





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast







class 	__lowerCAmelCase      (       unittest.TestCase	):





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	mock.Mock()
      a__						:    Optional[Any]   		=	500
      a__						:    Any   		=	{}
      a__						:    Optional[Any]   		=	HTTPError
      a__						:    Any   		=	{}

      # Download this model to make sure it's in the cache.
      a__						:    str   		=	BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )

      # Under the mock environment we get a 500 error when trying to reach the tokenizer.
      with mock.patch("requests.Session.request"       ,      return_value=_lowerCamelCase   ) as mock_head:
         a__						:    Dict   		=	BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )
         # This check we did call the fake head request
         mock_head.assert_called()





   @require_tokenizers
   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    List[str]   		=	mock.Mock()
      a__						:    int   		=	500
      a__						:    int   		=	{}
      a__						:    Tuple   		=	HTTPError
      a__						:    Tuple   		=	{}

      # Download this model to make sure it's in the cache.
      a__						:    Union[str, Any]   		=	GPTaTokenizerFast.from_pretrained("gpt2"   )

      # Under the mock environment we get a 500 error when trying to reach the tokenizer.
      with mock.patch("requests.Session.request"       ,      return_value=_lowerCamelCase   ) as mock_head:
         a__						:    str   		=	GPTaTokenizerFast.from_pretrained("gpt2"   )
         # This check we did call the fake head request
         mock_head.assert_called()





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      try:
         a__						:    Optional[Any]   		=	tempfile.mktemp()
         with open(_lowerCamelCase       ,      "wb"   ) as f:
            http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       ,      _lowerCamelCase   )

         a__						:    str   		=	AlbertTokenizer.from_pretrained(_lowerCamelCase   )
      finally:
         os.remove(_lowerCamelCase   )

      # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
      # the current folder and have the right name.
      if os.path.isfile("tokenizer.json"   ):
         # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
         return
      try:
         with open("tokenizer.json"       ,      "wb"   ) as f:
            http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"       ,      _lowerCamelCase   )
         a__						:    Optional[int]   		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
         # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
         self.assertEqual(tokenizer.vocab_size       ,      1_000   )
         # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

      finally:
         os.remove("tokenizer.json"   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    int   		=	AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"   )







@is_staging_test
class 	__lowerCAmelCase      (       unittest.TestCase	):
   _UpperCamelCase			:    str  		=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]





   @classmethod
   def 						_snake_case			(			cls   )					->	List[Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	TOKEN
      HfFolder.save_token(_lowerCamelCase   )





   @classmethod
   def 						_snake_case			(			cls   )					->	List[str]:
      """simple docstring"""





      try:
         delete_repo(token=cls._token       ,      repo_id="test-tokenizer"   )
      except HTTPError:
         pass

      try:
         delete_repo(token=cls._token       ,      repo_id="valid_org/test-tokenizer-org"   )
      except HTTPError:
         pass

      try:
         delete_repo(token=cls._token       ,      repo_id="test-dynamic-tokenizer"   )
      except HTTPError:
         pass





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      with tempfile.TemporaryDirectory() as tmp_dir:
         a__						:    Union[str, Any]   		=	os.path.join(_lowerCamelCase       ,      "vocab.txt"   )
         with open(_lowerCamelCase       ,      "w"       ,      encoding="utf-8"   ) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]   )   )
         a__						:    Any   		=	BertTokenizer(_lowerCamelCase   )

      tokenizer.push_to_hub("test-tokenizer"       ,      use_auth_token=self._token   )
      a__						:    List[Any]   		=	BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""   )
      self.assertDictEqual(new_tokenizer.vocab       ,      tokenizer.vocab   )

      # Reset repo
      delete_repo(token=self._token       ,      repo_id="test-tokenizer"   )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(_lowerCamelCase       ,      repo_id="test-tokenizer"       ,      push_to_hub=_lowerCamelCase       ,      use_auth_token=self._token   )

      a__						:    Optional[Any]   		=	BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""   )
      self.assertDictEqual(new_tokenizer.vocab       ,      tokenizer.vocab   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      with tempfile.TemporaryDirectory() as tmp_dir:
         a__						:    Optional[int]   		=	os.path.join(_lowerCamelCase       ,      "vocab.txt"   )
         with open(_lowerCamelCase       ,      "w"       ,      encoding="utf-8"   ) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]   )   )
         a__						:    List[Any]   		=	BertTokenizer(_lowerCamelCase   )

      tokenizer.push_to_hub("valid_org/test-tokenizer-org"       ,      use_auth_token=self._token   )
      a__						:    Tuple   		=	BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"   )
      self.assertDictEqual(new_tokenizer.vocab       ,      tokenizer.vocab   )

      # Reset repo
      delete_repo(token=self._token       ,      repo_id="valid_org/test-tokenizer-org"   )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(
             _lowerCamelCase       ,      repo_id="valid_org/test-tokenizer-org"       ,      push_to_hub=_lowerCamelCase       ,      use_auth_token=self._token   )

      a__						:    List[str]   		=	BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"   )
      self.assertDictEqual(new_tokenizer.vocab       ,      tokenizer.vocab   )





   @require_tokenizers
   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      CustomTokenizer.register_for_auto_class()
      with tempfile.TemporaryDirectory() as tmp_dir:
         a__						:    Optional[int]   		=	os.path.join(_lowerCamelCase       ,      "vocab.txt"   )
         with open(_lowerCamelCase       ,      "w"       ,      encoding="utf-8"   ) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]   )   )
         a__						:    Union[str, Any]   		=	CustomTokenizer(_lowerCamelCase   )

      # No fast custom tokenizer
      tokenizer.push_to_hub("test-dynamic-tokenizer"       ,      use_auth_token=self._token   )

      a__						:    List[str]   		=	AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""       ,      trust_remote_code=_lowerCamelCase   )
      # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__       ,      "CustomTokenizer"   )

      # Fast and slow custom tokenizer
      CustomTokenizerFast.register_for_auto_class()
      with tempfile.TemporaryDirectory() as tmp_dir:
         a__						:    Optional[int]   		=	os.path.join(_lowerCamelCase       ,      "vocab.txt"   )
         with open(_lowerCamelCase       ,      "w"       ,      encoding="utf-8"   ) as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]   )   )

         a__						:    str   		=	BertTokenizerFast.from_pretrained(_lowerCamelCase   )
         bert_tokenizer.save_pretrained(_lowerCamelCase   )
         a__						:    Union[str, Any]   		=	CustomTokenizerFast.from_pretrained(_lowerCamelCase   )

      tokenizer.push_to_hub("test-dynamic-tokenizer"       ,      use_auth_token=self._token   )

      a__						:    Optional[int]   		=	AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""       ,      trust_remote_code=_lowerCamelCase   )
      # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__       ,      "CustomTokenizerFast"   )
      a__						:    List[str]   		=	AutoTokenizer.from_pretrained(
          F"""{USER}/test-dynamic-tokenizer"""       ,      use_fast=_lowerCamelCase       ,      trust_remote_code=_lowerCamelCase   )
      # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__       ,      "CustomTokenizer"   )







class 	__lowerCAmelCase      (       unittest.TestCase	):





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Tuple   		=	Trie()
      trie.add("Hello 友達"   )
      self.assertEqual(trie.data       ,      {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}   )
      trie.add("Hello"   )
      trie.data
      self.assertEqual(trie.data       ,      {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}   )





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	Trie()
      self.assertEqual(trie.split("[CLS] This is a extra_id_100"   )       ,      ["[CLS] This is a extra_id_100"]   )
      trie.add("[CLS]"   )
      trie.add("extra_id_1"   )
      trie.add("extra_id_100"   )
      self.assertEqual(trie.split("[CLS] This is a extra_id_100"   )       ,      ["[CLS]", " This is a ", "extra_id_100"]   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	Trie()
      trie.add("A"   )
      self.assertEqual(trie.split("ABC"   )       ,      ["A", "BC"]   )
      self.assertEqual(trie.split("BCA"   )       ,      ["BC", "A"]   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	Trie()
      trie.add("TOKEN]"   )
      trie.add("[SPECIAL_TOKEN]"   )
      self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"   )       ,      ["This is something ", "[SPECIAL_TOKEN]"]   )





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    str   		=	Trie()
      trie.add("A"   )
      trie.add("P"   )
      trie.add("[SPECIAL_TOKEN]"   )
      self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"   )       ,      ["This is something ", "[SPECIAL_TOKEN]"]   )





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	Trie()
      trie.add("AB"   )
      trie.add("B"   )
      trie.add("C"   )
      self.assertEqual(trie.split("ABC"   )       ,      ["AB", "C"]   )





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    str   		=	Trie()
      trie.add("ABC"   )
      trie.add("B"   )
      trie.add("CD"   )
      self.assertEqual(trie.split("ABCD"   )       ,      ["ABC", "D"]   )





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    List[Any]   		=	Trie()
      a__						:    Dict   		=	trie.cut_text("ABC"       ,      [0, 0, 2, 1, 2, 3]   )
      self.assertEqual(_lowerCamelCase       ,      ["AB", "C"]   )



"""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

_lowercase							=				logging.get_logger(__name__)


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

_lowercase							=				{
    '''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'''
    },
}

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



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



def      lowerCAmelCase__			(		__magic_name__      )						->Tuple:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			Tuple          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Dict          =   ["""input_ids""", """attention_mask"""]
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="replace"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else bos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else eos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else sep_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else cls_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else unk_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else pad_token

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

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

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

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										__lowercase       =       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   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

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

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       " ".join(_lowerCamelCase       )
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       []
										for token in re.findall(self.pat  ,    _lowerCamelCase       ):
													__lowercase       =       "".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(_lowerCamelCase       ).split(" "       )       )
										return bpe_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       )
										__lowercase       =       bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"  ,    errors=self.errors       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

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

										__lowercase       =       0
										with open(_lowerCamelCase  ,    "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 _lowerCamelCase       : 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!"       )
																			__lowercase       =       token_index
																writer.write(" ".join(_lowerCamelCase       ) + "\n"       )
																index += 1

										return vocab_file, merge_file
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCamelCase  ,    token_ids_a=_lowerCamelCase  ,    already_has_special_tokens=_lowerCamelCase       )

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       kwargs.pop("add_prefix_space"  ,    self.add_prefix_space       )
										if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase       ) > 0 and not text[0].isspace()):
													__lowercase       =       " " + text
										return (text, kwargs)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple:




										'''simple docstring'''
										return token_ids_a + [self.eos_token_id]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       []
										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(_lowerCamelCase       )

										__lowercase       =       "  ".join(_lowerCamelCase       )
										__lowercase       =       self.encode(_lowerCamelCase       )
										if len(_lowerCamelCase       ) > self.model_max_length:
													__lowercase       =       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





