



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.layoutlm.modeling_tf_layoutlm import (
							    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFLayoutLMForMaskedLM,
							    TFLayoutLMForQuestionAnswering,
							    TFLayoutLMForSequenceClassification,
							    TFLayoutLMForTokenClassification,
							    TFLayoutLMModel,
							)







class       __lowerCAmelCase   :



			def __init__(						self       :  Dict			,		A       :  List[str]			,		A       :  List[str]=13			,		A       :  List[Any]=7			,		A       :  Optional[int]=True			,		A       :  Optional[int]=True			,		A       :  List[str]=True			,		A       :  Dict=True			,		A       :  str=99			,		A       :  Optional[Any]=32			,		A       :  int=2			,		A       :  int=4			,		A       :  Union[str, Any]=37			,		A       :  List[Any]="gelu"			,		A       :  Optional[int]=0.1			,		A       :  Union[str, Any]=0.1			,		A       :  Tuple=5_12			,		A       :  Union[str, Any]=16			,		A       :  Optional[Any]=2			,		A       :  Any=0.0_2			,		A       :  List[Any]=3			,		A       :  Dict=4			,		A       :  Optional[int]=None			,		A       :  Any=10_00			,		)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       num_choices
						_UpperCAmelCase			=       scope
						_UpperCAmelCase			=       range_bbox



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

						# convert bbox to numpy since TF does not support item assignment
						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length, 4]			,		self.range_bbox).numpy()
						# Ensure that bbox is legal
						for i in range(bbox.shape[0]):
									for j in range(bbox.shape[1]):
												if bbox[i, j, 3] < bbox[i, j, 1]:
															_UpperCAmelCase			=       bbox[i, j, 3]
															_UpperCAmelCase			=       bbox[i, j, 1]
															_UpperCAmelCase			=       t
												if bbox[i, j, 2] < bbox[i, j, 0]:
															_UpperCAmelCase			=       bbox[i, j, 2]
															_UpperCAmelCase			=       bbox[i, j, 0]
															_UpperCAmelCase			=       t
						_UpperCAmelCase			=       tf.convert_to_tensor(A)

						_UpperCAmelCase			=       None
						if self.use_input_mask:
									_UpperCAmelCase			=       random_attention_mask([self.batch_size, self.seq_length])

						_UpperCAmelCase			=       None
						if self.use_token_type_ids:
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size)

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.type_sequence_label_size)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels)
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.num_choices)

						_UpperCAmelCase			=       LayoutLMConfig(
						    vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		initializer_range=self.initializer_range			,		)

						return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  List[str]			,		A       :  Any			,		A       :  Dict			,		A       :  List[Any]			,		A       :  Tuple			,		A       :  int			,		A       :  Tuple			,		A       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMModel(config=A)

						_UpperCAmelCase			=       model(A			,		A			,		attention_mask=A			,		token_type_ids=A)
						_UpperCAmelCase			=       model(A			,		A			,		token_type_ids=A)
						_UpperCAmelCase			=       model(A			,		A)
						self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size))
						self.parent.assertEqual(result.pooler_output.shape			,		(self.batch_size, self.hidden_size))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Dict			,		A       :  Optional[int]			,		A       :  Tuple			,		A       :  Optional[Any]			,		A       :  List[str]			,		A       :  int			,		A       :  Dict			,		A       :  Tuple)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMForMaskedLM(config=A)

						_UpperCAmelCase			=       model(A			,		A			,		attention_mask=A			,		token_type_ids=A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Optional[int]			,		A       :  int			,		A       :  List[Any]			,		A       :  Optional[int]			,		A       :  str			,		A       :  Optional[Any]			,		A       :  Optional[int]			,		A       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFLayoutLMForSequenceClassification(config=A)

						_UpperCAmelCase			=       model(A			,		A			,		attention_mask=A			,		token_type_ids=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFLayoutLMForTokenClassification(config=A)

						_UpperCAmelCase			=       model(A			,		A			,		attention_mask=A			,		token_type_ids=A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels))



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Union[str, Any]			,		A       :  Tuple			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Any			,		A       :  Optional[int]			,		A       :  List[str]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMForQuestionAnswering(config=A)

						_UpperCAmelCase			=       model(A			,		A			,		attention_mask=A			,		token_type_ids=A)
						self.parent.assertEqual(result.start_logits.shape			,		(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape			,		(self.batch_size, self.seq_length))



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						(
						    (
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					
						)			=       config_and_inputs
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'bbox': bbox,
						    'token_type_ids': token_type_ids,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict







@require_tf
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(
			    (
			        TFLayoutLMModel,
			        TFLayoutLMForMaskedLM,
			        TFLayoutLMForTokenClassification,
			        TFLayoutLMForSequenceClassification,
			        TFLayoutLMForQuestionAnswering,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': TFLayoutLMModel,
			        '''fill-mask''': TFLayoutLMForMaskedLM,
			        '''text-classification''': TFLayoutLMForSequenceClassification,
			        '''token-classification''': TFLayoutLMForTokenClassification,
			        '''zero-shot''': TFLayoutLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase        =					False
			UpperCamelCase        =					True
			UpperCamelCase        =					1_0



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		hidden_size=37)



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



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Dict:



						"""simple docstring"""






						for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase			=       TFLayoutLMModel.from_pretrained(A)
									self.assertIsNotNone(A)



			@unittest.skip('Onnx compliancy broke with TF 2.10')
			def    _lowerCamelCase	(						self       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						pass





def 							A     (					)    ->			List[Any]:







			'''simple docstring'''
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			_UpperCAmelCase			=       tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]   )  # noqa: E231
			_UpperCAmelCase			=       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]   )  # noqa: E231
			_UpperCAmelCase			=       tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]   )  # noqa: E231
			_UpperCAmelCase			=       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]   )  # noqa: E231
			# these are sequence labels (i.e. at the token level)
			_UpperCAmelCase			=       tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]   )  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class       __lowerCAmelCase   (      unittest.TestCase						):



			@slow
			def    _lowerCamelCase	(						self       :  str)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')

						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       prepare_layoutlm_batch_inputs()

						# forward pass
						_UpperCAmelCase			=       model(input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A)

						# test the sequence output on [0, :3, :3]
						_UpperCAmelCase			=       tf.convert_to_tensor(
						    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]			,		)

						self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			,		A			,		atol=1E-3))

						# test the pooled output on [1, :3]
						_UpperCAmelCase			=       tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2])

						self.assertTrue(np.allclose(outputs.pooler_output[1, :3]			,		A			,		atol=1E-3))



			@slow
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,		num_labels=2)

						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       prepare_layoutlm_batch_inputs()

						# forward pass
						_UpperCAmelCase			=       model(
						    input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A			,		labels=tf.convert_to_tensor([1, 1])			,		)

						# test whether we get a loss as a scalar
						_UpperCAmelCase			=       outputs.loss
						_UpperCAmelCase			=       (2,)
						self.assertEqual(loss.shape			,		A)

						# test the shape of the logits
						_UpperCAmelCase			=       outputs.logits
						_UpperCAmelCase			=       (2, 2)
						self.assertEqual(logits.shape			,		A)



			@slow
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,		num_labels=13)

						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       prepare_layoutlm_batch_inputs()

						# forward pass
						_UpperCAmelCase			=       model(
						    input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A			,		labels=A)

						# test the shape of the logits
						_UpperCAmelCase			=       outputs.logits
						_UpperCAmelCase			=       tf.convert_to_tensor((2, 25, 13))
						self.assertEqual(logits.shape			,		A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased')

						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       prepare_layoutlm_batch_inputs()

						# forward pass
						_UpperCAmelCase			=       model(input_ids=A			,		bbox=A			,		attention_mask=A			,		token_type_ids=A)

						# test the shape of the logits
						_UpperCAmelCase			=       tf.convert_to_tensor((2, 25))
						self.assertEqual(outputs.start_logits.shape			,		A)
						self.assertEqual(outputs.end_logits.shape			,		A)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__       =	{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					RoFormerTokenizer



			def __init__(						self       :  Dict			,		A       :  Any=None			,		A       :  Optional[Any]=None			,		A       :  Union[str, Any]=True			,		A       :  List[str]="[UNK]"			,		A       :  List[str]="[SEP]"			,		A       :  Union[str, Any]="[PAD]"			,		A       :  Any="[CLS]"			,		A       :  str="[MASK]"			,		A       :  Optional[int]=True			,		A       :  str=None			,		**A       :  Dict			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						super().__init__(
						    A			,		tokenizer_file=A			,		do_lower_case=A			,		unk_token=A			,		sep_token=A			,		pad_token=A			,		cls_token=A			,		mask_token=A			,		tokenize_chinese_chars=A			,		strip_accents=A			,		**A			,		)

						_UpperCAmelCase			=       json.loads(self.backend_tokenizer.normalizer.__getstate__())
						if (
						    pre_tok_state.get('lowercase'			,		A) != do_lower_case
						    or pre_tok_state.get('strip_accents'			,		A) != strip_accents
						):
									_UpperCAmelCase			=       getattr(A			,		pre_tok_state.pop('type'))
									_UpperCAmelCase			=       do_lower_case
									_UpperCAmelCase			=       strip_accents
									_UpperCAmelCase			=       pre_tok_class(**A)

						_UpperCAmelCase			=       do_lower_case



			def __getstate__(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.__dict__.copy()
						_UpperCAmelCase			=       BertPreTokenizer()
						return state



			def __setstate__(						self       :  List[Any]			,		A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       d
						_UpperCAmelCase			=       self.__dict__['_tokenizer'].get_vocab()
						_UpperCAmelCase			=       PreTokenizer.custom(JiebaPreTokenizer(A))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Optional[int]			,		A       :  List[Any]=None)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  List[int]			,		A       :  Optional[List[int]] = None)       ->   List[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       [self.sep_token_id]
						_UpperCAmelCase			=       [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) * [0] + len(token_ids_a + sep) * [1]



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str			,		A       :  Optional[str] = None)       ->   Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self._tokenizer.model.save(A			,		name=A)
						return tuple(A)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  str=None			,		A       :  Union[str, Any]=None			,		A       :  Union[str, Any]=False			,		**A       :  Tuple			,		)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       BertPreTokenizer()
						return super().save_pretrained(A			,		A			,		A			,		A			,		**A)
