





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					Any      )->Tuple:






   '''simple docstring'''

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






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






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       1
   __lowerCAmelCase					:	List[str]      =       3
   __lowerCAmelCase					:	str      =       (32, 32)

   __lowerCAmelCase					:	List[str]      =       floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0      )      ).to(_snake_case      )
   return image






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






   '''simple docstring'''

   torch.manual_seed(0      )
   __lowerCAmelCase					:	List[str]      =       UNetaDConditionModel(
       block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,  cross_attention_dim=32       ,  )
   return model






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[str]:






   '''simple docstring'''

   torch.manual_seed(0      )
   __lowerCAmelCase					:	str      =       AutoencoderKL(
       block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,  latent_channels=4       ,  )
   return model






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   torch.manual_seed(0      )
   __lowerCAmelCase					:	Optional[int]      =       CLIPTextConfig(
       bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
   return CLIPTextModel(_snake_case      )






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






   '''simple docstring'''

   def extract(*_snake_case			:					Any       ,  **_snake_case			:					Dict      ):






    class        snake_case_    :






      def __init__(							self			:					Dict      )->str:






       '''simple docstring'''

       __lowerCAmelCase					:	List[str]      =       torch.ones([0]      )






      def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Dict      )->Union[str, Any]:






       '''simple docstring'''

       self.pixel_values.to(_snake_case      )
       return self



    return Out()

   return extract






  def 				UpperCAmelCase__						(							self			:					int      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
   __lowerCAmelCase					:	Dict      =       self.dummy_cond_unet
   __lowerCAmelCase					:	Union[str, Any]      =       DDIMScheduler(
       beta_start=0.00_085       ,  beta_end=0.012       ,  beta_schedule="""scaled_linear"""       ,  clip_sample=_snake_case       ,  set_alpha_to_one=_snake_case       ,  )

   __lowerCAmelCase					:	int      =       self.dummy_vae
   __lowerCAmelCase					:	Dict      =       self.dummy_text_encoder
   __lowerCAmelCase					:	str      =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # make sure here that pndm scheduler skips prk
   __lowerCAmelCase					:	int      =       StableDiffusionPipeline(
       unet=_snake_case       ,  scheduler=_snake_case       ,  vae=_snake_case       ,  text_encoder=_snake_case       ,  tokenizer=_snake_case       ,  safety_checker=_snake_case       ,  feature_extractor=self.dummy_extractor       ,  )
   __lowerCAmelCase					:	List[str]      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	Optional[int]      =       """A painting of a squirrel eating a burger"""

   __lowerCAmelCase					:	List[Any]      =       torch.Generator(device=_snake_case      ).manual_seed(0      )
   __lowerCAmelCase					:	Optional[Any]      =       sd_pipe([prompt]       ,  generator=_snake_case       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""      )
   __lowerCAmelCase					:	List[Any]      =       output.images

   __lowerCAmelCase					:	Tuple      =       torch.Generator(device=_snake_case      ).manual_seed(0      )
   __lowerCAmelCase					:	Any      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_snake_case       ,  )[0]

   __lowerCAmelCase					:	List[Any]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	int      =       image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   __lowerCAmelCase					:	List[Any]      =       np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]      )

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






  def 				UpperCAmelCase__						(							self			:					str      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
   __lowerCAmelCase					:	Dict      =       self.dummy_cond_unet
   __lowerCAmelCase					:	str      =       PNDMScheduler(skip_prk_steps=_snake_case      )
   __lowerCAmelCase					:	int      =       self.dummy_vae
   __lowerCAmelCase					:	Dict      =       self.dummy_text_encoder
   __lowerCAmelCase					:	Dict      =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # make sure here that pndm scheduler skips prk
   __lowerCAmelCase					:	Any      =       StableDiffusionPipeline(
       unet=_snake_case       ,  scheduler=_snake_case       ,  vae=_snake_case       ,  text_encoder=_snake_case       ,  tokenizer=_snake_case       ,  safety_checker=_snake_case       ,  feature_extractor=self.dummy_extractor       ,  )
   __lowerCAmelCase					:	Optional[Any]      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	Any      =       """A painting of a squirrel eating a burger"""
   __lowerCAmelCase					:	List[str]      =       torch.Generator(device=_snake_case      ).manual_seed(0      )
   __lowerCAmelCase					:	Dict      =       sd_pipe([prompt]       ,  generator=_snake_case       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""      )

   __lowerCAmelCase					:	Optional[int]      =       output.images

   __lowerCAmelCase					:	Union[str, Any]      =       torch.Generator(device=_snake_case      ).manual_seed(0      )
   __lowerCAmelCase					:	Optional[int]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_snake_case       ,  )[0]

   __lowerCAmelCase					:	Optional[Any]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	Optional[int]      =       image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   __lowerCAmelCase					:	Dict      =       np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]      )

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       StableDiffusionPipeline.from_pretrained(
       """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""       ,  safety_checker=_snake_case      )
   assert isinstance(_snake_case       ,  _snake_case      )
   assert isinstance(pipe.scheduler       ,  _snake_case      )
   assert pipe.safety_checker is None

   __lowerCAmelCase					:	str      =       pipe("""example prompt"""       ,  num_inference_steps=2      ).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
    pipe.save_pretrained(_snake_case      )
    __lowerCAmelCase					:	Optional[Any]      =       StableDiffusionPipeline.from_pretrained(_snake_case      )

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   __lowerCAmelCase					:	List[Any]      =       pipe("""example prompt"""       ,  num_inference_steps=2      ).images[0]
   assert image is not None






  @unittest.skipIf(torch_device != """cuda"""       ,  """This test requires a GPU"""      )
  def 				UpperCAmelCase__						(							self			:					str      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.dummy_cond_unet
   __lowerCAmelCase					:	str      =       PNDMScheduler(skip_prk_steps=_snake_case      )
   __lowerCAmelCase					:	List[str]      =       self.dummy_vae
   __lowerCAmelCase					:	str      =       self.dummy_text_encoder
   __lowerCAmelCase					:	Any      =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # put models in fp16
   __lowerCAmelCase					:	int      =       unet.half()
   __lowerCAmelCase					:	Optional[Any]      =       vae.half()
   __lowerCAmelCase					:	Tuple      =       bert.half()

   # make sure here that pndm scheduler skips prk
   __lowerCAmelCase					:	int      =       StableDiffusionPipeline(
       unet=_snake_case       ,  scheduler=_snake_case       ,  vae=_snake_case       ,  text_encoder=_snake_case       ,  tokenizer=_snake_case       ,  safety_checker=_snake_case       ,  feature_extractor=self.dummy_extractor       ,  )
   __lowerCAmelCase					:	Tuple      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	Optional[Any]      =       """A painting of a squirrel eating a burger"""
   __lowerCAmelCase					:	Dict      =       sd_pipe([prompt]       ,  num_inference_steps=2       ,  output_type="""np"""      ).images

   assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_snake_case      )
   __lowerCAmelCase					:	str      =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
   __lowerCAmelCase					:	Tuple      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	List[str]      =       (
       """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
       """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
       """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
       """ children from bahnhof zoo, detailed """
   )
   __lowerCAmelCase					:	List[str]      =       4003660346
   __lowerCAmelCase					:	List[str]      =       7

   # without safety guidance (sld_guidance_scale = 0)
   __lowerCAmelCase					:	int      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

   __lowerCAmelCase					:	Optional[int]      =       output.images
   __lowerCAmelCase					:	List[str]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	str      =       [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

   # without safety guidance (strong configuration)
   __lowerCAmelCase					:	List[Any]      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	Union[str, Any]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

   __lowerCAmelCase					:	Any      =       output.images
   __lowerCAmelCase					:	Dict      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	Any      =       [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_snake_case      )
   __lowerCAmelCase					:	Tuple      =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
   __lowerCAmelCase					:	List[Any]      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	str      =       """padme amidala taking a bath artwork, safe for work, no nudity"""
   __lowerCAmelCase					:	Tuple      =       2734971755
   __lowerCAmelCase					:	Dict      =       7

   __lowerCAmelCase					:	int      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	List[str]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

   __lowerCAmelCase					:	List[Any]      =       output.images
   __lowerCAmelCase					:	List[str]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	Optional[Any]      =       [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

   __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	Optional[Any]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

   __lowerCAmelCase					:	Tuple      =       output.images
   __lowerCAmelCase					:	Optional[Any]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	int      =       [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      )
   __lowerCAmelCase					:	Optional[int]      =       sd_pipe.to(_snake_case      )
   sd_pipe.set_progress_bar_config(disable=_snake_case      )

   __lowerCAmelCase					:	List[Any]      =       (
       """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
       """ leyendecker"""
   )
   __lowerCAmelCase					:	Optional[int]      =       1044355234
   __lowerCAmelCase					:	Optional[int]      =       12

   __lowerCAmelCase					:	Tuple      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	List[Any]      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

   __lowerCAmelCase					:	Optional[int]      =       output.images
   __lowerCAmelCase					:	List[Any]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	Union[str, Any]      =       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]      )

   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-7

   __lowerCAmelCase					:	List[str]      =       torch.manual_seed(_snake_case      )
   __lowerCAmelCase					:	Tuple      =       sd_pipe(
       [prompt]       ,  generator=_snake_case       ,  guidance_scale=_snake_case       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

   __lowerCAmelCase					:	Tuple      =       output.images
   __lowerCAmelCase					:	Optional[Any]      =       image[0, -3:, -3:, -1]
   __lowerCAmelCase					:	Any      =       np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]      )
   assert image.shape == (1, 512, 512, 3)

   assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2






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             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

_UpperCAmelCase             =							{
    '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             =							{
    'abeja/gpt-neox-japanese-2.7b': 2048,
}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   Optional[Any]:
 with open(SCREAMING_SNAKE_CASE   ,   """r"""   ,   encoding="""utf-8"""   ) as f:
  __lowerCAmelCase					:	int      =       json.loads(f.read()   )

 __lowerCAmelCase					:	Dict      =       collections.OrderedDict()
 __lowerCAmelCase					:	str      =       collections.OrderedDict()
 __lowerCAmelCase					:	Union[str, Any]      =       collections.OrderedDict()
 with open(SCREAMING_SNAKE_CASE   ,   """r"""   ,   encoding="""utf-8"""   ) as f:
  __lowerCAmelCase					:	Tuple      =       f.readlines()
 __lowerCAmelCase					:	Tuple      =       [[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   ):
  __lowerCAmelCase					:	Dict      =       b
  __lowerCAmelCase					:	Dict      =       idx
  for wd in b:
   __lowerCAmelCase					:	List[str]      =       idx

 return vocab, raw_vocab, ids_to_tokens, emoji






class        snake_case_    (		__lowercase						):
  A_      							=   VOCAB_FILES_NAMES
  A_      							=   PRETRAINED_VOCAB_FILES_MAP
  A_      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_      							=   ['input_ids', 'attention_mask']






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






   '''simple docstring'''

   super().__init__(
       unk_token=_snake_case       ,  pad_token=_snake_case       ,  bos_token=_snake_case       ,  eos_token=_snake_case       ,  do_clean_text=_snake_case       ,  **_snake_case       ,  )
   if not os.path.isfile(_snake_case      ):
    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(_snake_case      ):
    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)`"""      )
   __lowerCAmelCase					:	Any      =       do_clean_text
   __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       load_vocab_and_emoji(_snake_case       ,  _snake_case      )
   __lowerCAmelCase					:	int      =       SubWordJapaneseTokenizer(
       vocab=self.vocab       ,  ids_to_tokens=self.ids_to_tokens       ,  emoji=self.emoji      )






  @property
  def 				UpperCAmelCase__						(							self			:					int      )->str:






   '''simple docstring'''

   return len(self.raw_vocab      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Any:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str      )->Optional[int]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					Optional[Any]      )->Any:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Any      )->int:






   '''simple docstring'''

   return self.subword_tokenizer.convert_id_to_token(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					int      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       """""".join(_snake_case      ).strip()
   return out_string






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					"Conversation"      )->List[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(_snake_case       ,  add_special_tokens=_snake_case      ) + [self.eos_token_id]      )

   if len(_snake_case      ) > self.model_max_length:
    __lowerCAmelCase					:	List[str]      =       input_ids[-self.model_max_length :]
   return input_ids






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					str       ,  _snake_case			:					Optional[str] = None      )->Tuple[str]:






   '''simple docstring'''

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






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Optional[Any]       ,  _snake_case			:					str       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Optional[int]      )->List[Any]:






   '''simple docstring'''

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






  def __len__(							self			:					int      )->int:






   '''simple docstring'''

   return len(self.ids_to_tokens      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Any      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.content_repattera.sub("""<URL>"""       ,  _snake_case      )
   __lowerCAmelCase					:	Tuple      =       self.content_repattera.sub("""<EMAIL>"""       ,  _snake_case      )
   __lowerCAmelCase					:	Optional[Any]      =       self.content_repattera.sub("""<TEL>"""       ,  _snake_case      )
   __lowerCAmelCase					:	str      =       self.content_repattera.sub("""<DATE>"""       ,  _snake_case      )
   __lowerCAmelCase					:	Tuple      =       self.content_repattera.sub("""<DATE>"""       ,  _snake_case      )
   __lowerCAmelCase					:	Tuple      =       self.content_repattera.sub("""<PRICE>"""       ,  _snake_case      )
   __lowerCAmelCase					:	List[Any]      =       content.translate(self.content_transa      )
   while "<BLOCK><BLOCK>" in content:
    __lowerCAmelCase					:	str      =       content.replace("""<BLOCK><BLOCK>"""       ,  """<BLOCK>"""      )
   return content






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Optional[int]=False      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       text.replace(""" """       ,  """<SP>"""      )
   __lowerCAmelCase					:	Optional[int]      =       text.replace("""　"""       ,  """<SP>"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       text.replace("""\r\n"""       ,  """<BR>"""      )
   __lowerCAmelCase					:	Tuple      =       text.replace("""\n"""       ,  """<BR>"""      )
   __lowerCAmelCase					:	List[str]      =       text.replace("""\r"""       ,  """<BR>"""      )
   __lowerCAmelCase					:	Dict      =       text.replace("""\t"""       ,  """<TAB>"""      )
   __lowerCAmelCase					:	Dict      =       text.replace("""—"""       ,  """ー"""      )
   __lowerCAmelCase					:	Tuple      =       text.replace("""−"""       ,  """ー"""      )
   for k, v in self.emoji["emoji"].items():
    if k in text:
     __lowerCAmelCase					:	Optional[Any]      =       text.replace(_snake_case       ,  _snake_case      )
   if clean:
    __lowerCAmelCase					:	List[Any]      =       self.clean_text(_snake_case      )

   def check_simbol(_snake_case			:					List[str]      ):
    __lowerCAmelCase					:	Optional[int]      =       x.encode()
    if len(_snake_case      ) == 1 and len(_snake_case      ) == 2:
     __lowerCAmelCase					:	Optional[Any]      =       (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(_snake_case			:					Union[str, Any]      ):
    __lowerCAmelCase					:	Dict      =       x.encode()
    if len(_snake_case      ) == 1 and len(_snake_case      ) == 3:
     __lowerCAmelCase					:	List[str]      =       (int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
     if c >= 0xe2_8080 and c <= 0xe2_b07f:
      return True
    return False

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






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					List[Any]="\n"      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       []
   __lowerCAmelCase					:	Union[str, Any]      =       []
   __lowerCAmelCase					:	Optional[Any]      =       self.ids_to_tokens[index][0]
   if word[:6] == "<|byte" and word[-2:] == "|>":
    byte_tokens.append(int(word[6:-2]      )      )
   else:
    if len(_snake_case      ) > 0:
     words.append(bytearray(_snake_case      ).decode("""utf-8"""       ,  errors="""replace"""      )      )
     __lowerCAmelCase					:	Optional[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(_snake_case      )
    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(_snake_case      )
   if len(_snake_case      ) > 0:
    words.append(bytearray(_snake_case      ).decode("""utf-8"""       ,  errors="""replace"""      )      )
   __lowerCAmelCase					:	Dict      =       """""".join(_snake_case      )
   return text