






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
							import tensorflow as tf

							from transformers import (
							    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    FlaubertConfig,
							    TFFlaubertForMultipleChoice,
							    TFFlaubertForQuestionAnsweringSimple,
							    TFFlaubertForSequenceClassification,
							    TFFlaubertForTokenClassification,
							    TFFlaubertModel,
							    TFFlaubertWithLMHeadModel,
							)

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Dict  ,     lowerCamelCase__     :List[str]  ,     ):
					UpperCamelCase__ :Optional[int]						=      parent
					UpperCamelCase__ :int						=      13
					UpperCamelCase__ :Optional[int]						=      7
					UpperCamelCase__ :Dict						=      True
					UpperCamelCase__ :Dict						=      True
					UpperCamelCase__ :str						=      True
					UpperCamelCase__ :List[Any]						=      True
					UpperCamelCase__ :Any						=      True
					UpperCamelCase__ :Optional[int]						=      False
					UpperCamelCase__ :Optional[int]						=      False
					UpperCamelCase__ :Tuple						=      False
					UpperCamelCase__ :Optional[int]						=      2
					UpperCamelCase__ :List[str]						=      99
					UpperCamelCase__ :Optional[Any]						=      0
					UpperCamelCase__ :Any						=      32
					UpperCamelCase__ :List[str]						=      2
					UpperCamelCase__ :int						=      4
					UpperCamelCase__ :List[str]						=      0.1
					UpperCamelCase__ :Union[str, Any]						=      0.1
					UpperCamelCase__ :Union[str, Any]						=      5_12
					UpperCamelCase__ :List[str]						=      16
					UpperCamelCase__ :str						=      2
					UpperCamelCase__ :Optional[int]						=      0.02
					UpperCamelCase__ :Optional[int]						=      3
					UpperCamelCase__ :Optional[int]						=      4
					UpperCamelCase__ :Optional[int]						=      """last"""
					UpperCamelCase__ :Tuple						=      True
					UpperCamelCase__ :int						=      None
					UpperCamelCase__ :Dict						=      0







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Optional[Any]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )
					UpperCamelCase__ :Any						=      random_attention_mask([self.batch_size, self.seq_length]  ,     dtype=tf.floataa     )

					UpperCamelCase__ :Union[str, Any]						=      None
					if self.use_input_lengths:
						UpperCamelCase__ :Union[str, Any]						=      (
						    ids_tensor([self.batch_size]  ,     vocab_size=2     ) + self.seq_length - 2
						)  # small variation of seq_length

					UpperCamelCase__ :List[str]						=      None
					if self.use_token_type_ids:
						UpperCamelCase__ :List[str]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.n_langs     )

					UpperCamelCase__ :int						=      None
					UpperCamelCase__ :List[str]						=      None
					UpperCamelCase__ :List[str]						=      None
					if self.use_labels:
						UpperCamelCase__ :List[Any]						=      ids_tensor([self.batch_size]  ,     self.type_sequence_label_size     )
						UpperCamelCase__ :Union[str, Any]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels     )
						UpperCamelCase__ :str						=      ids_tensor([self.batch_size]  ,     2  ,     dtype=tf.floataa     )
						UpperCamelCase__ :int						=      ids_tensor([self.batch_size]  ,     self.num_choices     )

					UpperCamelCase__ :List[Any]						=      FlaubertConfig(
					    vocab_size=self.vocab_size  ,     n_special=self.n_special  ,     emb_dim=self.hidden_size  ,     n_layers=self.num_hidden_layers  ,     n_heads=self.num_attention_heads  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     gelu_activation=self.gelu_activation  ,     sinusoidal_embeddings=self.sinusoidal_embeddings  ,     asm=self.asm  ,     causal=self.causal  ,     n_langs=self.n_langs  ,     max_position_embeddings=self.max_position_embeddings  ,     initializer_range=self.initializer_range  ,     summary_type=self.summary_type  ,     use_proj=self.use_proj  ,     bos_token_id=self.bos_token_id  ,     )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    input_lengths,
					    sequence_labels,
					    token_labels,
					    is_impossible_labels,
					    choice_labels,
					    input_mask,
					)







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :int  ,     ):
					UpperCamelCase__ :int						=      TFFlaubertModel(config=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
					UpperCamelCase__ :Optional[Any]						=      model(lowerCamelCase__     )

					UpperCamelCase__ :Union[str, Any]						=      [input_ids, input_mask]
					UpperCamelCase__ :Optional[int]						=      model(lowerCamelCase__     )
					self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Optional[Any]  ,     ):
					UpperCamelCase__ :List[str]						=      TFFlaubertWithLMHeadModel(lowerCamelCase__     )

					UpperCamelCase__ :Optional[Any]						=      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
					UpperCamelCase__ :Any						=      model(lowerCamelCase__     )

					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)     )







				def 				__a   ( self     :Dict  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Tuple  ,     ):
					UpperCamelCase__ :int						=      TFFlaubertForQuestionAnsweringSimple(lowerCamelCase__     )

					UpperCamelCase__ :int						=      {"""input_ids""": input_ids, """lengths""": input_lengths}

					UpperCamelCase__ :Optional[int]						=      model(lowerCamelCase__     )

					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 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Optional[int]  ,     ):
					UpperCamelCase__ :List[Any]						=      TFFlaubertForSequenceClassification(lowerCamelCase__     )

					UpperCamelCase__ :List[str]						=      {"""input_ids""": input_ids, """lengths""": input_lengths}

					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__     )

					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Any  ,     ):
					UpperCamelCase__ :Any						=      self.num_labels
					UpperCamelCase__ :Tuple						=      TFFlaubertForTokenClassification(config=lowerCamelCase__     )
					UpperCamelCase__ :Any						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					UpperCamelCase__ :List[Any]						=      model(lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :List[str]  ,     ):
					UpperCamelCase__ :Optional[int]						=      self.num_choices
					UpperCamelCase__ :Dict						=      TFFlaubertForMultipleChoice(config=lowerCamelCase__     )
					UpperCamelCase__ :Any						=      tf.tile(tf.expand_dims(lowerCamelCase__  ,     1     )  ,     (1, self.num_choices, 1)     )
					UpperCamelCase__ :str						=      tf.tile(tf.expand_dims(lowerCamelCase__  ,     1     )  ,     (1, self.num_choices, 1)     )
					UpperCamelCase__ :Any						=      tf.tile(tf.expand_dims(lowerCamelCase__  ,     1     )  ,     (1, self.num_choices, 1)     )
					UpperCamelCase__ :int						=      {
					    """input_ids""": multiple_choice_inputs_ids,
					    """attention_mask""": multiple_choice_input_mask,
					    """token_type_ids""": multiple_choice_token_type_ids,
					}
					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)     )







				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :str						=      self.prepare_config_and_inputs()
					(
					    (
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		
					) :str						=      config_and_inputs
					UpperCamelCase__ :Optional[Any]						=      {
					    """input_ids""": input_ids,
					    """token_type_ids""": token_type_ids,
					    """langs""": token_type_ids,
					    """lengths""": input_lengths,
					}
					return config, inputs_dict

@require_tf
class        lowerCAmelCase_				(      lowercase						,      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			(
				    (
				        TFFlaubertModel,
				        TFFlaubertWithLMHeadModel,
				        TFFlaubertForSequenceClassification,
				        TFFlaubertForQuestionAnsweringSimple,
				        TFFlaubertForTokenClassification,
				        TFFlaubertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_snake_case				:   List[Any]        =			(
				    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				_snake_case				:   Optional[int]        =			(
				    {
				        """feature-extraction""": TFFlaubertModel,
				        """fill-mask""": TFFlaubertWithLMHeadModel,
				        """question-answering""": TFFlaubertForQuestionAnsweringSimple,
				        """text-classification""": TFFlaubertForSequenceClassification,
				        """token-classification""": TFFlaubertForTokenClassification,
				        """zero-shot""": TFFlaubertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case				:   List[Any]        =			False
				_snake_case				:   Tuple        =			False







				def 				__a   ( self     :Optional[int]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :List[Any]     ):
					if (
					    pipeline_test_casse_name == "QAPipelineTests"
					    and tokenizer_name is not None
					    and not tokenizer_name.endswith("""Fast"""     )
					):
						# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
						# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
						# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
						return True

					return False







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :List[str]						=      TFFlaubertModelTester(self     )
					UpperCamelCase__ :Tuple						=      ConfigTester(self  ,     config_class=lowerCamelCase__  ,     emb_dim=37     )







				def 				__a   ( self     :int     ):
					self.config_tester.run_common_tests()







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_model(*lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :Tuple						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_lm_head(*lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Optional[int]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_qa(*lowerCamelCase__     )







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCamelCase__     )







				def 				__a   ( self     :Any     ):
					UpperCamelCase__ :Any						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_for_token_classification(*lowerCamelCase__     )







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowerCamelCase__     )







				@slow
				def 				__a   ( self     :str     ):
					for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :Dict						=      TFFlaubertModel.from_pretrained(lowerCamelCase__     )
						self.assertIsNotNone(lowerCamelCase__     )

@require_tf
@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				@slow
				def 				__a   ( self     :str     ):
					UpperCamelCase__ :Tuple						=      TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""     )

					UpperCamelCase__ :Optional[int]						=      tf.convert_to_tensor(
					    [[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]]  ,     dtype=tf.intaa  ,     )  # "J'aime flaubert !"

					UpperCamelCase__ :Optional[Any]						=      model(lowerCamelCase__     )[0]
					UpperCamelCase__ :Optional[int]						=      tf.TensorShape((1, 8, 5_12)     )
					self.assertEqual(output.shape  ,     lowerCamelCase__     )
					# compare the actual values for a slice.
					UpperCamelCase__ :str						=      tf.convert_to_tensor(
					    [
					        [
					            [-1.876_8773, -1.56_6555, 0.2707_2418],
					            [-1.692_0038, -0.587_3505, 1.932_9599],
					            [-2.956_3985, -1.699_3835, 1.797_2052],
					        ]
					    ]  ,     dtype=tf.floataa  ,     )

					self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1e-4     )     )






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_lowerCAmelCase				:   Optional[Any]						       =						logging.get_logger(__name__)


_lowerCAmelCase				:   List[str]						       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase				:   int						       =						{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

_lowerCAmelCase				:   Optional[int]						       =						{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']
     __UpperCamelCase            =  BartTokenizer



     def __init__(				self							:List[str]      ,   snake_case							:Tuple=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:List[Any]="replace"      ,   snake_case							:List[str]="<s>"      ,   snake_case							:Optional[int]="</s>"      ,   snake_case							:Union[str, Any]="</s>"      ,   snake_case							:Optional[Any]="<s>"      ,   snake_case							:List[Any]="<unk>"      ,   snake_case							:Optional[Any]="<pad>"      ,   snake_case							:Dict="<mask>"      ,   snake_case							:int=False      ,   snake_case							:List[Any]=True      ,   **snake_case							:Union[str, Any]      ,   ):





        '''simple docstring'''




        super().__init__(
            snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   errors=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   unk_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   trim_offsets=snake_case      ,   **snake_case      ,   )

        A_       :					Optional[Any]        =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
           A_       :					Dict        =      getattr(snake_case      ,   pre_tok_state.pop("type"	)	)
           A_       :					List[str]        =      add_prefix_space
           A_       :					int        =      pre_tok_class(**snake_case	)

        A_       :					Any        =      add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A_       :					Tuple        =      "post_processor"
        A_       :					Union[str, Any]        =      getattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)
        if tokenizer_component_instance:
           A_       :					Any        =      json.loads(tokenizer_component_instance.__getstate__()	)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              A_       :					List[Any]        =      tuple(state["sep"]	)
           if "cls" in state:
              A_       :					str        =      tuple(state["cls"]	)

           A_       :					int        =      False

           if state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
              A_       :					List[Any]        =      add_prefix_space
              A_       :					Union[str, Any]        =      True

           if state.get("trim_offsets"      ,   snake_case	) != trim_offsets:
              A_       :					int        =      trim_offsets
              A_       :					str        =      True

           if changes_to_apply:
              A_       :					Tuple        =      getattr(snake_case      ,   state.pop("type"	)	)
              A_       :					Union[str, Any]        =      component_class(**snake_case	)
              setattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
           return None
        return str(self._mask_token	)



     @mask_token.setter
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Optional[int]        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else value
        A_       :					List[Any]        =      value



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   *snake_case							:str      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					Tuple        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._batch_encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   *snake_case							:Any      ,   **snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        A_       :					Any        =      self._tokenizer.model.save(snake_case      ,   name=snake_case	)
        return tuple(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:List[str]      ,   snake_case							:Optional[int]=None	):





        '''simple docstring'''




        A_       :					str        =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Dict        =      [self.sep_token_id]
        A_       :					List[str]        =      [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 + sep + token_ids_a + sep	) * [0]
