






"""simple docstring"""



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_    =							get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							XLMRobertaTokenizer
							snake_case_     =							XLMRobertaTokenizerFast
							snake_case_     =							True
							snake_case_     =							True


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





													super().setUp()

													# We have a SentencePiece fixture for testing
													A__						:  Tuple		  =  XLMRobertaTokenizer(snake_case			,    keep_accents=snake_case							)
													tokenizer.save_pretrained(self.tmpdirname							)


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





													A__						:  Dict		  =  """<pad>"""
													A__						:  Union[str, Any]		  =  1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case							)			,    snake_case							)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case							)			,    snake_case							)


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





													A__						:  List[str]		  =  list(self.get_tokenizer().get_vocab().keys()							)

													self.assertEqual(vocab_keys[0]			,    """<s>"""							)
													self.assertEqual(vocab_keys[1]			,    """<pad>"""							)
													self.assertEqual(vocab_keys[-1]			,    """<mask>"""							)
													self.assertEqual(len(snake_case							)			,    1002							)


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





													self.assertEqual(self.get_tokenizer().vocab_size			,    1002							)


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





													A__						:  Dict		  =  XLMRobertaTokenizer(snake_case			,    keep_accents=snake_case							)

													A__						:  int		  =  tokenizer.tokenize("""This is a test"""							)
													self.assertListEqual(snake_case			,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(snake_case							)			,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,    )

													A__						:  Dict		  =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
													self.assertListEqual(
													    snake_case			,    [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """9""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """é""",
													        """.""",
													    ]			,    )
													A__						:  Union[str, Any]		  =  tokenizer.convert_tokens_to_ids(snake_case							)
													self.assertListEqual(
													    snake_case			,    [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]			,    )

													A__						:  List[Any]		  =  tokenizer.convert_ids_to_tokens(snake_case							)
													self.assertListEqual(
													    snake_case			,    [
													        SPIECE_UNDERLINE + """I""",
													        SPIECE_UNDERLINE + """was""",
													        SPIECE_UNDERLINE + """b""",
													        """or""",
													        """n""",
													        SPIECE_UNDERLINE + """in""",
													        SPIECE_UNDERLINE + """""",
													        """<unk>""",
													        """2""",
													        """0""",
													        """0""",
													        """0""",
													        """,""",
													        SPIECE_UNDERLINE + """and""",
													        SPIECE_UNDERLINE + """this""",
													        SPIECE_UNDERLINE + """is""",
													        SPIECE_UNDERLINE + """f""",
													        """al""",
													        """s""",
													        """<unk>""",
													        """.""",
													    ]			,    )


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





													if not self.test_slow_tokenizer:
																			# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																			return

													A__						:  str		  =  (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
																									A__						:  Optional[int]		  =  self.rust_tokenizer_class.from_pretrained(snake_case			,    **snake_case							)
																									A__						:  Dict		  =  self.tokenizer_class.from_pretrained(snake_case			,    **snake_case							)

																									A__						:  Any		  =  tempfile.mkdtemp()

																									A__						:  Union[str, Any]		  =  tokenizer_r.save_pretrained(snake_case							)
																									A__						:  Tuple		  =  tokenizer_p.save_pretrained(snake_case							)

																									# Checks it save with the same files + the tokenizer.json file for the fast one
																									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)
																									A__						:  Optional[Any]		  =  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f							)
																									self.assertSequenceEqual(snake_case			,    snake_case							)

																									# Checks everything loads correctly in the same way
																									A__						:  int		  =  tokenizer_r.from_pretrained(snake_case							)
																									A__						:  Tuple		  =  tokenizer_p.from_pretrained(snake_case							)

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(snake_case			,    snake_case							)							)
																															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																									shutil.rmtree(snake_case							)

																									# Save tokenizer rust, legacy_format=True
																									A__						:  Optional[Any]		  =  tempfile.mkdtemp()

																									A__						:  Union[str, Any]		  =  tokenizer_r.save_pretrained(snake_case			,    legacy_format=snake_case							)
																									A__						:  List[str]		  =  tokenizer_p.save_pretrained(snake_case							)

																									# Checks it save with the same files
																									self.assertSequenceEqual(snake_case			,    snake_case							)

																									# Checks everything loads correctly in the same way
																									A__						:  int		  =  tokenizer_r.from_pretrained(snake_case							)
																									A__						:  Optional[int]		  =  tokenizer_p.from_pretrained(snake_case							)

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(snake_case			,    snake_case							)							)

																									shutil.rmtree(snake_case							)

																									# Save tokenizer rust, legacy_format=False
																									A__						:  str		  =  tempfile.mkdtemp()

																									A__						:  str		  =  tokenizer_r.save_pretrained(snake_case			,    legacy_format=snake_case							)
																									A__						:  Dict		  =  tokenizer_p.save_pretrained(snake_case							)

																									# Checks it saved the tokenizer.json file
																									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files							)							)

																									# Checks everything loads correctly in the same way
																									A__						:  Tuple		  =  tokenizer_r.from_pretrained(snake_case							)
																									A__						:  Dict		  =  tokenizer_p.from_pretrained(snake_case							)

																									# Check special tokens are set accordingly on Rust and Python
																									for key in tokenizer_pp.special_tokens_map:
																															self.assertTrue(hasattr(snake_case			,    snake_case							)							)

																									shutil.rmtree(snake_case							)


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





													return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""							)


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





													with tempfile.NamedTemporaryFile() as f:
																			shutil.copyfile(snake_case			,    f.name							)
																			A__						:  Optional[Any]		  =  XLMRobertaTokenizer(f.name			,    keep_accents=snake_case							)
																			A__						:  Any		  =  pickle.dumps(snake_case							)
													pickle.loads(snake_case							)


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





													if not self.test_rust_tokenizer:
																			return

													A__						:  Tuple		  =  self.get_tokenizer()
													A__						:  Union[str, Any]		  =  self.get_rust_tokenizer()

													A__						:  Optional[int]		  =  """I was born in 92000, and this is falsé."""

													A__						:  Any		  =  tokenizer.tokenize(snake_case							)
													A__						:  Tuple		  =  rust_tokenizer.tokenize(snake_case							)
													self.assertListEqual(snake_case			,    snake_case							)

													A__						:  int		  =  tokenizer.encode(snake_case			,    add_special_tokens=snake_case							)
													A__						:  List[Any]		  =  rust_tokenizer.encode(snake_case			,    add_special_tokens=snake_case							)
													self.assertListEqual(snake_case			,    snake_case							)

													A__						:  Union[str, Any]		  =  self.get_rust_tokenizer()
													A__						:  Optional[Any]		  =  tokenizer.encode(snake_case							)
													A__						:  List[str]		  =  rust_tokenizer.encode(snake_case							)
													self.assertListEqual(snake_case			,    snake_case							)


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





													A__						:  Optional[Any]		  =  """Hello World!"""
													A__						:  Optional[int]		  =  [0, 3_5378, 6661, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(snake_case			,    self.big_tokenizer.encode(snake_case							)							)


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





													A__						:  List[str]		  =  (
													    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
													    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
													)
													A__						:  Optional[int]		  =  [
													    0,
													    3293,
													    83,
													    10,
													    4552,
													    4989,
													    7986,
													    678,
													    10,
													    5915,
													    111,
													    17_9459,
													    12_4850,
													    4,
													    6044,
													    237,
													    12,
													    6,
													    5,
													    6,
													    4,
													    6780,
													    705,
													    15,
													    1388,
													    44,
													    378,
													    1_0114,
													    711,
													    152,
													    20,
													    6,
													    5,
													    2_2376,
													    642,
													    1221,
													    1_5190,
													    3_4153,
													    450,
													    5608,
													    959,
													    1119,
													    5_7702,
													    136,
													    186,
													    47,
													    1098,
													    2_9367,
													    47,
													    # 4426, # What fairseq tokenizes from "<unk>": "_<"
													    # 3678, # What fairseq tokenizes from "<unk>": "unk"
													    # 2740, # What fairseq tokenizes from "<unk>": ">"
													    3,  # What we tokenize from "<unk>": "<unk>"
													    6,  # Residue from the tokenization: an extra sentencepiece underline
													    4,
													    6044,
													    237,
													    6284,
													    5_0901,
													    528,
													    31,
													    90,
													    34,
													    927,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(snake_case			,    self.big_tokenizer.encode(snake_case							)							)


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





													A__						:  Tuple		  =  {"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
													# fmt: on

													self.tokenizer_integration_test_util(
													    expected_encoding=snake_case			,    model_name="""xlm-roberta-base"""			,    revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""			,    )














"""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






