code: import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
  					__UpperCamelCase						=   PaintByExamplePipeline
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							torch.manual_seed(0						)
  							__snake_case		       =     UNetaDConditionModel(
  							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
  							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
  							torch.manual_seed(0						)
  							__snake_case		       =     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      ,)
  							torch.manual_seed(0						)
  							__snake_case		       =     CLIPVisionConfig(
  							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
  							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
  							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)
  
  							__snake_case		       =     {
  							    "unet": unet,
  							    "scheduler": scheduler,
  							    "vae": vae,
  							    "image_encoder": image_encoder,
  							    "safety_checker": None,
  							    "feature_extractor": feature_extractor,
  							}
  							return components
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.array(image.convert("RGB"						)						)
  							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
  							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
  							return image
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)
  
  							if str(_lowerCAmelCase						).startswith("mps"						):
  									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
  							else:
  									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     {
  							    "example_image": example_image,
  							    "image": init_image,
  							    "mask_image": mask_image,
  							    "generator": generator,
  							    "num_inference_steps": 2,
  							    "guidance_scale": 6.0,
  							    "output_type": "numpy",
  							}
  							return inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
  							__snake_case		       =     pipe.to("cpu"						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     self.get_dummy_inputs()
  							__snake_case		       =     pipe(**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "cpu"
  							__snake_case		       =     self.get_dummy_inputs()
  							inputs.pop("mask_image"						)
  							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
  							__snake_case		       =     image.clamp(0      ,1						) / 2
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]
  
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  
  							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
  							__snake_case		       =     output.images
  
  							assert out_a.shape == (1, 64, 64, 3)
  							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/dog_in_bucket.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/mask.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/panda.jpg"						)
  
  							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     torch.manual_seed(321						)
  							__snake_case		       =     pipe(
  							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)
  
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 512, 512, 3)
  							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  
style_context: import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  lowerCamelCase__           =				{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowerCamelCase__           =				{
      'vocab_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
          ),
      },
      'merges_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
          ),
      },
  }
  
  lowerCamelCase__           =				{
      'roberta-base': 5_12,
      'roberta-large': 5_12,
      'roberta-large-mnli': 5_12,
      'distilroberta-base': 5_12,
      'roberta-base-openai-detector': 5_12,
      'roberta-large-openai-detector': 5_12,
  }
  
  
  
  @lru_cache()
  def 							_lowerCamelCase(				)  ->   List[str]:
  		__snake_case		       =     (
  		    list(range(ord("!"	)						,						ord("~"	) + 1	)	) + list(range(ord("¡"	)						,						ord("¬"	) + 1	)	) + list(range(ord("®"	)						,						ord("ÿ"	) + 1	)	)
  		)
  		__snake_case		       =     bs[:]
  		__snake_case		       =     0
  		for b in range(2**8	):
  				if b not in bs:
  						bs.append(__snake_case	)
  						cs.append(2**8 + n	)
  						n += 1
  		__snake_case		       =     [chr(__snake_case	) for n in cs]
  		return dict(zip(__snake_case						,						__snake_case	)	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   Optional[Any]:
  		__snake_case		       =     set()
  		__snake_case		       =     word[0]
  		for char in word[1:]:
  				pairs.add((prev_char, char)	)
  				__snake_case		       =     char
  		return pairs
  
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   VOCAB_FILES_NAMES
  					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
  					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]
  
  
  
  
  					def __init__( self :					str      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Union[str, Any]="replace"      ,_lowerCAmelCase :					List[Any]="<s>"      ,_lowerCAmelCase :					Union[str, Any]="</s>"      ,_lowerCAmelCase :					Any="</s>"      ,_lowerCAmelCase :					Optional[int]="<s>"      ,_lowerCAmelCase :					int="<unk>"      ,_lowerCAmelCase :					Tuple="<pad>"      ,_lowerCAmelCase :					Optional[Any]="<mask>"      ,_lowerCAmelCase :					List[str]=False      ,**_lowerCAmelCase :					Union[str, Any]      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else bos_token
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else eos_token
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else sep_token
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else cls_token
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else unk_token
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else pad_token
  
  							# Mask token behave like a normal word, i.e. include the space before it
  							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else mask_token
  
  							super().__init__(
  							    errors=_lowerCAmelCase      ,bos_token=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,sep_token=_lowerCAmelCase      ,cls_token=_lowerCAmelCase      ,pad_token=_lowerCAmelCase      ,mask_token=_lowerCAmelCase      ,add_prefix_space=_lowerCAmelCase      ,**_lowerCAmelCase      ,)
  
  							with open(_lowerCAmelCase      ,encoding="utf-8"						) as vocab_handle:
  									__snake_case		       =     json.load(_lowerCAmelCase						)
  							__snake_case		       =     {v: k for k, v in self.encoder.items()}
  							__snake_case		       =     errors  # how to handle errors in decoding
  							__snake_case		       =     bytes_to_unicode()
  							__snake_case		       =     {v: k for k, v in self.byte_encoder.items()}
  							with open(_lowerCAmelCase      ,encoding="utf-8"						) as merges_handle:
  									__snake_case		       =     merges_handle.read().split("\n"						)[1:-1]
  							__snake_case		       =     [tuple(merge.split()						) for merge in bpe_merges]
  							__snake_case		       =     dict(zip(_lowerCAmelCase      ,range(len(_lowerCAmelCase						)						)						)						)
  							__snake_case		       =     {}
  							__snake_case		       =     add_prefix_space
  
  							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  							__snake_case		       =     re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return len(self.encoder						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return dict(self.encoder      ,**self.added_tokens_encoder						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if token in self.cache:
  									return self.cache[token]
  							__snake_case		       =     tuple(_lowerCAmelCase						)
  							__snake_case		       =     get_pairs(_lowerCAmelCase						)
  
  							if not pairs:
  									return token
  
  							while True:
  									__snake_case		       =     min(_lowerCAmelCase      ,key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase      ,float("inf"						)						)						)
  									if bigram not in self.bpe_ranks:
  											break
  									__snake_case       ,       __snake_case		       =     bigram
  									__snake_case		       =     []
  									__snake_case		       =     0
  									while i < len(_lowerCAmelCase						):
  											try:
  													__snake_case		       =     word.index(_lowerCAmelCase      ,_lowerCAmelCase						)
  											except ValueError:
  													new_word.extend(word[i:]						)
  													break
  											else:
  													new_word.extend(word[i:j]						)
  													__snake_case		       =     j
  
  											if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
  													new_word.append(first + second						)
  													i += 2
  											else:
  													new_word.append(word[i]						)
  													i += 1
  									__snake_case		       =     tuple(_lowerCAmelCase						)
  									__snake_case		       =     new_word
  									if len(_lowerCAmelCase						) == 1:
  											break
  									else:
  											__snake_case		       =     get_pairs(_lowerCAmelCase						)
  							__snake_case		       =     " ".join(_lowerCAmelCase						)
  							__snake_case		       =     word
  							return word
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							for token in re.findall(self.pat      ,_lowerCAmelCase						):
  									__snake_case		       =     "".join(
  									    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase						).split(" "						)						)
  							return bpe_tokens
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.encoder.get(_lowerCAmelCase      ,self.encoder.get(self.unk_token						)						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.decoder.get(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "".join(_lowerCAmelCase						)
  							__snake_case		       =     bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"      ,errors=self.errors						)
  							return text
  
  
  
  
  					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not os.path.isdir(_lowerCAmelCase						):
  									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
  									return
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)
  
  							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as f:
  									f.write(json.dumps(self.encoder      ,indent=2      ,sort_keys=_lowerCAmelCase      ,ensure_ascii=_lowerCAmelCase						) + "\n"						)
  
  							__snake_case		       =     0
  							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as writer:
  									writer.write("#version: 0.2\n"						)
  									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,key=lambda _lowerCAmelCase						: kv[1]						):
  											if index != token_index:
  													logger.warning(
  													    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
  													    " Please check that the tokenizer is not corrupted!"						)
  													__snake_case		       =     token_index
  											writer.write(" ".join(_lowerCAmelCase						) + "\n"						)
  											index += 1
  
  							return vocab_file, merge_file
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if token_ids_a is None:
  									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  							__snake_case		       =     [self.cls_token_id]
  							__snake_case		       =     [self.sep_token_id]
  							return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None      ,_lowerCAmelCase :					bool = False						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if already_has_special_tokens:
  									return super().get_special_tokens_mask(
  									    token_ids_a=_lowerCAmelCase      ,token_ids_a=_lowerCAmelCase      ,already_has_special_tokens=_lowerCAmelCase						)
  
  							if token_ids_a is None:
  									return [1] + ([0] * len(_lowerCAmelCase						)) + [1]
  							return [1] + ([0] * len(_lowerCAmelCase						)) + [1, 1] + ([0] * len(_lowerCAmelCase						)) + [1]
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     [self.sep_token_id]
  							__snake_case		       =     [self.cls_token_id]
  
  							if token_ids_a is None:
  									return len(cls + token_ids_a + sep						) * [0]
  							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Optional[Any]=False      ,**_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     kwargs.pop("add_prefix_space"      ,self.add_prefix_space						)
  							if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase						) > 0 and not text[0].isspace()):
  									__snake_case		       =     " " + text
  							return (text, kwargs)
  
  
  
  
  
  