import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							int    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      list(tokenizer_class.max_model_input_sizes.keys()    )
  for model_name in sa_models[:1]:
   UpperCamelCase_      :		int				=      tokenizer_class.from_pretrained(snake_case    )
   self.assertIsNotNone(snake_case    )
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(snake_case     ,   snake_case    )

   for special_tok in tokenizer.all_special_tokens:
    self.assertIsInstance(snake_case     ,   snake_case    )
    UpperCamelCase_      :		str				=      tokenizer.convert_tokens_to_ids(snake_case    )
    self.assertIsInstance(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							BatchEncoding     ,   snake_case			:							Optional[Callable] = None    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      pickle.dumps(snake_case    )
  self.assertIsNotNone(snake_case    )

  UpperCamelCase_      :		Tuple				=      pickle.loads(snake_case    )

  # Ensure is_fast is correctly restored
  self.assertEqual(be_restored.is_fast     ,   be_original.is_fast    )

  # Ensure encodings are potentially correctly restored
  if be_original.is_fast:
   self.assertIsNotNone(be_restored.encodings    )
  else:
   self.assertIsNone(be_restored.encodings    )

  # Ensure the keys are the same
  for original_v, restored_v in zip(be_original.values()     ,   be_restored.values()    ):
   if equal_op:
    self.assertTrue(equal_op(snake_case     ,   snake_case    )    )
   else:
    self.assertEqual(snake_case     ,   snake_case    )

 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		List[str]:

  """simple docstring"""





  self.check_tokenizer_from_pretrained(snake_case    )

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

  """simple docstring"""





  self.assertEqual(TensorType('tf'    )     ,   TensorType.TENSORFLOW    )
  self.assertEqual(TensorType('pt'    )     ,   TensorType.PYTORCH    )
  self.assertEqual(TensorType('np'    )     ,   TensorType.NUMPY    )

 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		str:

  """simple docstring"""





  import numpy as np

  UpperCamelCase_      :		Optional[Any]				=      BertTokenizer.from_pretrained('bert-base-cased'    )
  UpperCamelCase_      :		int				=      BertTokenizerFast.from_pretrained('bert-base-cased'    )

  # Python no tensor
  with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
   self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )

  with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
   self.assert_dump_and_restore(
       tokenizer_p('Small example to encode'     ,   return_tensors=TensorType.NUMPY    )     ,   np.array_equal    )

  with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
   self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )

  with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
   self.assert_dump_and_restore(
       tokenizer_r('Small example to encode'     ,   return_tensors=TensorType.NUMPY    )     ,   np.array_equal    )

 @require_tf
 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		List[str]:

  """simple docstring"""





  import tensorflow as tf

  def tf_array_equals(snake_case			:							Optional[int]     ,   snake_case			:							Optional[int]    ):
   return tf.reduce_all(tf.equal(snake_case     ,   snake_case    )    )

  UpperCamelCase_      :		Tuple				=      BertTokenizer.from_pretrained('bert-base-cased'    )
  UpperCamelCase_      :		List[Any]				=      BertTokenizerFast.from_pretrained('bert-base-cased'    )

  with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
   self.assert_dump_and_restore(
       tokenizer_p('Small example to encode'     ,   return_tensors=TensorType.TENSORFLOW    )     ,   snake_case    )

  with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
   self.assert_dump_and_restore(
       tokenizer_r('Small example to encode'     ,   return_tensors=TensorType.TENSORFLOW    )     ,   snake_case    )

 @require_torch
 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Optional[int]:

  """simple docstring"""





  import torch

  UpperCamelCase_      :		int				=      BertTokenizer.from_pretrained('bert-base-cased'    )
  UpperCamelCase_      :		Optional[Any]				=      BertTokenizerFast.from_pretrained('bert-base-cased'    )

  with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
   self.assert_dump_and_restore(
       tokenizer_p('Small example to encode'     ,   return_tensors=TensorType.PYTORCH    )     ,   torch.equal    )

  with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
   self.assert_dump_and_restore(
       tokenizer_r('Small example to encode'     ,   return_tensors=TensorType.PYTORCH    )     ,   torch.equal    )

 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      BertTokenizer.from_pretrained('bert-base-cased'    )
  UpperCamelCase_      :		str				=      BertTokenizerFast.from_pretrained('bert-base-cased'    )

  with self.subTest('Python Tokenizer'    ):
   self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )

  with self.subTest('Rust Tokenizer'    ):
   self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )

 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      BertTokenizerFast.from_pretrained('bert-base-cased'    )
  UpperCamelCase_      :		Tuple				=      tokenizer_r(['Test', '\xad', 'test']     ,   is_split_into_words=snake_case    )

  self.assertEqual(encoded.word_to_tokens(0    )     ,   TokenSpan(start=1     ,   end=2    )    )
  self.assertEqual(encoded.word_to_tokens(1    )     ,   snake_case    )
  self.assertEqual(encoded.word_to_tokens(2    )     ,   TokenSpan(start=2     ,   end=3    )    )

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

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  UpperCamelCase_      :		Any				=      batch.convert_to_tensors(tensor_type='np'    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (2, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (2,)    )
  # test converting the converted
  with CaptureStderr() as cs:
   UpperCamelCase_      :		List[Any]				=      batch.convert_to_tensors(tensor_type='np'    )
  self.assertFalse(len(cs.err    )     ,   msg=f"should have no warning, but got {cs.err}"    )

  UpperCamelCase_      :		Optional[int]				=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  UpperCamelCase_      :		Optional[Any]				=      batch.convert_to_tensors(tensor_type='np'     ,   prepend_batch_axis=snake_case    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (1, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (1,)    )

 @require_torch
 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  UpperCamelCase_      :		int				=      batch.convert_to_tensors(tensor_type='pt'    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (2, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (2,)    )
  # test converting the converted
  with CaptureStderr() as cs:
   UpperCamelCase_      :		Optional[int]				=      batch.convert_to_tensors(tensor_type='pt'    )
  self.assertFalse(len(cs.err    )     ,   msg=f"should have no warning, but got {cs.err}"    )

  UpperCamelCase_      :		str				=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  UpperCamelCase_      :		Union[str, Any]				=      batch.convert_to_tensors(tensor_type='pt'     ,   prepend_batch_axis=snake_case    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (1, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (1,)    )

 @require_tf
 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  UpperCamelCase_      :		Dict				=      batch.convert_to_tensors(tensor_type='tf'    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (2, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (2,)    )
  # test converting the converted
  with CaptureStderr() as cs:
   UpperCamelCase_      :		Dict				=      batch.convert_to_tensors(tensor_type='tf'    )
  self.assertFalse(len(cs.err    )     ,   msg=f"should have no warning, but got {cs.err}"    )

  UpperCamelCase_      :		Tuple				=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  UpperCamelCase_      :		Union[str, Any]				=      batch.convert_to_tensors(tensor_type='tf'     ,   prepend_batch_axis=snake_case    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (1, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (1,)    )

 @require_flax
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  UpperCamelCase_      :		List[str]				=      batch.convert_to_tensors(tensor_type='jax'    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (2, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (2,)    )
  # test converting the converted
  with CaptureStderr() as cs:
   UpperCamelCase_      :		Optional[int]				=      batch.convert_to_tensors(tensor_type='jax'    )
  self.assertFalse(len(cs.err    )     ,   msg=f"should have no warning, but got {cs.err}"    )

  UpperCamelCase_      :		Optional[Any]				=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  UpperCamelCase_      :		Union[str, Any]				=      batch.convert_to_tensors(tensor_type='jax'     ,   prepend_batch_axis=snake_case    )
  self.assertEqual(tensor_batch['inputs'].shape     ,   (1, 3)    )
  self.assertEqual(tensor_batch['labels'].shape     ,   (1,)    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
  UpperCamelCase_      :		Optional[int]				=      BertTokenizer.from_pretrained('bert-base-cased'    )

  UpperCamelCase_      :		Optional[Any]				=      tokenizer.pad(snake_case     ,   padding=snake_case    )
  self.assertTrue(isinstance(batch['input_ids']     ,   np.ndarray    )    )
  self.assertEqual(batch['input_ids'].tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  UpperCamelCase_      :		int				=      tokenizer.pad(snake_case     ,   padding=snake_case     ,   return_tensors='np'    )
  self.assertTrue(isinstance(batch['input_ids']     ,   np.ndarray    )    )
  self.assertEqual(batch['input_ids'].tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

 @require_torch
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Any:

  """simple docstring"""





  import torch

  UpperCamelCase_      :		Tuple				=      [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
  UpperCamelCase_      :		List[str]				=      BertTokenizer.from_pretrained('bert-base-cased'    )

  UpperCamelCase_      :		Union[str, Any]				=      tokenizer.pad(snake_case     ,   padding=snake_case    )
  self.assertTrue(isinstance(batch['input_ids']     ,   torch.Tensor    )    )
  self.assertEqual(batch['input_ids'].tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  UpperCamelCase_      :		Any				=      tokenizer.pad(snake_case     ,   padding=snake_case     ,   return_tensors='pt'    )
  self.assertTrue(isinstance(batch['input_ids']     ,   torch.Tensor    )    )
  self.assertEqual(batch['input_ids'].tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

 @require_tf
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Optional[Any]:

  """simple docstring"""





  import tensorflow as tf

  UpperCamelCase_      :		int				=      [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
  UpperCamelCase_      :		int				=      BertTokenizer.from_pretrained('bert-base-cased'    )

  UpperCamelCase_      :		Dict				=      tokenizer.pad(snake_case     ,   padding=snake_case    )
  self.assertTrue(isinstance(batch['input_ids']     ,   tf.Tensor    )    )
  self.assertEqual(batch['input_ids'].numpy().tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  UpperCamelCase_      :		Union[str, Any]				=      tokenizer.pad(snake_case     ,   padding=snake_case     ,   return_tensors='tf'    )
  self.assertTrue(isinstance(batch['input_ids']     ,   tf.Tensor    )    )
  self.assertEqual(batch['input_ids'].numpy().tolist()     ,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  PreTrainedTokenizerFast(tokenizer_object=snake_case    )


 @require_tokenizers
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  with tempfile.TemporaryDirectory() as tmpdirname:
   bert_tokenizer.save(os.path.join(snake_case     ,   'tokenizer.json'    )    )
   PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case     ,   'tokenizer.json'    )    )




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


a_					      = logging.get_logger(__name__)

a_					      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_					      = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[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     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		str:

  """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			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





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

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

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

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

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [self.sep_token_id]
  UpperCamelCase_      :		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    ) * [0] + len(token_ids_a + sep    ) * [1]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  assert self.add_prefix_space or not is_split_into_words, (
      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			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  assert self.add_prefix_space or not is_split_into_words, (
      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			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(snake_case     ,   add_special_tokens=snake_case    ) + [self.eos_token_id]    )

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



