






"""simple docstring"""



from __future__ import annotations

import unittest

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

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						List[str]			,    snake_case      :						Any			,    snake_case      :						Tuple=13			,    snake_case      :						Union[str, Any]=7			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						Tuple=True			,    snake_case      :						Any=True			,    snake_case      :						Dict=True			,    snake_case      :						Optional[int]=99			,    snake_case      :						int=32			,    snake_case      :						List[Any]=2			,    snake_case      :						Optional[Any]=4			,    snake_case      :						Optional[Any]=37			,    snake_case      :						int="gelu"			,    snake_case      :						Any=0.1			,    snake_case      :						str=0.1			,    snake_case      :						Tuple=512			,    snake_case      :						List[Any]=16			,    snake_case      :						Union[str, Any]=2			,    snake_case      :						Union[str, Any]=0.02			,    snake_case      :						int=False			,    snake_case      :						Any=True			,    snake_case      :						int="None"			,    snake_case      :						Optional[Any]=3			,    snake_case      :						str=4			,    snake_case      :						Tuple=None			,    ):
													'''simple docstring'''





													A__						:  Tuple		  =  parent
													A__						:  int		  =  batch_size
													A__						:  Union[str, Any]		  =  seq_length
													A__						:  Union[str, Any]		  =  is_training
													A__						:  Tuple		  =  use_input_mask
													A__						:  Union[str, Any]		  =  use_token_type_ids
													A__						:  Any		  =  use_labels
													A__						:  List[str]		  =  vocab_size
													A__						:  Any		  =  hidden_size
													A__						:  int		  =  num_hidden_layers
													A__						:  str		  =  num_attention_heads
													A__						:  Any		  =  intermediate_size
													A__						:  List[str]		  =  hidden_act
													A__						:  str		  =  hidden_dropout_prob
													A__						:  int		  =  attention_probs_dropout_prob
													A__						:  Tuple		  =  max_position_embeddings
													A__						:  Dict		  =  type_vocab_size
													A__						:  str		  =  type_sequence_label_size
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  Union[str, Any]		  =  num_labels
													A__						:  Optional[Any]		  =  num_choices
													A__						:  str		  =  relative_attention
													A__						:  List[str]		  =  position_biased_input
													A__						:  Tuple		  =  pos_att_type
													A__						:  Optional[Any]		  =  scope


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size							)

													A__						:  Any		  =  None
													if self.use_input_mask:
																			A__						:  int		  =  random_attention_mask([self.batch_size, self.seq_length]							)

													A__						:  List[Any]		  =  None
													if self.use_token_type_ids:
																			A__						:  List[str]		  =  ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size							)

													A__						:  Tuple		  =  None
													A__						:  int		  =  None
													A__						:  Tuple		  =  None
													if self.use_labels:
																			A__						:  Any		  =  ids_tensor([self.batch_size]			,    self.type_sequence_label_size							)
																			A__						:  str		  =  ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels							)

													A__						:  List[Any]		  =  DebertaVaConfig(
													    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			,    relative_attention=self.relative_attention			,    position_biased_input=self.position_biased_input			,    initializer_range=self.initializer_range			,    return_dict=__UpperCamelCase			,    )

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


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Dict			,    snake_case      :						List[str]			,    snake_case      :						str			,    snake_case      :						List[str]			,    snake_case      :						Optional[Any]			,    snake_case      :						int			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  TFDebertaVaModel(config=__UpperCamelCase							)
													A__						:  Union[str, Any]		  =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

													A__						:  List[Any]		  =  [input_ids, input_mask]
													A__						:  Optional[Any]		  =  model(__UpperCamelCase							)

													A__						:  Union[str, Any]		  =  model(__UpperCamelCase							)

													self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						int			,    snake_case      :						Tuple			,    snake_case      :						List[str]			,    snake_case      :						List[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						int			,    snake_case      :						str							):
													'''simple docstring'''





													A__						:  int		  =  TFDebertaVaForMaskedLM(config=__UpperCamelCase							)
													A__						:  Optional[Any]		  =  {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}
													A__						:  Tuple		  =  model(__UpperCamelCase							)
													self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[str, Any]			,    snake_case      :						List[Any]			,    snake_case      :						Tuple			,    snake_case      :						Optional[Any]			,    snake_case      :						Tuple			,    snake_case      :						str			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  str		  =  self.num_labels
													A__						:  Union[str, Any]		  =  TFDebertaVaForSequenceClassification(config=__UpperCamelCase							)
													A__						:  Dict		  =  {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}

													A__						:  str		  =  model(__UpperCamelCase							)
													self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Optional[int]			,    snake_case      :						Optional[int]			,    snake_case      :						List[str]			,    snake_case      :						str			,    snake_case      :						Any			,    snake_case      :						List[Any]			,    snake_case      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.num_labels
													A__						:  int		  =  TFDebertaVaForTokenClassification(config=__UpperCamelCase							)
													A__						:  Union[str, Any]		  =  {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}
													A__						:  List[Any]		  =  model(__UpperCamelCase							)
													self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						int			,    snake_case      :						str			,    snake_case      :						Dict			,    snake_case      :						Optional[int]			,    snake_case      :						Dict			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  TFDebertaVaForQuestionAnswering(config=__UpperCamelCase							)
													A__						:  Tuple		  =  {
													    """input_ids""": input_ids,
													    """attention_mask""": input_mask,
													    """token_type_ids""": token_type_ids,
													}

													A__						:  int		  =  model(__UpperCamelCase							)
													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      :						str							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  self.prepare_config_and_inputs()
													(
													    (
													    A__
													)  ,  (
													    A__
													)  ,  (
													    A__
													)  ,  (
													    A__
													)  ,  (
													    A__
													)  ,  (
													    A__
													)  ,  (
													    A__
													)  ,  
													)						:  Tuple		  =  config_and_inputs
													A__						:  List[str]		  =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
													return config, inputs_dict






@require_tf
class 							__SCREAMING_SNAKE_CASE						(				_UpperCAmelCase       ,		_UpperCAmelCase       ,		unittest.TestCase		):
							snake_case_     =							(
							    (
							        TFDebertaVaModel,
							        TFDebertaVaForMaskedLM,
							        TFDebertaVaForQuestionAnswering,
							        TFDebertaVaForSequenceClassification,
							        TFDebertaVaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							snake_case_     =							(
							    {
							        'feature-extraction': TFDebertaVaModel,
							        'fill-mask': TFDebertaVaForMaskedLM,
							        'question-answering': TFDebertaVaForQuestionAnswering,
							        'text-classification': TFDebertaVaForSequenceClassification,
							        'token-classification': TFDebertaVaForTokenClassification,
							        'zero-shot': TFDebertaVaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							snake_case_     =							False
							snake_case_     =							False


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  TFDebertaVaModelTester(self							)
													A__						:  Union[str, Any]		  =  ConfigTester(self			,    config_class=__UpperCamelCase			,    hidden_size=37							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													self.config_tester.run_common_tests()


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCamelCase							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Tuple		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  str		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Dict		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase							)


							@slow
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""							)
													self.assertIsNotNone(__UpperCamelCase							)






@require_tf
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@unittest.skip(reason="""Model not available yet"""							)
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													pass


							@slow
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  str		  =  TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""							)
													A__						:  str		  =  tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]							)
													A__						:  Tuple		  =  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
													A__						:  List[str]		  =  model(__UpperCamelCase			,    attention_mask=__UpperCamelCase							)[0]

													A__						:  List[str]		  =  tf.constant(
													    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]							)
													tf.debugging.assert_near(output[:, 1:4, 1:4]			,    __UpperCamelCase			,    atol=1e-4							)














"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A_    =							logging.get_logger(__name__)


A_    =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A_    =							{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

A_    =							{'''facebook/blenderbot-3B''': 128}




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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->Tuple:
						A__						:  Any		  =  set()
						A__						:  Optional[int]		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  Dict		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						int			,    snake_case      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						str="replace"			,    snake_case      :						Tuple="<s>"			,    snake_case      :						Dict="</s>"			,    snake_case      :						Optional[Any]="</s>"			,    snake_case      :						int="<s>"			,    snake_case      :						Dict="<unk>"			,    snake_case      :						List[Any]="<pad>"			,    snake_case      :						Union[str, Any]="<mask>"			,    snake_case      :						Optional[Any]=False			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





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

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    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			,    )

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

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Optional[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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  Tuple		  =  tuple(snake_case							)
													A__						:  Union[str, Any]		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  Any		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  Optional[int]		  =  bigram
																			A__						:  Tuple		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  Optional[Any]		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Optional[int]		  =  tuple(snake_case							)
																			A__						:  str		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  List[str]		  =  get_pairs(snake_case							)
													A__						:  int		  =  """ """.join(snake_case							)
													A__						:  Optional[int]		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  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(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  """""".join(snake_case							)
													A__						:  Dict		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





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

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

													A__						:  str		  =  0
													with open(snake_case			,    """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 snake_case							: 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!"""							)
																															A__						:  List[str]		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





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

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


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  [self.sep_token_id]
													A__						:  Optional[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      :						List[Any]			,    snake_case      :						Optional[int]			,    snake_case      :						List[Any]=False			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  int		  =  """ """ + text
													return (text, kwargs)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													return token_ids_a + [self.eos_token_id]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						"Conversation"							):
													'''simple docstring'''





													A__						:  str		  =  []
													for is_user, text in conversation.iter_texts():
																			if is_user:
																									# We need to space prefix as it's being done within blenderbot
																									inputs.append(""" """ + text							)
																			else:
																									# Generated responses should contain them already.
																									inputs.append(snake_case							)

													A__						:  Tuple		  =  """  """.join(snake_case							)
													A__						:  Dict		  =  self.encode(snake_case							)
													if len(snake_case							) > self.model_max_length:
																			A__						:  Optional[int]		  =  input_ids[-self.model_max_length :]
																			logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'							)
													return input_ids






