



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin







class 							_a							:

			def __init__(self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_="resnet50",					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=32,					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=True,					)    ->					str:
						UpperCAmelCase_:						Tuple					=			parent
						UpperCAmelCase_:						Any					=			out_indices if out_indices is not None else [4]
						UpperCAmelCase_:						List[str]					=			stage_names
						UpperCAmelCase_:						Optional[Any]					=			out_features
						UpperCAmelCase_:						Optional[int]					=			backbone
						UpperCAmelCase_:						str					=			batch_size
						UpperCAmelCase_:						Union[str, Any]					=			image_size
						UpperCAmelCase_:						Optional[Any]					=			num_channels
						UpperCAmelCase_:						List[str]					=			use_pretrained_backbone
						UpperCAmelCase_:						Optional[int]					=			is_training

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						Tuple					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
						UpperCAmelCase_:						Optional[Any]					=			self.get_config()

						return config, pixel_values

			def 			__snake_case    (self )    ->					Any:
						return TimmBackboneConfig(
						    image_size=self.image_size,					num_channels=self.num_channels,					out_features=self.out_features,					out_indices=self.out_indices,					stage_names=self.stage_names,					use_pretrained_backbone=self.use_pretrained_backbone,					backbone=self.backbone,					)

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					List[str]:
						UpperCAmelCase_:						Dict					=			TimmBackbone(config=A__ )
						model.to(A__ )
						model.eval()
						with torch.no_grad():
									UpperCAmelCase_:						Any					=			model(A__ )
						self.parent.assertEqual(
						    result.feature_map[-1].shape,					(self.batch_size, model.channels[-1], 14, 14),					)

			def 			__snake_case    (self )    ->					Dict:
						UpperCAmelCase_:						Tuple					=			self.prepare_config_and_inputs()
						UpperCAmelCase_	,				UpperCAmelCase_:						Tuple					=			config_and_inputs
						UpperCAmelCase_:						List[str]					=			{"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
@require_timm
class 							_a							(					SCREAMING_SNAKE_CASE__      ,						SCREAMING_SNAKE_CASE__      ,						SCREAMING_SNAKE_CASE__      ,						unittest.TestCase ):
			A							=     (TimmBackbone,) if is_torch_available() else ()
			A							=     {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
			A							=     False
			A							=     False
			A							=     False
			A							=     False

			def 			__snake_case    (self )    ->					Union[str, Any]:
						UpperCAmelCase_:						int					=			TimmBackboneModelTester(self )
						UpperCAmelCase_:						Union[str, Any]					=			ConfigTester(self,					config_class=A__,					has_text_modality=A__ )

			def 			__snake_case    (self )    ->					Tuple:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()

			def 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						Optional[Any]					=			"""resnet18"""
						UpperCAmelCase_:						List[str]					=			"""microsoft/resnet-18"""

						UpperCAmelCase_:						Optional[int]					=			AutoBackbone.from_pretrained(A__,					use_timm_backbone=A__ )
						UpperCAmelCase_:						List[str]					=			AutoBackbone.from_pretrained(A__ )

						self.assertEqual(len(timm_model.out_features ),					len(transformers_model.out_features ) )
						self.assertEqual(len(timm_model.stage_names ),					len(transformers_model.stage_names ) )
						self.assertEqual(timm_model.channels,					transformers_model.channels )
						# Out indices are set to the last layer by default. For timm models, we don't know
						# the number of layers in advance, so we set it to (-1,), whereas for transformers
						# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
						self.assertEqual(timm_model.out_indices,					(-1,) )
						self.assertEqual(transformers_model.out_indices,					[len(timm_model.stage_names ) - 1] )

						UpperCAmelCase_:						Optional[Any]					=			AutoBackbone.from_pretrained(A__,					use_timm_backbone=A__,					out_indices=[1, 2, 3] )
						UpperCAmelCase_:						Tuple					=			AutoBackbone.from_pretrained(A__,					out_indices=[1, 2, 3] )

						self.assertEqual(timm_model.out_indices,					transformers_model.out_indices )
						self.assertEqual(len(timm_model.out_features ),					len(transformers_model.out_features ) )
						self.assertEqual(timm_model.channels,					transformers_model.channels )

			@unittest.skip("""TimmBackbone doesn\'t support feed forward chunking""" )
			def 			__snake_case    (self )    ->					Optional[Any]:
						pass

			@unittest.skip("""TimmBackbone doesn\'t have num_hidden_layers attribute""" )
			def 			__snake_case    (self )    ->					List[str]:
						pass

			@unittest.skip("""TimmBackbone initialization is managed on the timm side""" )
			def 			__snake_case    (self )    ->					int:
						pass

			@unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds""" )
			def 			__snake_case    (self )    ->					int:
						pass

			@unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds""" )
			def 			__snake_case    (self )    ->					Optional[int]:
						pass

			@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""" )
			def 			__snake_case    (self )    ->					Any:
						pass

			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
			def 			__snake_case    (self )    ->					Tuple:
						pass

			@unittest.skip("""model weights aren\'t tied in TimmBackbone.""" )
			def 			__snake_case    (self )    ->					int:
						pass

			@unittest.skip("""model weights aren\'t tied in TimmBackbone.""" )
			def 			__snake_case    (self )    ->					List[str]:
						pass

			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
			def 			__snake_case    (self )    ->					str:
						pass

			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
			def 			__snake_case    (self )    ->					Optional[int]:
						pass

			@unittest.skip("""TimmBackbone doesn\'t have hidden size info in its configuration.""" )
			def 			__snake_case    (self )    ->					List[str]:
						pass

			@unittest.skip("""TimmBackbone doesn\'t support output_attentions.""" )
			def 			__snake_case    (self )    ->					Optional[int]:
						pass

			@unittest.skip("""Safetensors is not supported by timm.""" )
			def 			__snake_case    (self )    ->					Dict:
						pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
			def 			__snake_case    (self )    ->					Optional[int]:
						pass

			def 			__snake_case    (self )    ->					Dict:
						UpperCAmelCase_	,				UpperCAmelCase_:						int					=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:						List[str]					=			model_class(A__ )
									UpperCAmelCase_:						Optional[int]					=			inspect.signature(model.forward )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_:						int					=			[*signature.parameters.keys()]

									UpperCAmelCase_:						List[Any]					=			["""pixel_values"""]
									self.assertListEqual(arg_names[:1],					A__ )

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_	,				UpperCAmelCase_:						str					=			self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_:						Dict					=			True
						UpperCAmelCase_:						str					=			self.has_attentions

						# no need to test all models as different heads yield the same functionality
						UpperCAmelCase_:						Dict					=			self.all_model_classes[0]
						UpperCAmelCase_:						List[str]					=			model_class(A__ )
						model.to(A__ )

						UpperCAmelCase_:						Tuple					=			self._prepare_for_class(A__,					A__ )
						UpperCAmelCase_:						Tuple					=			model(**A__ )
						UpperCAmelCase_:						Dict					=			outputs[0][-1]

						# Encoder-/Decoder-only models
						UpperCAmelCase_:						Optional[Any]					=			outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
									UpperCAmelCase_:						Any					=			outputs.attentions[0]
									attentions.retain_grad()

						output.flatten()[0].backward(retain_graph=A__ )

						self.assertIsNotNone(hidden_states.grad )

						if self.has_attentions:
									self.assertIsNotNone(attentions.grad )

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_	,				UpperCAmelCase_:						Union[str, Any]					=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:						Union[str, Any]					=			model_class(A__ )
									model.to(A__ )
									model.eval()
									UpperCAmelCase_:						Union[str, Any]					=			model(**A__ )

									self.assertEqual(len(result.feature_maps ),					len(config.out_indices ) )
									self.assertEqual(len(model.channels ),					len(config.out_indices ) )

									# Check output of last stage is taken if out_features=None, out_indices=None
									UpperCAmelCase_:						Tuple					=			copy.deepcopy(A__ )
									UpperCAmelCase_:						Any					=			None
									UpperCAmelCase_:						Union[str, Any]					=			model_class(A__ )
									model.to(A__ )
									model.eval()
									UpperCAmelCase_:						Any					=			model(**A__ )

									self.assertEqual(len(result.feature_maps ),					1 )
									self.assertEqual(len(model.channels ),					1 )

									# Check backbone can be initialized with fresh weights
									UpperCAmelCase_:						Dict					=			copy.deepcopy(A__ )
									UpperCAmelCase_:						Dict					=			False
									UpperCAmelCase_:						Tuple					=			model_class(A__ )
									model.to(A__ )
									model.eval()
									UpperCAmelCase_:						str					=			model(**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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Union[str, Any]       					=RoCBertTokenizer
							lowercase_       :       str       					=None
							lowercase_       :       Optional[Any]       					=False
							lowercase_       :       Any       					=True
							lowercase_       :       int       					=filter_non_english




							def  A__      (				self):
												super().setUp()

												lowercase							=						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''你''', '''好''', '''是''', '''谁''', '''a''', '''b''', '''c''', '''d''']
												lowercase							=						{}
												lowercase							=						{}
												for i, value in enumerate(A__):
																	lowercase							=						i
																	lowercase							=						i
												lowercase							=						os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase							=						os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''word_shape_file'''])
												lowercase							=						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  A__      (				self):
												lowercase							=						self.tokenizer_class(self.vocab_file   ,self.word_shape_file   ,self.word_pronunciation_file)

												lowercase							=						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  A__      (				self):
												lowercase							=						RoCBertBasicTokenizer()

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




							def  A__      (				self):
												lowercase							=						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  A__      (				self):
												lowercase							=						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  A__      (				self):
												lowercase							=						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  A__      (				self):
												lowercase							=						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  A__      (				self):
												lowercase							=						RoCBertBasicTokenizer(do_lower_case=A__)

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




							def  A__      (				self):
												lowercase							=						RoCBertBasicTokenizer(do_lower_case=A__   ,strip_accents=A__)

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




							def  A__      (				self):
												lowercase							=						RoCBertBasicTokenizer(do_lower_case=A__   ,strip_accents=A__)

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




							def  A__      (				self):
												lowercase							=						RoCBertBasicTokenizer(do_lower_case=A__   ,never_split=['''[UNK]'''])

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




							def  A__      (				self):
												lowercase							=						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

												lowercase							=						{}
												for i, token in enumerate(A__):
																	lowercase							=						i
												lowercase							=						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  A__      (				self):
												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  A__      (				self):
												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  A__      (				self):
												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  A__      (				self):
												lowercase							=						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:
																	lowercase							=						self.get_rust_tokenizer()
																	self.assertListEqual(
																	    [rust_tokenizer.tokenize(A__) for t in ['''Test''', '''\xad''', '''test''']]   ,[['''[UNK]'''], [], ['''[UNK]''']])




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

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

																						lowercase							=						tokenizer_r.do_lower_case if hasattr(A__   ,'''do_lower_case''') else False
																						lowercase							=						(
																						    [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), '''A'''),
																						        ((1, 2), ''','''),
																						        ((3, 5), '''na'''),
																						        ((5, 6), '''##ï'''),
																						        ((6, 8), '''##ve'''),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), '''Allen'''),
																						        ((2_1, 2_3), '''##NL'''),
																						        ((2_3, 2_4), '''##P'''),
																						        ((2_5, 3_3), '''sentence'''),
																						        ((3_3, 3_4), '''.'''),
																						        ((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, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), '''allen'''),
																						        ((2_1, 2_3), '''##nl'''),
																						        ((2_3, 2_4), '''##p'''),
																						        ((2_5, 3_3), '''sentence'''),
																						        ((3_3, 3_4), '''.'''),
																						        ((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  A__      (				self):
												lowercase							=						['''的''', '''人''', '''有''']
												lowercase							=						''''''.join(A__)
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
																						lowercase							=						True
																						lowercase							=						self.tokenizer_class.from_pretrained(A__   ,**A__)
																						lowercase							=						self.rust_tokenizer_class.from_pretrained(A__   ,**A__)

																						lowercase							=						tokenizer_p.encode(A__   ,add_special_tokens=A__)
																						lowercase							=						tokenizer_r.encode(A__   ,add_special_tokens=A__)

																						lowercase							=						tokenizer_r.convert_ids_to_tokens(A__)
																						lowercase							=						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__)

																						lowercase							=						False
																						lowercase							=						self.rust_tokenizer_class.from_pretrained(A__   ,**A__)
																						lowercase							=						self.tokenizer_class.from_pretrained(A__   ,**A__)

																						lowercase							=						tokenizer_r.encode(A__   ,add_special_tokens=A__)
																						lowercase							=						tokenizer_p.encode(A__   ,add_special_tokens=A__)

																						lowercase							=						tokenizer_r.convert_ids_to_tokens(A__)
																						lowercase							=						tokenizer_p.convert_ids_to_tokens(A__)

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




							@slow
							def  A__      (				self):
												lowercase							=						self.tokenizer_class(self.vocab_file   ,self.word_shape_file   ,self.word_pronunciation_file)

												lowercase							=						tokenizer.encode('''你好'''   ,add_special_tokens=A__)
												lowercase							=						tokenizer.encode('''你是谁'''   ,add_special_tokens=A__)

												lowercase							=						tokenizer.build_inputs_with_special_tokens(A__)
												lowercase							=						tokenizer.build_inputs_with_special_tokens(A__   ,A__)

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





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

																						lowercase							=						tokenizer.encode_plus(A__   ,add_special_tokens=A__)

																						self.assertEqual(A__   ,A__)




