





'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import 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 numpy
      import tensorflow as tf

      from transformers import (
          TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BertConfig,
          DPRConfig,
          TFDPRContextEncoder,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )







class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Optional[int]=13							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					Optional[int]=True							,	UpperCamelCase__    :					Dict=True							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					List[Any]=99							,	UpperCamelCase__    :					Optional[Any]=32							,	UpperCamelCase__    :					Optional[int]=2							,	UpperCamelCase__    :					int=4							,	UpperCamelCase__    :					Tuple=37							,	UpperCamelCase__    :					Tuple="gelu"							,	UpperCamelCase__    :					int=0.1							,	UpperCamelCase__    :					Optional[Any]=0.1							,	UpperCamelCase__    :					Optional[Any]=512							,	UpperCamelCase__    :					Union[str, Any]=16							,	UpperCamelCase__    :					Dict=2							,	UpperCamelCase__    :					int=0.02							,	UpperCamelCase__    :					Tuple=3							,	UpperCamelCase__    :					Dict=4							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Dict=0							,	)     ->						Any:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=seq_length
    _UpperCamelCase       			=is_training
    _UpperCamelCase       			=use_input_mask
    _UpperCamelCase       			=use_token_type_ids
    _UpperCamelCase       			=use_labels
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=max_position_embeddings
    _UpperCamelCase       			=type_vocab_size
    _UpperCamelCase       			=type_sequence_label_size
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=num_labels
    _UpperCamelCase       			=num_choices
    _UpperCamelCase       			=scope
    _UpperCamelCase       			=projection_dim


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[Any]:
    _UpperCamelCase       			=ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size  )

    _UpperCamelCase       			=None
    if self.use_input_mask:
      # follow test_modeling_tf_ctrl.py
      _UpperCamelCase       			=random_attention_mask([self.batch_size, self.seq_length]  )

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

    _UpperCamelCase       			=None
    _UpperCamelCase       			=None
    _UpperCamelCase       			=None
    if self.use_labels:
      _UpperCamelCase       			=ids_tensor([self.batch_size]							,	self.type_sequence_label_size  )
      _UpperCamelCase       			=ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels  )
      _UpperCamelCase       			=ids_tensor([self.batch_size]							,	self.num_choices  )

    _UpperCamelCase       			=BertConfig(
        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							,	is_decoder=UpperCamelCase__							,	initializer_range=self.initializer_range							,	)
    _UpperCamelCase       			=DPRConfig(projection_dim=self.projection_dim							,	**config.to_dict()  )

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


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=TFDPRContextEncoder(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	token_type_ids=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	token_type_ids=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__  )
    self.parent.assertEqual(result.pooler_output.shape							,	(self.batch_size, self.projection_dim or self.hidden_size)  )


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					List[Any]  )     ->						int:
    _UpperCamelCase       			=TFDPRQuestionEncoder(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	token_type_ids=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	token_type_ids=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__  )
    self.parent.assertEqual(result.pooler_output.shape							,	(self.batch_size, self.projection_dim or self.hidden_size)  )


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Optional[int]  )     ->						Dict:
    _UpperCamelCase       			=TFDPRReader(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=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)  )
    self.parent.assertEqual(result.relevance_logits.shape							,	(self.batch_size,)  )




  def   UpperCamelCase__      (				self    :					List[str]  )     ->						List[Any]:
    _UpperCamelCase       			=self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     (
        _UpperCamelCase
    )			,     
    )       			=config_and_inputs
    _UpperCamelCase       			={'''input_ids''': input_ids}
    return config, inputs_dict







@require_tf
class 		UpperCAmelCase   (				lowercase_   ,     lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				(
      (
          TFDPRContextEncoder,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )
      if is_tf_available()
      else ()
  )
  lowerCAmelCase_					=				{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=TFDPRModelTester(self  )
    _UpperCamelCase       			=ConfigTester(self							,	config_class=UpperCamelCase__							,	hidden_size=37  )


  def   UpperCamelCase__      (				self    :					str  )     ->						Dict:
    self.config_tester.run_common_tests()


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Optional[int]:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_context_encoder(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Tuple:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_question_encoder(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_reader(*UpperCamelCase__  )




  @slow
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _UpperCamelCase       			=TFDPRContextEncoder.from_pretrained(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )

    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _UpperCamelCase       			=TFDPRContextEncoder.from_pretrained(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )

    for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _UpperCamelCase       			=TFDPRQuestionEncoder.from_pretrained(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )

    for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _UpperCamelCase       			=TFDPRReader.from_pretrained(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )







@require_tf
class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  @slow
  def   UpperCamelCase__      (				self    :					Any  )     ->						Any:
    _UpperCamelCase       			=TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''  )

    _UpperCamelCase       			=tf.constant(
        [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]  )  # [CLS] hello, is my dog cute? [SEP]
    _UpperCamelCase       			=model(UpperCamelCase__  )[0]  # embedding shape = (1, 768)
    # compare the actual values for a slice.
    _UpperCamelCase       			=tf.constant(
        [
            [
                0.03236253,
                0.12753335,
                0.16818509,
                0.00279786,
                0.3896933,
                0.24264945,
                0.2178971,
                -0.02335227,
                -0.08481959,
                -0.14324117,
            ]
        ]  )
    self.assertTrue(numpy.allclose(output[:, :10].numpy()							,	expected_slice.numpy()							,	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

__lowerCamelCase						: Optional[int]		=   logging.get_logger(__name__)


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

__lowerCamelCase						: Any		=   {
    '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'
    },
}

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


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=(
      list(range(ord('''!'''   )  ,      ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   )  ,      ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   )  ,      ord('''ÿ'''   ) + 1   )   )
  )
  _UpperCamelCase       			=bs[:]
  _UpperCamelCase       			=0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(__SCREAMING_SNAKE_CASE   )
      cs.append(2**8 + n   )
      n += 1
  _UpperCamelCase       			=[chr(__SCREAMING_SNAKE_CASE   ) for n in cs]
  return dict(zip(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=set()
  _UpperCamelCase       			=word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    _UpperCamelCase       			=char
  return pairs







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]


  def __init__(				self    :					str							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[Any]="replace"							,	UpperCamelCase__    :					Dict="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Union[str, Any]="</s>"							,	UpperCamelCase__    :					Tuple="<s>"							,	UpperCamelCase__    :					int="<unk>"							,	UpperCamelCase__    :					int="<pad>"							,	UpperCamelCase__    :					Optional[int]="<mask>"							,	UpperCamelCase__    :					List[str]=False							,	**UpperCamelCase__    :					Tuple							,	)     ->						Optional[int]:
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else bos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else eos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else sep_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else cls_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else unk_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else pad_token

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

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

    with open(UpperCamelCase__							,	encoding='''utf-8'''  ) as vocab_handle:
      _UpperCamelCase       			=json.load(UpperCamelCase__  )
    _UpperCamelCase       			={v: k for k, v in self.encoder.items()}
    _UpperCamelCase       			=errors  # how to handle errors in decoding
    _UpperCamelCase       			=bytes_to_unicode()
    _UpperCamelCase       			={v: k for k, v in self.byte_encoder.items()}
    with open(UpperCamelCase__							,	encoding='''utf-8'''  ) as merges_handle:
      _UpperCamelCase       			=merges_handle.read().split('''\n'''  )[1:-1]
    _UpperCamelCase       			=[tuple(merge.split()  ) for merge in bpe_merges]
    _UpperCamelCase       			=dict(zip(UpperCamelCase__							,	range(len(UpperCamelCase__  )  )  )  )
    _UpperCamelCase       			={}
    _UpperCamelCase       			=add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    _UpperCamelCase       			=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    :					Tuple  )     ->						List[Any]:
    return len(self.encoder  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Tuple:
    return dict(self.encoder							,	**self.added_tokens_encoder  )


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[str]  )     ->						Dict:
    if token in self.cache:
      return self.cache[token]
    _UpperCamelCase       			=tuple(UpperCamelCase__  )
    _UpperCamelCase       			=get_pairs(UpperCamelCase__  )

    if not pairs:
      return token

    while True:
      _UpperCamelCase       			=min(UpperCamelCase__							,	key=lambda UpperCamelCase__  : self.bpe_ranks.get(UpperCamelCase__							,	float('''inf'''  )  )  )
      if bigram not in self.bpe_ranks:
        break
      _UpperCamelCase			,     _UpperCamelCase       			=bigram
      _UpperCamelCase       			=[]
      _UpperCamelCase       			=0
      while i < len(UpperCamelCase__  ):
        try:
          _UpperCamelCase       			=word.index(UpperCamelCase__							,	UpperCamelCase__  )
        except ValueError:
          new_word.extend(word[i:]  )
          break
        else:
          new_word.extend(word[i:j]  )
          _UpperCamelCase       			=j

        if word[i] == first and i < len(UpperCamelCase__  ) - 1 and word[i + 1] == second:
          new_word.append(first + second  )
          i += 2
        else:
          new_word.append(word[i]  )
          i += 1
      _UpperCamelCase       			=tuple(UpperCamelCase__  )
      _UpperCamelCase       			=new_word
      if len(UpperCamelCase__  ) == 1:
        break
      else:
        _UpperCamelCase       			=get_pairs(UpperCamelCase__  )
    _UpperCamelCase       			=''' '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=word
    return word


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=[]
    for token in re.findall(self.pat							,	UpperCamelCase__  ):
      _UpperCamelCase       			=''''''.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(UpperCamelCase__  ).split(''' '''  )  )
    return bpe_tokens


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Optional[int]  )     ->						int:
    return self.encoder.get(UpperCamelCase__							,	self.encoder.get(self.unk_token  )  )


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Optional[Any]  )     ->						List[str]:
    return self.decoder.get(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					int  )     ->						List[Any]:
    _UpperCamelCase       			=''''''.join(UpperCamelCase__  )
    _UpperCamelCase       			=bytearray([self.byte_decoder[c] for c in text]  ).decode('''utf-8'''							,	errors=self.errors  )
    return text


  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    if not os.path.isdir(UpperCamelCase__  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

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

    _UpperCamelCase       			=0
    with open(UpperCamelCase__							,	'''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 UpperCamelCase__  : 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       			=token_index
        writer.write(''' '''.join(UpperCamelCase__  ) + '''\n'''  )
        index += 1

    return vocab_file, merge_file


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None							,	UpperCamelCase__    :					bool = False  )     ->						List[int]:
    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__							,	token_ids_a=UpperCamelCase__							,	already_has_special_tokens=UpperCamelCase__  )

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


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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    :					Optional[Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str=False							,	**UpperCamelCase__    :					Union[str, Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=kwargs.pop('''add_prefix_space'''							,	self.add_prefix_space  )
    if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__  ) > 0 and not text[0].isspace()):
      _UpperCamelCase       			=''' ''' + text
    return (text, kwargs)


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						Any:
    return token_ids_a + [self.eos_token_id]




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					"Conversation"  )     ->						List[int]:
    _UpperCamelCase       			=[]
    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(UpperCamelCase__  )

    _UpperCamelCase       			='''  '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=self.encode(UpperCamelCase__  )
    if len(UpperCamelCase__  ) > self.model_max_length:
      _UpperCamelCase       			=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






