



'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


 '''simple docstring'''




 __magic_name__					: List[Any]  =      KandinskyVaaImgaImgPipeline
 __magic_name__					: Tuple  =      ["image_embeds", "negative_image_embeds", "image"]
 __magic_name__					: Tuple  =      [
     "image_embeds",
     "negative_image_embeds",
     "image",
 ]
 __magic_name__					: Tuple  =      [
     "generator",
     "height",
     "width",
     "strength",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 __magic_name__					: Optional[int]  =      False




 @property
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      return 3_2




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      return 3_2




 @property
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      return self.time_input_dim




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return self.time_input_dim * 4




 @property
 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







      return 1_0_0




 @property
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      torch.manual_seed(0)

      a_       		={
          "in_channels": 4,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      a_       		=UNetaDConditionModel(**lowerCAmelCase_)
      return model




 @property
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







      return {
          "block_out_channels": [3_2, 6_4],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }




 @property
 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      torch.manual_seed(0)
      a_       		=VQModel(**self.dummy_movq_kwargs)
      return model




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







      a_       		=self.dummy_unet
      a_       		=self.dummy_movq

      a_       		={
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.0_0_0_8_5,
          "beta_end": 0.0_1_2,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      a_       		=DDIMScheduler(**lowerCAmelCase_)

      a_       		={
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=0) ->      str:



      """simple docstring"""







      a_       		=floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
      a_       		=floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(seed + 1)).to(
          lowerCAmelCase_)
      # create init_image
      a_       		=floats_tensor((1, 3, 6_4, 6_4)							,  rng=random.Random(lowerCAmelCase_)).to(lowerCAmelCase_)
      a_       		=image.cpu().permute(0							,  2							,  3							,  1)[0]
      a_       		=Image.fromarray(np.uinta(lowerCAmelCase_)).convert("RGB").resize((2_5_6, 2_5_6))

      if str(lowerCAmelCase_).startswith("mps"):
           a_       		=torch.manual_seed(lowerCAmelCase_)
      else:
           a_       		=torch.Generator(device=lowerCAmelCase_).manual_seed(lowerCAmelCase_)
      a_       		={
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      a_       		="cpu"

      a_       		=self.get_dummy_components()

      a_       		=self.pipeline_class(**lowerCAmelCase_)
      a_       		=pipe.to(lowerCAmelCase_)

      pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=pipe(**self.get_dummy_inputs(lowerCAmelCase_))
      a_       		=output.images

      a_       		=pipe(
          **self.get_dummy_inputs(lowerCAmelCase_)							,  return_dict=lowerCAmelCase_							,  )[0]

      a_       		=image[0, -3:, -3:, -1]
      a_       		=image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      a_       		=np.array(
          [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3])
      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
      ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







@slow
@require_torch_gpu
class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self) ->      Dict:



      """simple docstring"""







      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_img2img_frog.npy")

      a_       		=load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
      a_       		="A red cartoon frog, 4k"

      a_       		=KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"							,  torch_dtype=torch.floataa)
      pipe_prior.to(lowerCAmelCase_)

      a_       		=KandinskyVaaImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-decoder"							,  torch_dtype=torch.floataa)
      a_       		=pipeline.to(lowerCAmelCase_)

      pipeline.set_progress_bar_config(disable=lowerCAmelCase_)

      a_       		=torch.Generator(device="cpu").manual_seed(0)
      a_		,						a_       		=pipe_prior(
          lowerCAmelCase_							,  generator=lowerCAmelCase_							,  num_inference_steps=5							,  negative_prompt=""							,  ).to_tuple()

      a_       		=pipeline(
          image=lowerCAmelCase_							,  image_embeds=lowerCAmelCase_							,  negative_image_embeds=lowerCAmelCase_							,  generator=lowerCAmelCase_							,  num_inference_steps=1_0_0							,  height=7_6_8							,  width=7_6_8							,  strength=0.2							,  output_type="np"							,  )

      a_       		=output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(lowerCAmelCase_							,  lowerCAmelCase_)







'''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''': 2_048,
}






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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

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

     return vocab, raw_vocab, ids_to_tokens, emoji







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      VOCAB_FILES_NAMES
 __magic_name__					: str  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: Union[str, Any]  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: str  =      ["input_ids", "attention_mask"]




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_="<|startoftext|>"							,  lowerCAmelCase_="<|endoftext|>"							,  lowerCAmelCase_=False							,  **lowerCAmelCase_							,  ) ->      List[Any]:



      """simple docstring"""







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




 @property
 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      return len(self.raw_vocab)




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		="".join(lowerCAmelCase_).strip()
      return out_string




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[int]:



      """simple docstring"""







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

      if len(lowerCAmelCase_) > self.model_max_length:
           a_       		=input_ids[-self.model_max_length :]
      return input_ids




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Tuple[str]:



      """simple docstring"""







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







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







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




 def __len__(   self) ->      Tuple:



      """simple docstring"""







      return len(self.ids_to_tokens)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=False) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=text.replace(" "							,  "<SP>")
      a_       		=text.replace("　"							,  "<SP>")
      a_       		=text.replace("\r\n"							,  "<BR>")
      a_       		=text.replace("\n"							,  "<BR>")
      a_       		=text.replace("\r"							,  "<BR>")
      a_       		=text.replace("\t"							,  "<TAB>")
      a_       		=text.replace("—"							,  "ー")
      a_       		=text.replace("−"							,  "ー")
      for k, v in self.emoji["emoji"].items():
           if k in text:
                a_       		=text.replace(lowerCAmelCase_							,  lowerCAmelCase_)
      if clean:
           a_       		=self.clean_text(lowerCAmelCase_)

      def check_simbol(lowerCAmelCase_):
           a_       		=x.encode()
           if len(lowerCAmelCase_) == 1 and len(lowerCAmelCase_) == 2:
                a_       		=(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(lowerCAmelCase_):
           a_       		=x.encode()
           if len(lowerCAmelCase_) == 1 and len(lowerCAmelCase_) == 3:
                a_       		=(int(e[0]) << 1_6) + (int(e[1]) << 8) + int(e[2])
                if c >= 0xe2_8080 and c <= 0xe2_b07f:
                     return True
           return False

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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_="\n") ->      List[Any]:



      """simple docstring"""







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


