()
      return out_string


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					"Conversation"	)						->     List[int]:
      UpperCAmelCase	:     Tuple						 =			[]
      for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(lowercase_					,      add_special_tokens=lowercase_	) + [self.eos_token_id]	)

      if len(lowercase_	) > self.model_max_length:
       UpperCAmelCase	:     str						 =			input_ids[-self.model_max_length :]
      return input_ids




     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					str					,      lowercase_			:					Optional[str] = None	)						->     Tuple[str]:
      UpperCAmelCase	:     Dict						 =			0
      if os.path.isdir(lowercase_	):
       UpperCAmelCase	:     Tuple						 =			os.path.join(
           lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
       UpperCAmelCase	:     List[str]						 =			os.path.join(
           lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']	)
      else:
       UpperCAmelCase	:     Any						 =			(
           (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
       )
       UpperCAmelCase	:     Optional[int]						 =			(
           (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
       )
      with open(lowercase_					,      '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	:     List[str]						 =			token_index
        writer.write(','.join(lowercase_	) + '\n'	)
        index += 1
      with open(lowercase_					,      'w'					,      encoding='utf-8'	) as writer:
       json.dump(self.emoji					,      lowercase_	)
      return vocab_file, emoji_file




class 	A_			(    _snake_case							):






     '''simple docstring'''



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


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


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[Any]	)						->     Tuple:
      UpperCAmelCase	:     Optional[Any]						 =			self.content_repattera.sub('<URL>'					,      lowercase_	)
      UpperCAmelCase	:     Optional[Any]						 =			self.content_repattera.sub('<EMAIL>'					,      lowercase_	)
      UpperCAmelCase	:     Any						 =			self.content_repattera.sub('<TEL>'					,      lowercase_	)
      UpperCAmelCase	:     str						 =			self.content_repattera.sub('<DATE>'					,      lowercase_	)
      UpperCAmelCase	:     Dict						 =			self.content_repattera.sub('<DATE>'					,      lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			self.content_repattera.sub('<PRICE>'					,      lowercase_	)
      UpperCAmelCase	:     int						 =			content.translate(self.content_transa	)
      while "<BLOCK><BLOCK>" in content:
       UpperCAmelCase	:     List[str]						 =			content.replace('<BLOCK><BLOCK>'					,      '<BLOCK>'	)
      return content


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					int					,      lowercase_			:					List[Any]=False	)						->     Dict:
      UpperCAmelCase	:     Dict						 =			text.replace(' '					,      '<SP>'	)
      UpperCAmelCase	:     Tuple						 =			text.replace('　'					,      '<SP>'	)
      UpperCAmelCase	:     Dict						 =			text.replace('\r\n'					,      '<BR>'	)
      UpperCAmelCase	:     Any						 =			text.replace('\n'					,      '<BR>'	)
      UpperCAmelCase	:     Any						 =			text.replace('\r'					,      '<BR>'	)
      UpperCAmelCase	:     List[Any]						 =			text.replace('\t'					,      '<TAB>'	)
      UpperCAmelCase	:     Dict						 =			text.replace('—'					,      'ー'	)
      UpperCAmelCase	:     List[str]						 =			text.replace('−'					,      'ー'	)
      for k, v in self.emoji["emoji"].items():
       if k in text:
        UpperCAmelCase	:     Dict						 =			text.replace(lowercase_					,      lowercase_	)
      if clean:
       UpperCAmelCase	:     Any						 =			self.clean_text(lowercase_	)

      def check_simbol(lowercase_			:					Optional[int]	):
       UpperCAmelCase	:     int						 =			x.encode()
       if len(lowercase_	) == 1 and len(lowercase_	) == 2:
        UpperCAmelCase	:     Tuple						 =			(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(lowercase_			:					List[Any]	):
       UpperCAmelCase	:     Dict						 =			x.encode()
       if len(lowercase_	) == 1 and len(lowercase_	) == 3:
        UpperCAmelCase	:     List[Any]						 =			(int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
        if c >= 0xE28080 and c <= 0xE2B07F:
         return True
       return False

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




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







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




