




'''simple docstring'''


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   __A :



   """simple docstring"""







   def __init__(      self				, _lowerCamelCase				, )->       Optional[Any]:
      lowercase__       							=							parent
      lowercase__       							=							1_3
      lowercase__       							=							7
      lowercase__       							=							True
      lowercase__       							=							True
      lowercase__       							=							True
      lowercase__       							=							True
      lowercase__       							=							True
      lowercase__       							=							False
      lowercase__       							=							False
      lowercase__       							=							False
      lowercase__       							=							2
      lowercase__       							=							9_9
      lowercase__       							=							0
      lowercase__       							=							3_2
      lowercase__       							=							2
      lowercase__       							=							4
      lowercase__       							=							0.1
      lowercase__       							=							0.1
      lowercase__       							=							5_1_2
      lowercase__       							=							1_6
      lowercase__       							=							2
      lowercase__       							=							0.0_2
      lowercase__       							=							3
      lowercase__       							=							4
      lowercase__       							=							"last"
      lowercase__       							=							True
      lowercase__       							=							None
      lowercase__       							=							0

   def snake_case_(      self						)->       List[str]:
      lowercase__       							=							ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size						)
      lowercase__       							=							random_attention_mask([self.batch_size, self.seq_length]				, dtype=tf.floataa						)

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

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

      lowercase__       							=							None
      lowercase__       							=							None
      lowercase__       							=							None
      if self.use_labels:
         lowercase__       							=							ids_tensor([self.batch_size]				, self.type_sequence_label_size						)
         lowercase__       							=							ids_tensor([self.batch_size, self.seq_length]				, self.num_labels						)
         lowercase__       							=							ids_tensor([self.batch_size]				, 2				, dtype=tf.floataa						)
         lowercase__       							=							ids_tensor([self.batch_size]				, self.num_choices						)

      lowercase__       							=							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 snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       List[Any]:
      lowercase__       							=							TFFlaubertModel(config=A_						)
      lowercase__       							=							{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      lowercase__       							=							model(A_						)

      lowercase__       							=							[input_ids, input_mask]
      lowercase__       							=							model(A_						)
      self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)						)

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       int:
      lowercase__       							=							TFFlaubertWithLMHeadModel(A_						)

      lowercase__       							=							{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      lowercase__       							=							model(A_						)

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

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       Tuple:
      lowercase__       							=							TFFlaubertForQuestionAnsweringSimple(A_						)

      lowercase__       							=							{"input_ids": input_ids, "lengths": input_lengths}

      lowercase__       							=							model(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 snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       Any:
      lowercase__       							=							TFFlaubertForSequenceClassification(A_						)

      lowercase__       							=							{"input_ids": input_ids, "lengths": input_lengths}

      lowercase__       							=							model(A_						)

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

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       Tuple:
      lowercase__       							=							self.num_labels
      lowercase__       							=							TFFlaubertForTokenClassification(config=A_						)
      lowercase__       							=							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
      lowercase__       							=							model(A_						)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)						)

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       Optional[int]:
      lowercase__       							=							self.num_choices
      lowercase__       							=							TFFlaubertForMultipleChoice(config=A_						)
      lowercase__       							=							tf.tile(tf.expand_dims(A_				, 1						)				, (1, self.num_choices, 1)						)
      lowercase__       							=							tf.tile(tf.expand_dims(A_				, 1						)				, (1, self.num_choices, 1)						)
      lowercase__       							=							tf.tile(tf.expand_dims(A_				, 1						)				, (1, self.num_choices, 1)						)
      lowercase__       							=							{
          "input_ids": multiple_choice_inputs_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
      }
      lowercase__       							=							model(A_						)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_choices)						)

   def snake_case_(      self						)->       Union[str, Any]:
      lowercase__       							=							self.prepare_config_and_inputs()
      (
          lowercase__       
      )       							=							config_and_inputs
      lowercase__       							=							{
          "input_ids": input_ids,
          "token_type_ids": token_type_ids,
          "langs": token_type_ids,
          "lengths": input_lengths,
      }
      return config, inputs_dict



@require_tf
class   __A (							__snake_case     ,   __snake_case     ,   unittest.TestCase  ):



   """simple docstring"""







   A_												=				(
       (
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
           TFFlaubertForSequenceClassification,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForTokenClassification,
           TFFlaubertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   A_												=				(
       (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A_												=				(
       {
           'feature-extraction': TFFlaubertModel,
           'fill-mask': TFFlaubertWithLMHeadModel,
           'question-answering': TFFlaubertForQuestionAnsweringSimple,
           'text-classification': TFFlaubertForSequenceClassification,
           'token-classification': TFFlaubertForTokenClassification,
           'zero-shot': TFFlaubertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_												=				False
   A_												=				False

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




      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 snake_case_(      self						)->       Tuple:
      lowercase__       							=							TFFlaubertModelTester(self						)
      lowercase__       							=							ConfigTester(self				, config_class=A_				, emb_dim=3_7						)

   def snake_case_(      self						)->       Union[str, Any]:
      self.config_tester.run_common_tests()

   def snake_case_(      self						)->       Dict:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*A_						)

   def snake_case_(      self						)->       List[str]:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*A_						)

   def snake_case_(      self						)->       str:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*A_						)

   def snake_case_(      self						)->       Optional[int]:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*A_						)

   def snake_case_(      self						)->       List[str]:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_token_classification(*A_						)

   def snake_case_(      self						)->       Any:
      lowercase__       							=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_						)

   @slow
   def snake_case_(      self						)->       str:




      for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__       							=							TFFlaubertModel.from_pretrained(A_						)
         self.assertIsNotNone(A_						)



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



   """simple docstring"""







   @slow
   def snake_case_(      self						)->       Union[str, Any]:
      lowercase__       							=							TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''						)

      lowercase__       							=							tf.convert_to_tensor(
          [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]]				, dtype=tf.intaa				, )  # "J'aime flaubert !"

      lowercase__       							=							model(A_						)[0]
      lowercase__       							=							tf.TensorShape((1, 8, 5_1_2)						)
      self.assertEqual(output.shape				, A_						)
      # compare the actual values for a slice.
      lowercase__       							=							tf.convert_to_tensor(
          [
              [
                  [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                  [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                  [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
              ]
          ]				, dtype=tf.floataa				, )

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











import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowerCamelCase			:	Tuple					    =	logging.get_logger(__name__)


__lowerCamelCase			:	int					    =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__lowerCamelCase			:	str					    =	{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase			:	List[str]					    =	{
    """allenai/led-base-16384""": 1_6384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						A_							(	)		->					List[str]:
 UpperCamelCase      :   Optional[int]			      =  (
     list(range(ord("!"	)				,  ord("~"	) + 1	)	) + list(range(ord("¡"	)				,  ord("¬"	) + 1	)	) + list(range(ord("®"	)				,  ord("ÿ"	) + 1	)	)
 )
 UpperCamelCase      :   List[Any]			      =  bs[:]
 UpperCamelCase      :   List[Any]			      =  0
 for b in range(2**8	):
  if b not in bs:
   bs.append(_lowerCAmelCase	)
   cs.append(2**8 + n	)
   n += 1
 UpperCamelCase      :   Union[str, Any]			      =  [chr(_lowerCAmelCase	) for n in cs]
 return dict(zip(_lowerCAmelCase				,  _lowerCAmelCase	)	)



def 						A_							(	_lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   int			      =  set()
 UpperCamelCase      :   List[str]			      =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)	)
  UpperCamelCase      :   List[str]			      =  char
 return pairs



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Dict		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :List[Any]		       =  PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :List[Any]		       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Union[str, Any]		       =  ['input_ids', 'attention_mask']





      def __init__(					self			,				A_			,				A_			,				A_="replace"			,				A_="<s>"			,				A_="</s>"			,				A_="</s>"			,				A_="<s>"			,				A_="<unk>"			,				A_="<pad>"			,				A_="<mask>"			,				A_=False			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else bos_token
       UpperCamelCase      :   Tuple			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else eos_token
       UpperCamelCase      :   Optional[Any]			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else sep_token
       UpperCamelCase      :   Union[str, Any]			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else cls_token
       UpperCamelCase      :   Dict			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else unk_token
       UpperCamelCase      :   int			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase      :   Optional[Any]			      =  AddedToken(A_			,				lstrip=A_			,				rstrip=A_  ) if isinstance(A_			,				A_  ) else mask_token

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

       with open(A_			,				encoding="utf-8"  ) as vocab_handle:
        UpperCamelCase      :   int			      =  json.load(A_  )
       UpperCamelCase      :   Dict			      =  {v: k for k, v in self.encoder.items()}
       UpperCamelCase      :   List[str]			      =  errors  # how to handle errors in decoding
       UpperCamelCase      :   Optional[Any]			      =  bytes_to_unicode()
       UpperCamelCase      :   Tuple			      =  {v: k for k, v in self.byte_encoder.items()}
       with open(A_			,				encoding="utf-8"  ) as merges_handle:
        UpperCamelCase      :   str			      =  merges_handle.read().split("\n"  )[1:-1]
       UpperCamelCase      :   Optional[Any]			      =  [tuple(merge.split()  ) for merge in bpe_merges]
       UpperCamelCase      :   Any			      =  dict(zip(A_			,				range(len(A_  )  )  )  )
       UpperCamelCase      :   List[str]			      =  {}
       UpperCamelCase      :   Any			      =  add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCamelCase      :   Any			      =  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"  )





      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return len(self.encoder  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return dict(self.encoder			,				**self.added_tokens_encoder  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       if token in self.cache:
        return self.cache[token]
       UpperCamelCase      :   Union[str, Any]			      =  tuple(A_  )
       UpperCamelCase      :   str			      =  get_pairs(A_  )

       if not pairs:
        return token

       while True:
        UpperCamelCase      :   Any			      =  min(A_			,				key=lambda A_  : self.bpe_ranks.get(A_			,				float("inf"  )  )  )
        if bigram not in self.bpe_ranks:
         break
        UpperCamelCase      ,       UpperCamelCase      :   Union[str, Any]			      =  bigram
        UpperCamelCase      :   List[Any]			      =  []
        UpperCamelCase      :   Union[str, Any]			      =  0
        while i < len(A_  ):
         try:
          UpperCamelCase      :   Union[str, Any]			      =  word.index(A_			,				A_  )
         except ValueError:
          new_word.extend(word[i:]  )
          break
         else:
          new_word.extend(word[i:j]  )
          UpperCamelCase      :   Union[str, Any]			      =  j

         if word[i] == first and i < len(A_  ) - 1 and word[i + 1] == second:
          new_word.append(first + second  )
          i += 2
         else:
          new_word.append(word[i]  )
          i += 1
        UpperCamelCase      :   Tuple			      =  tuple(A_  )
        UpperCamelCase      :   Union[str, Any]			      =  new_word
        if len(A_  ) == 1:
         break
        else:
         UpperCamelCase      :   int			      =  get_pairs(A_  )
       UpperCamelCase      :   Union[str, Any]			      =  " ".join(A_  )
       UpperCamelCase      :   Any			      =  word
       return word





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  []
       for token in re.findall(self.pat			,				A_  ):
        UpperCamelCase      :   Optional[Any]			      =  "".join(
            self.byte_encoder[b] for b in token.encode("utf-8"  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_  ).split(" "  )  )
       return bpe_tokens





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       return self.encoder.get(A_			,				self.encoder.get(self.unk_token  )  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       return self.decoder.get(A_  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  "".join(A_  )
       UpperCamelCase      :   List[Any]			      =  bytearray([self.byte_decoder[c] for c in text]  ).decode("utf-8"			,				errors=self.errors  )
       return text





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




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

       with open(A_			,				"w"			,				encoding="utf-8"  ) as f:
        f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=A_			,				ensure_ascii=A_  ) + "\n"  )

       UpperCamelCase      :   str			      =  0
       with open(A_			,				"w"			,				encoding="utf-8"  ) as writer:
        writer.write("#version: 0.2\n"  )
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda A_  : kv[1]  ):
         if index != token_index:
          logger.warning(
              F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
              " Please check that the tokenizer is not corrupted!"  )
          UpperCamelCase      :   Any			      =  token_index
         writer.write(" ".join(A_  ) + "\n"  )
         index += 1

       return vocab_file, merge_file





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase      :   List[str]			      =  [self.cls_token_id]
       UpperCamelCase      :   Optional[Any]			      =  [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None			,				A_ = False  ):
       '''simple docstring'''




       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=A_			,				token_ids_a=A_			,				already_has_special_tokens=A_  )

       if token_ids_a is None:
        return [1] + ([0] * len(A_  )) + [1]
       return [1] + ([0] * len(A_  )) + [1, 1] + ([0] * len(A_  )) + [1]





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  [self.sep_token_id]
       UpperCamelCase      :   Any			      =  [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]





      def 			__UpperCamelCase(					self			,				A_			,				A_=False			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  kwargs.pop("add_prefix_space"			,				self.add_prefix_space  )
       if (is_split_into_words or add_prefix_space) and (len(A_  ) > 0 and not text[0].isspace()):
        UpperCamelCase      :   List[str]			      =  " " + text
       return (text, kwargs)





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None			,				A_ = PaddingStrategy.DO_NOT_PAD			,				A_ = None			,				A_ = None			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  super()._pad(
           encoded_inputs=A_			,				max_length=A_			,				padding_strategy=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				)

       # Load from model defaults
       if return_attention_mask is None:
        UpperCamelCase      :   Union[str, Any]			      =  "attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        UpperCamelCase      :   int			      =  encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        UpperCamelCase      :   str			      =  len(encoded_inputs["global_attention_mask"]  ) != len(A_  )

        if needs_to_be_padded:
         UpperCamelCase      :   Optional[int]			      =  len(A_  ) - len(encoded_inputs["global_attention_mask"]  )

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          UpperCamelCase      :   Union[str, Any]			      =  (
              encoded_inputs["global_attention_mask"] + [-1] * difference
          )
         elif self.padding_side == "left":
          UpperCamelCase      :   Tuple			      =  [-1] * difference + encoded_inputs[
              "global_attention_mask"
          ]
         else:
          raise ValueError("Invalid padding strategy:" + str(self.padding_side  )  )

       return encoded_inputs
