



"""simple docstring"""






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class     A__     :




	'''simple docstring'''





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

				"""simple docstring"""

				return self.get_dummy_input()

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

				"""simple docstring"""

				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

				if self.block_type == "mid":
							init_dict.pop("out_channels")

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

				self.assertEqual(output.shape ,    self.output_shape)

				__lowerCAmelCase   :						str				= output[0, -1, -3:, -3:]
				__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				assert torch_all_close(output_slice.flatten() ,    _SCREAMING_SNAKE_CASE ,    atol=5e-3)







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()




"""simple docstring"""






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     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			RoCBertTokenizer
	SCREAMING_SNAKE_CASE					   =			None
	SCREAMING_SNAKE_CASE					   =			False
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			filter_non_english

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Optional[Any]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase   :						Dict				= ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
				__lowerCAmelCase   :						str				= {}
				__lowerCAmelCase   :						List[Any]				= {}
				for i, value in enumerate(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						int				= i
							__lowerCAmelCase   :						List[str]				= i
				__lowerCAmelCase   :						int				= os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["vocab_file"])
				__lowerCAmelCase   :						Union[str, Any]				= os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["word_shape_file"])
				__lowerCAmelCase   :						Any				= 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(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    ensure_ascii=_SCREAMING_SNAKE_CASE)
				with open(self.word_pronunciation_file ,    "w" ,    encoding="utf-8") as word_pronunciation_writer:
							json.dump(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    ensure_ascii=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.tokenizer_class(self.vocab_file ,    self.word_shape_file ,    self.word_pronunciation_file)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= RoCBertBasicTokenizer()

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE ,    strip_accents=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE ,    strip_accents=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE ,    strip_accents=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE ,    strip_accents=_SCREAMING_SNAKE_CASE)

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= RoCBertBasicTokenizer(do_lower_case=_SCREAMING_SNAKE_CASE ,    never_split=["[UNK]"])

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						str				= ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				__lowerCAmelCase   :						Union[str, Any]				= {}
				for i, token in enumerate(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Optional[int]				= i
				__lowerCAmelCase   :						Any				= RoCBertWordpieceTokenizer(vocab=_SCREAMING_SNAKE_CASE ,    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 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Dict:

				"""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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> 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 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.get_tokenizer()

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

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

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

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase   :						List[Any]				= self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						Optional[Any]				= F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										__lowerCAmelCase   :						List[Any]				= tokenizer_r.encode_plus(
										    _SCREAMING_SNAKE_CASE ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    return_token_type_ids=_SCREAMING_SNAKE_CASE ,    return_offsets_mapping=_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE ,    )

										__lowerCAmelCase   :						Optional[Any]				= tokenizer_r.do_lower_case if hasattr(_SCREAMING_SNAKE_CASE ,    "do_lower_case") else False
										__lowerCAmelCase   :						int				= (
										    [
										        ((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 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= ["的", "人", "有"]
				__lowerCAmelCase   :						Tuple				= "".join(_SCREAMING_SNAKE_CASE)
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase   :						Any				= True
										__lowerCAmelCase   :						Dict				= self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						int				= self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						Dict				= tokenizer_p.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[Any]				= tokenizer_r.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						Optional[Any]				= tokenizer_r.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						Optional[int]				= tokenizer_p.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)

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

										__lowerCAmelCase   :						int				= False
										__lowerCAmelCase   :						List[Any]				= self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[Any]				= self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						Any				= tokenizer_r.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[Any]				= tokenizer_p.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						int				= tokenizer_r.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						Dict				= tokenizer_p.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)

										# it is expected that only the first Chinese character is not preceded by "##".
										__lowerCAmelCase   :						Union[str, Any]				= [
										    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_SCREAMING_SNAKE_CASE)
										]
										self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
										self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.tokenizer_class(self.vocab_file ,    self.word_shape_file ,    self.word_pronunciation_file)

				__lowerCAmelCase   :						List[Any]				= tokenizer.encode("你好" ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.encode("你是谁" ,    add_special_tokens=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										__lowerCAmelCase   :						Dict				= "你好，你是谁"
										__lowerCAmelCase   :						Tuple				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						Optional[Any]				= tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[str]				= tokenizer.convert_tokens_to_shape_ids(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						str				= tokenizer.convert_tokens_to_pronunciation_ids(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						int				= tokenizer.prepare_for_model(
										    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)

										__lowerCAmelCase   :						Union[str, Any]				= tokenizer.encode_plus(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)

										self.assertEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)