





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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCamelCase   				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""					,   UpperCamelCase__					,   )
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		if isinstance(image[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      image[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 255.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 255.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


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



									"""simple docstring"""



									super().__init__()
									self.register_modules(unet=__A							,				scheduler=__A		)


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE__			=      generator[0] if isinstance(__A							,				__A		) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__			=      self.scheduler.step(__A							,				__A							,				__A							,				__A							,				__A							,				__A		).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE__			=      self.scheduler.undo_step(__A							,				__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      t

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)











import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					RoCBertTokenizer
							lowerCamelCase_           =					None
							lowerCamelCase_           =					False
							lowerCamelCase_           =					True
							lowerCamelCase_           =					filter_non_english


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



									"""simple docstring"""



									super().setUp()

									SCREAMING_SNAKE_CASE__			=      ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
									SCREAMING_SNAKE_CASE__			=      {}
									SCREAMING_SNAKE_CASE__			=      {}
									for i, value in enumerate(__A		):
											SCREAMING_SNAKE_CASE__			=      i
											SCREAMING_SNAKE_CASE__			=      i
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""word_shape_file"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""word_pronunciation_file"""]		)
									with open(self.vocab_file							,				"""w"""							,				encoding="""utf-8"""		) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
									with open(self.word_shape_file							,				"""w"""							,				encoding="""utf-8"""		) as word_shape_writer:
											json.dump(__A							,				__A							,				ensure_ascii=__A		)
									with open(self.word_pronunciation_file							,				"""w"""							,				encoding="""utf-8"""		) as word_pronunciation_writer:
											json.dump(__A							,				__A							,				ensure_ascii=__A		)


							def      _snake_case  (							self    :List[Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer_class(self.vocab_file							,				self.word_shape_file							,				self.word_pronunciation_file		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize("""你好[SEP]你是谁"""		)
									self.assertListEqual(__A							,				["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)							,				[5, 6, 2, 5, 7, 8]		)
									self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__A		)							,				[5, 6, 2, 5, 7, 8]		)
									self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__A		)							,				[5, 6, 2, 5, 7, 8]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer()

									self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""		)							,				["""ah""", """\u535A""", """\u63A8""", """zz"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)							,				["""hello""", """!""", """how""", """are""", """you""", """?"""]		)
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)							,				["""hello"""]		)


							def      _snake_case  (							self    :str		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A							,				strip_accents=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)							,				["""hällo""", """!""", """how""", """are""", """you""", """?"""]		)
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)							,				["""h\u00E9llo"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A							,				strip_accents=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)							,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)							,				["""hello"""]		)


							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)							,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)							,				["""hello"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)							,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


							def      _snake_case  (							self    :int		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A							,				strip_accents=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)							,				["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A							,				strip_accents=__A		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)							,				["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      RoCBertBasicTokenizer(do_lower_case=__A							,				never_split=["""[UNK]"""]		)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""		)							,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

									SCREAMING_SNAKE_CASE__			=      {}
									for i, token in enumerate(__A		):
											SCREAMING_SNAKE_CASE__			=      i
									SCREAMING_SNAKE_CASE__			=      RoCBertWordpieceTokenizer(vocab=__A							,				unk_token="""[UNK]"""		)

									self.assertListEqual(tokenizer.tokenize(""""""		)							,				[]		)

									self.assertListEqual(tokenizer.tokenize("""unwanted running"""		)							,				["""un""", """##want""", """##ed""", """runn""", """##ing"""]		)

									self.assertListEqual(tokenizer.tokenize("""unwantedX running"""		)							,				["""[UNK]""", """runn""", """##ing"""]		)


							def      _snake_case  (							self    :Any		)   ->   str:



									"""simple docstring"""



									self.assertTrue(_is_whitespace(""" """		)		)
									self.assertTrue(_is_whitespace("""\t"""		)		)
									self.assertTrue(_is_whitespace("""\r"""		)		)
									self.assertTrue(_is_whitespace("""\n"""		)		)
									self.assertTrue(_is_whitespace("""\u00A0"""		)		)

									self.assertFalse(_is_whitespace("""A"""		)		)
									self.assertFalse(_is_whitespace("""-"""		)		)


							def      _snake_case  (							self    :int		)   ->   str:



									"""simple docstring"""



									self.assertTrue(_is_control("""\u0005"""		)		)

									self.assertFalse(_is_control("""A"""		)		)
									self.assertFalse(_is_control(""" """		)		)
									self.assertFalse(_is_control("""\t"""		)		)
									self.assertFalse(_is_control("""\r"""		)		)


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



									"""simple docstring"""



									self.assertTrue(_is_punctuation("""-"""		)		)
									self.assertTrue(_is_punctuation("""$"""		)		)
									self.assertTrue(_is_punctuation("""`"""		)		)
									self.assertTrue(_is_punctuation("""."""		)		)

									self.assertFalse(_is_punctuation("""A"""		)		)
									self.assertFalse(_is_punctuation(""" """		)		)


							def      _snake_case  (							self    :str		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(__A		) for t in ["""Test""", """\xad""", """test"""]]							,				[["""[UNK]"""], [], ["""[UNK]"""]]		)

									if self.test_rust_tokenizer:
											SCREAMING_SNAKE_CASE__			=      self.get_rust_tokenizer()
											self.assertListEqual(
											    [rust_tokenizer.tokenize(__A		) for t in ["""Test""", """\xad""", """test"""]]							,				[["""[UNK]"""], [], ["""[UNK]"""]]		)


							def      _snake_case  (							self    :int		)   ->   Any:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)

													SCREAMING_SNAKE_CASE__			=      f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
													SCREAMING_SNAKE_CASE__			=      tokenizer_r.encode_plus(
													    __A							,				return_attention_mask=__A							,				return_token_type_ids=__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A							,				)

													SCREAMING_SNAKE_CASE__			=      tokenizer_r.do_lower_case if hasattr(__A							,				"""do_lower_case"""		) else False
													SCREAMING_SNAKE_CASE__			=      (
													    [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), """A"""),
													        ((1, 2), ""","""),
													        ((3, 5), """na"""),
													        ((5, 6), """##ï"""),
													        ((6, 8), """##ve"""),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), """Allen"""),
													        ((21, 23), """##NL"""),
													        ((23, 24), """##P"""),
													        ((25, 33), """sentence"""),
													        ((33, 34), """."""),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													    if not do_lower_case
													    else [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), """a"""),
													        ((1, 2), ""","""),
													        ((3, 8), """naive"""),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), """allen"""),
													        ((21, 23), """##nl"""),
													        ((23, 24), """##p"""),
													        ((25, 33), """sentence"""),
													        ((33, 34), """."""),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													)

													self.assertEqual(
													    [e[1] for e in expected_results]							,				tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]		)		)
													self.assertEqual([e[0] for e in expected_results]							,				tokens["""offset_mapping"""]		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ["""的""", """人""", """有"""]
									SCREAMING_SNAKE_CASE__			=      """""".join(__A		)
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__			=      True
													SCREAMING_SNAKE_CASE__			=      self.tokenizer_class.from_pretrained(__A							,				**__A		)
													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)

													SCREAMING_SNAKE_CASE__			=      tokenizer_p.encode(__A							,				add_special_tokens=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r.encode(__A							,				add_special_tokens=__A		)

													SCREAMING_SNAKE_CASE__			=      tokenizer_r.convert_ids_to_tokens(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_p.convert_ids_to_tokens(__A		)

													# it is expected that each Chinese character is not preceded by "##"
													self.assertListEqual(__A							,				__A		)
													self.assertListEqual(__A							,				__A		)

													SCREAMING_SNAKE_CASE__			=      False
													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(__A							,				**__A		)
													SCREAMING_SNAKE_CASE__			=      self.tokenizer_class.from_pretrained(__A							,				**__A		)

													SCREAMING_SNAKE_CASE__			=      tokenizer_r.encode(__A							,				add_special_tokens=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_p.encode(__A							,				add_special_tokens=__A		)

													SCREAMING_SNAKE_CASE__			=      tokenizer_r.convert_ids_to_tokens(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_p.convert_ids_to_tokens(__A		)

													# it is expected that only the first Chinese character is not preceded by "##".
													SCREAMING_SNAKE_CASE__			=      [
													    f'''##{token}''' if idx != 0 else token for idx, token in enumerate(__A		)
													]
													self.assertListEqual(__A							,				__A		)
													self.assertListEqual(__A							,				__A		)


							@slow
							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer_class(self.vocab_file							,				self.word_shape_file							,				self.word_pronunciation_file		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""你好"""							,				add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""你是谁"""							,				add_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A							,				__A		)

									assert encoded_sentence == [1] + text + [2]
									assert encoded_pair == [1] + text + [2] + text_a + [2]


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizers(do_lower_case=__A		)
									for tokenizer in tokenizers:
											with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
													SCREAMING_SNAKE_CASE__			=      """你好，你是谁"""
													SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_ids(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_shape_ids(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_pronunciation_ids(__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer.prepare_for_model(
													    __A							,				__A							,				__A							,				add_special_tokens=__A		)

													SCREAMING_SNAKE_CASE__			=      tokenizer.encode_plus(__A							,				add_special_tokens=__A		)

													self.assertEqual(__A							,				__A		)