






import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    LayoutLMForMaskedLM,
							    LayoutLMForQuestionAnswering,
							    LayoutLMForSequenceClassification,
							    LayoutLMForTokenClassification,
							    LayoutLMModel,
							)
class    a   :



						"""simple docstring"""


						def __init__(				self :      Optional[int]		,				__lowercase :      List[str]		,				__lowercase :      Union[str, Any]=13		,				__lowercase :      Union[str, Any]=7		,				__lowercase :      Tuple=True		,				__lowercase :      Dict=True		,				__lowercase :      Optional[int]=True		,				__lowercase :      List[Any]=True		,				__lowercase :      Optional[int]=99		,				__lowercase :      List[str]=32		,				__lowercase :      List[Any]=5		,				__lowercase :      Dict=4		,				__lowercase :      Union[str, Any]=37		,				__lowercase :      Dict="gelu"		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      Dict=0.1		,				__lowercase :      str=512		,				__lowercase :      Tuple=16		,				__lowercase :      str=2		,				__lowercase :      str=0.02		,				__lowercase :      Tuple=3		,				__lowercase :      Optional[Any]=4		,				__lowercase :      str=None		,				__lowercase :      Tuple=1000		,				)							->				Optional[int]:
								__UpperCAmelCase							:     Union[str, Any]									=  parent
								__UpperCAmelCase							:     Tuple									=  batch_size
								__UpperCAmelCase							:     List[str]									=  seq_length
								__UpperCAmelCase							:     Union[str, Any]									=  is_training
								__UpperCAmelCase							:     Optional[Any]									=  use_input_mask
								__UpperCAmelCase							:     List[str]									=  use_token_type_ids
								__UpperCAmelCase							:     int									=  use_labels
								__UpperCAmelCase							:     Dict									=  vocab_size
								__UpperCAmelCase							:     List[str]									=  hidden_size
								__UpperCAmelCase							:     str									=  num_hidden_layers
								__UpperCAmelCase							:     str									=  num_attention_heads
								__UpperCAmelCase							:     Dict									=  intermediate_size
								__UpperCAmelCase							:     Union[str, Any]									=  hidden_act
								__UpperCAmelCase							:     Any									=  hidden_dropout_prob
								__UpperCAmelCase							:     Union[str, Any]									=  attention_probs_dropout_prob
								__UpperCAmelCase							:     Optional[int]									=  max_position_embeddings
								__UpperCAmelCase							:     Any									=  type_vocab_size
								__UpperCAmelCase							:     Tuple									=  type_sequence_label_size
								__UpperCAmelCase							:     Optional[Any]									=  initializer_range
								__UpperCAmelCase							:     Union[str, Any]									=  num_labels
								__UpperCAmelCase							:     Tuple									=  num_choices
								__UpperCAmelCase							:     int									=  scope
								__UpperCAmelCase							:     int									=  range_bbox
						def      UpperCAmelCase				(				self :      str )							->				Any:
								__UpperCAmelCase							:     Any									=  ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size )

								__UpperCAmelCase							:     str									=  ids_tensor([self.batch_size, self.seq_length, 4]		,				self.range_bbox )
								# 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							:     Optional[Any]									=  bbox[i, j, 3]
														__UpperCAmelCase							:     Optional[Any]									=  bbox[i, j, 1]
														__UpperCAmelCase							:     Tuple									=  t
												if bbox[i, j, 2] < bbox[i, j, 0]:
														__UpperCAmelCase							:     Any									=  bbox[i, j, 2]
														__UpperCAmelCase							:     str									=  bbox[i, j, 0]
														__UpperCAmelCase							:     Tuple									=  t

								__UpperCAmelCase							:     Union[str, Any]									=  None
								if self.use_input_mask:
										__UpperCAmelCase							:     Optional[int]									=  ids_tensor([self.batch_size, self.seq_length]		,				vocab_size=2 )

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

								__UpperCAmelCase							:     int									=  None
								__UpperCAmelCase							:     str									=  None
								__UpperCAmelCase							:     Dict									=  None
								if self.use_labels:
										__UpperCAmelCase							:     Any									=  ids_tensor([self.batch_size]		,				self.type_sequence_label_size )
										__UpperCAmelCase							:     Any									=  ids_tensor([self.batch_size, self.seq_length]		,				self.num_labels )
										__UpperCAmelCase							:     List[str]									=  ids_tensor([self.batch_size]		,				self.num_choices )

								__UpperCAmelCase							:     str									=  self.get_config()

								return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def      UpperCAmelCase				(				self :      List[Any] )							->				int:
								return 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		,				)
						def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Union[str, Any]		,				__lowercase :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Dict		,				__lowercase :      Any		,				__lowercase :      Any		,				__lowercase :      Tuple		,				__lowercase :      Dict )							->				Union[str, Any]:
								__UpperCAmelCase							:     Any									=  LayoutLMModel(config=_A )
								model.to(_A )
								model.eval()
								__UpperCAmelCase							:     List[str]									=  model(_A		,				_A		,				attention_mask=_A		,				token_type_ids=_A )
								__UpperCAmelCase							:     Any									=  model(_A		,				_A		,				token_type_ids=_A )
								__UpperCAmelCase							:     Tuple									=  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      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      Optional[int]		,				__lowercase :      Tuple		,				__lowercase :      Dict		,				__lowercase :      List[str]		,				__lowercase :      Dict		,				__lowercase :      Union[str, Any]		,				__lowercase :      Dict		,				__lowercase :      Dict )							->				List[str]:
								__UpperCAmelCase							:     Any									=  LayoutLMForMaskedLM(config=_A )
								model.to(_A )
								model.eval()
								__UpperCAmelCase							:     Union[str, Any]									=  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      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      List[str]		,				__lowercase :      Optional[Any]		,				__lowercase :      Optional[int]		,				__lowercase :      int		,				__lowercase :      int		,				__lowercase :      int		,				__lowercase :      int		,				__lowercase :      Union[str, Any] )							->				Optional[Any]:
								__UpperCAmelCase							:     List[Any]									=  self.num_labels
								__UpperCAmelCase							:     Any									=  LayoutLMForSequenceClassification(_A )
								model.to(_A )
								model.eval()
								__UpperCAmelCase							:     Any									=  model(
								    _A		,				_A		,				attention_mask=_A		,				token_type_ids=_A		,				labels=_A )
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels) )
						def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      int		,				__lowercase :      List[Any]		,				__lowercase :      Any		,				__lowercase :      Any		,				__lowercase :      Optional[int]		,				__lowercase :      List[Any]		,				__lowercase :      Union[str, Any]		,				__lowercase :      Dict )							->				Any:
								__UpperCAmelCase							:     List[Any]									=  self.num_labels
								__UpperCAmelCase							:     int									=  LayoutLMForTokenClassification(config=_A )
								model.to(_A )
								model.eval()
								__UpperCAmelCase							:     int									=  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      UpperCAmelCase				(				self :      Any		,				__lowercase :      int		,				__lowercase :      Dict		,				__lowercase :      List[Any]		,				__lowercase :      List[Any]		,				__lowercase :      Any		,				__lowercase :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      List[str] )							->				Dict:
								__UpperCAmelCase							:     Union[str, Any]									=  LayoutLMForQuestionAnswering(config=_A )
								model.to(_A )
								model.eval()
								__UpperCAmelCase							:     Any									=  model(
								    _A		,				bbox=_A		,				attention_mask=_A		,				token_type_ids=_A		,				start_positions=_A		,				end_positions=_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      UpperCAmelCase				(				self :      int )							->				Dict:
								__UpperCAmelCase							:     Union[str, Any]									=  self.prepare_config_and_inputs()
								(
								    __UpperCAmelCase   
								)							:     Dict									=  config_and_inputs
								__UpperCAmelCase							:     Any									=  {
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict


@require_torch
class    a   (			A__							,	A__							,	unittest.TestCase       ):



						"""simple docstring"""


						a  :			List[Any]								=     (
						    (
						        LayoutLMModel,
						        LayoutLMForMaskedLM,
						        LayoutLMForSequenceClassification,
						        LayoutLMForTokenClassification,
						        LayoutLMForQuestionAnswering,
						    )
						    if is_torch_available()
						    else None
						)
						a  :			str								=     (
						    {
						        'document-question-answering': LayoutLMForQuestionAnswering,
						        'feature-extraction': LayoutLMModel,
						        'fill-mask': LayoutLMForMaskedLM,
						        'text-classification': LayoutLMForSequenceClassification,
						        'token-classification': LayoutLMForTokenClassification,
						        'zero-shot': LayoutLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						a  :			Any								=     True
						def      UpperCAmelCase				(				self :      List[Any] )							->				str:
								__UpperCAmelCase							:     List[str]									=  LayoutLMModelTester(self )
								__UpperCAmelCase							:     str									=  ConfigTester(self		,				config_class=_A		,				hidden_size=37 )
						def      UpperCAmelCase				(				self :      str )							->				Any:
								self.config_tester.run_common_tests()
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Tuple:
								__UpperCAmelCase							:     Dict									=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A )
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				List[Any]:
								__UpperCAmelCase							:     Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										__UpperCAmelCase							:     Optional[int]									=  type
										self.model_tester.create_and_check_model(*_A )
						def      UpperCAmelCase				(				self :      List[str] )							->				Union[str, Any]:
								__UpperCAmelCase							:     Dict									=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_A )
						def      UpperCAmelCase				(				self :      Dict )							->				str:
								__UpperCAmelCase							:     List[Any]									=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_A )
						def      UpperCAmelCase				(				self :      Union[str, Any] )							->				Union[str, Any]:
								__UpperCAmelCase							:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_A )
						def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
								__UpperCAmelCase							:     int									=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_A )




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






def     lowerCamelCase__				(   ):
		__UpperCAmelCase							:     Tuple									=  torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]  ,		device=__lowerCamelCase		)  # noqa: E231
		__UpperCAmelCase							:     Optional[int]									=  torch.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],]  ,		device=__lowerCamelCase		)  # noqa: E231
		__UpperCAmelCase							:     Dict									=  torch.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],[1000,1000,1000,1000]],[[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],[1000,1000,1000,1000]]]  ,		device=__lowerCamelCase		)  # noqa: E231
		__UpperCAmelCase							:     List[Any]									=  torch.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]]  ,		device=__lowerCamelCase		)  # noqa: E231
		# these are sequence labels (i.e. at the token level)
		__UpperCAmelCase							:     List[Any]									=  torch.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]]  ,		device=__lowerCamelCase		)  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class    a   (			unittest.TestCase       ):



						"""simple docstring"""


						@slow
						def      UpperCAmelCase				(				self :      Dict )							->				Tuple:
								__UpperCAmelCase							:     List[Any]									=  LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(_A )

								__UpperCAmelCase							:     Union[str, Any]									=  prepare_layoutlm_batch_inputs()

								# forward pass
								__UpperCAmelCase							:     Tuple									=  model(input_ids=_A		,				bbox=_A		,				attention_mask=_A		,				token_type_ids=_A )

								# test the sequence output on [0, :3, :3]
								__UpperCAmelCase							:     Optional[int]									=  torch.tensor(
								    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]		,				device=_A		,				)

								self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,				_A		,				atol=1e-3 ) )

								# test the pooled output on [1, :3]
								__UpperCAmelCase							:     List[Any]									=  torch.tensor([-0.6_580, -0.0_214, 0.8_552]		,				device=_A )

								self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]		,				_A		,				atol=1e-3 ) )
						@slow
						def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[str]:
								__UpperCAmelCase							:     str									=  LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""		,				num_labels=2 ).to(
								    _A )

								__UpperCAmelCase							:     Dict									=  prepare_layoutlm_batch_inputs()

								# forward pass
								__UpperCAmelCase							:     Tuple									=  model(
								    input_ids=_A		,				bbox=_A		,				attention_mask=_A		,				token_type_ids=_A		,				labels=torch.tensor([1, 1]		,				device=_A )		,				)

								# test whether we get a loss as a scalar
								__UpperCAmelCase							:     Dict									=  outputs.loss
								__UpperCAmelCase							:     str									=  torch.Size([] )
								self.assertEqual(loss.shape		,				_A )

								# test the shape of the logits
								__UpperCAmelCase							:     int									=  outputs.logits
								__UpperCAmelCase							:     Tuple									=  torch.Size((2, 2) )
								self.assertEqual(logits.shape		,				_A )
						@slow
						def      UpperCAmelCase				(				self :      Dict )							->				Optional[Any]:
								__UpperCAmelCase							:     Tuple									=  LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""		,				num_labels=13 ).to(
								    _A )

								__UpperCAmelCase							:     Tuple									=  prepare_layoutlm_batch_inputs()

								# forward pass
								__UpperCAmelCase							:     Any									=  model(
								    input_ids=_A		,				bbox=_A		,				attention_mask=_A		,				token_type_ids=_A		,				labels=_A )

								# test the loss calculation to be around 2.65
								# expected_loss = torch.tensor(2.65, device=torch_device)

								# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
								# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

								# test the shape of the logits
								__UpperCAmelCase							:     str									=  outputs.logits
								__UpperCAmelCase							:     int									=  torch.Size((2, 25, 13) )
								self.assertEqual(logits.shape		,				_A )




						@slow
						def      UpperCAmelCase				(				self :      Any )							->				str:
								__UpperCAmelCase							:     Optional[int]									=  LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(_A )

								__UpperCAmelCase							:     str									=  prepare_layoutlm_batch_inputs()

								# forward pass
								__UpperCAmelCase							:     Optional[int]									=  model(input_ids=_A		,				bbox=_A		,				attention_mask=_A		,				token_type_ids=_A )

								# test the shape of the logits
								__UpperCAmelCase							:     Optional[Any]									=  torch.Size((2, 25) )
								self.assertEqual(outputs.start_logits.shape		,				_A )
								self.assertEqual(outputs.end_logits.shape		,				_A )










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a		:	Any      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:	Optional[int]      					=			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}
class    a   (			lowercase__       ):



      """simple docstring"""


      def __init__(				self :      List[Any]		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      Tuple=None		,				__lowercase :      Optional[int]=False		,				__lowercase :      Any=True		,				__lowercase :      List[str]=False		,				__lowercase :      Union[str, Any]="<s>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      Dict="<unk>"		,				__lowercase :      List[str]="<sep>"		,				__lowercase :      int="<pad>"		,				__lowercase :      Any="<cls>"		,				__lowercase :      int="<mask>"		,				__lowercase :      Tuple=["<eop>", "<eod>"]		,				**__lowercase :      Union[str, Any]		,				)							->				Optional[Any]:
        __UpperCAmelCase							:     Any									=  AddedToken(__lowercase		,				lstrip=__lowercase		,				rstrip=__lowercase ) if isinstance(__lowercase		,				__lowercase ) else mask_token

        super().__init__(
            vocab_file=__lowercase		,				tokenizer_file=__lowercase		,				do_lower_case=__lowercase		,				remove_space=__lowercase		,				keep_accents=__lowercase		,				bos_token=__lowercase		,				eos_token=__lowercase		,				unk_token=__lowercase		,				sep_token=__lowercase		,				pad_token=__lowercase		,				cls_token=__lowercase		,				mask_token=__lowercase		,				additional_special_tokens=__lowercase		,				**__lowercase		,				)

        __UpperCAmelCase							:     Optional[int]									=  3
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Union[str, Any]									=  remove_space
        __UpperCAmelCase							:     Tuple									=  keep_accents
        __UpperCAmelCase							:     int									=  vocab_file
        __UpperCAmelCase							:     Dict									=  False if not self.vocab_file else True

        try:
          import jieba
        except ModuleNotFoundError as error:
          raise error.__class__(
              """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
              """See https://pypi.org/project/jieba/ for installation.""" )
        __UpperCAmelCase							:     Dict									=  jieba
        __UpperCAmelCase							:     str									=  str.maketrans(""" \n"""		,				"""\u2582\u2583""" )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[str]									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [self.cls_token_id]
        if token_ids_a is None:
          return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     Dict									=  [self.sep_token_id]
        __UpperCAmelCase							:     List[str]									=  [2]

        if token_ids_a is None:
          return len(token_ids_a + sep ) * [0] + cls_segment_id
        return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
      def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        if not self.can_save_slow_tokenizer:
          raise ValueError(
              """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
              """tokenizer.""" )

        if not os.path.isdir(__lowercase ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
          return
        __UpperCAmelCase							:     List[str]									=  os.path.join(
            __lowercase		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
          copyfile(self.vocab_file		,				__lowercase )

        return (out_vocab_file,)
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Any		,				*__lowercase :      Dict		,				**__lowercase :      int )							->				Optional[Any]:
        __UpperCAmelCase							:     List[str]									=  [
            """ """.join([x.translate(self.translator ) for x in self.jieba.cut(__lowercase		,				cut_all=__lowercase )] )
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(__lowercase		,				*__lowercase		,				**__lowercase )




      def      UpperCAmelCase				(				self :      int		,				*__lowercase :      List[Any]		,				**__lowercase :      Tuple )							->				List[str]:
        __UpperCAmelCase							:     List[str]									=  super()._decode(*__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     List[Any]									=  text.replace(""" """		,				"""""" ).replace("""\u2582"""		,				""" """ ).replace("""\u2583"""		,				"""\n""" )
        return text


