






'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCAmelCase__						:Union[str, Any]          =			logging.get_logger(__name__)


UpperCAmelCase__						:Optional[Any]          =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCAmelCase__						:int          =			{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase__						:int          =			{
    """allenai/led-base-16384""": 16_384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __lowercase  ()   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				(
         list(range(ord("""!"""     ),						ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     ),						ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     ),						ord("""ÿ"""     ) + 1     )     )
     )
     __lowerCamelCase						:       Dict				   =				bs[:]
     __lowerCamelCase						:       Optional[Any]				   =				0
     for b in range(2**8     ):
          if b not in bs:
               bs.append(_lowercase     )
               cs.append(2**8 + n     )
               n += 1
     __lowerCamelCase						:       Union[str, Any]				   =				[chr(_lowercase     ) for n in cs]
     return dict(zip(_lowercase,						_lowercase     )     )
def     __lowercase  (_lowercase     )   ->     Dict:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				set()
     __lowerCamelCase						:       int				   =				word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          __lowerCamelCase						:       Union[str, Any]				   =				char
     return pairs







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[Any]   		=			VOCAB_FILES_NAMES
       snake_case__    :     Any   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     List[Any]   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Optional[Any]   		=			['input_ids', 'attention_mask']




       def __init__(					self      : Optional[int]	,    A__      : Tuple	,    A__      : Union[str, Any]	,    A__      : Any="replace"	,    A__      : int="<s>"	,    A__      : Optional[int]="</s>"	,    A__      : str="</s>"	,    A__      : Union[str, Any]="<s>"	,    A__      : int="<unk>"	,    A__      : Optional[Any]="<pad>"	,    A__      : Union[str, Any]="<mask>"	,    A__      : str=False	,    **A__      : Optional[int]	,    ):


            """simple docstring"""







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

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

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

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

            # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
            __lowerCamelCase						:       List[str]				   =				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.bart.tokenization_bart.BartTokenizer.vocab_size
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return len(self.encoder					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : int	,    A__      : List[str]					):


            """simple docstring"""







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

            if not pairs:
                 return token

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

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




       def 					a_					(					self      : Optional[Any]	,    A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Dict	,    A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    A__      : Optional[int]					):


            """simple docstring"""







            return self.decoder.get(A__					)




       def 					a_					(					self      : Optional[int]	,    A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[Any]	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







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

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

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

            return vocab_file, merge_file




       def 					a_					(					self      : str	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __lowerCamelCase						:       Union[str, Any]				   =				[self.cls_token_id]
            __lowerCamelCase						:       Optional[int]				   =				[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					a_					(					self      : Dict	,    A__      : List[int]	,    A__      : Optional[List[int]] = None	,    A__      : bool = False					):


            """simple docstring"""







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

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




       def 					a_					(					self      : Tuple	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				[self.sep_token_id]
            __lowerCamelCase						:       str				   =				[self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]




       def 					a_					(					self      : Any	,    A__      : int	,    A__      : int=False	,    **A__      : Optional[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]	,    A__      : Union[Dict[str, EncodedInput], BatchEncoding]	,    A__      : Optional[int] = None	,    A__      : PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,    A__      : Optional[int] = None	,    A__      : Optional[bool] = None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				super()._pad(
                encoded_inputs=A__	,    max_length=A__	,    padding_strategy=A__	,    pad_to_multiple_of=A__	,    return_attention_mask=A__	,    )

            # Load from model defaults
            if return_attention_mask is None:
                 __lowerCamelCase						:       str				   =				"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                 __lowerCamelCase						:       Optional[int]				   =				encoded_inputs[self.model_input_names[0]]
                 # `global_attention_mask` need to have the same length as other (sequential) inputs.
                 __lowerCamelCase						:       List[Any]				   =				len(encoded_inputs["""global_attention_mask"""]					) != len(A__					)

                 if needs_to_be_padded:
                      __lowerCamelCase						:       List[Any]				   =				len(A__					) - len(encoded_inputs["""global_attention_mask"""]					)

                      if self.padding_side == "right":
                           # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                           __lowerCamelCase						:       Union[str, Any]				   =				(
                               encoded_inputs["""global_attention_mask"""] + [-1] * difference
                           )
                      elif self.padding_side == "left":
                           __lowerCamelCase						:       int				   =				[-1] * difference + encoded_inputs[
                               """global_attention_mask"""
                           ]
                      else:
                           raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs











'''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


UpperCAmelCase__						:List[str]          =			get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Optional[Any]   		=			XLMRobertaTokenizer
       snake_case__    :     str   		=			XLMRobertaTokenizerFast
       snake_case__    :     str   		=			True
       snake_case__    :     int   		=			True




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            super().setUp()

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




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""<pad>"""
            __lowerCamelCase						:       List[Any]				   =				1

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




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       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(A__					)	,    1002					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				XLMRobertaTokenizer(A__	,    keep_accents=A__					)

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

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

            __lowerCamelCase						:       Any				   =				tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
            self.assertListEqual(
                A__	,    [
                    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""",
                    """.""",
                ]	,    )
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_tokens_to_ids(A__					)
            self.assertListEqual(
                A__	,    [
                    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 ^
                ]	,    )

            __lowerCamelCase						:       Dict				   =				tokenizer.convert_ids_to_tokens(A__					)
            self.assertListEqual(
                A__	,    [
                    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 					a_					(					self      : 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

            __lowerCamelCase						:       Tuple				   =				(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})"					):
                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)

                      __lowerCamelCase						:       str				   =				tempfile.mkdtemp()

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

                      # 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					)					)
                      __lowerCamelCase						:       Any				   =				tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f					)
                      self.assertSequenceEqual(A__	,    A__					)

                      # Checks everything loads correctly in the same way
                      __lowerCamelCase						:       str				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_p.from_pretrained(A__					)

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

                      shutil.rmtree(A__					)

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

                      __lowerCamelCase						:       Dict				   =				tokenizer_r.save_pretrained(A__	,    legacy_format=A__					)
                      __lowerCamelCase						:       List[Any]				   =				tokenizer_p.save_pretrained(A__					)

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

                      # Checks everything loads correctly in the same way
                      __lowerCamelCase						:       Any				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.from_pretrained(A__					)

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

                      shutil.rmtree(A__					)

                      # Save tokenizer rust, legacy_format=False
                      __lowerCamelCase						:       int				   =				tempfile.mkdtemp()

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

                      # 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
                      __lowerCamelCase						:       List[Any]				   =				tokenizer_r.from_pretrained(A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_p.from_pretrained(A__					)

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

                      shutil.rmtree(A__					)




       @cached_property
       def 					a_					(					self      : str					):


            """simple docstring"""







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




       def 					a_					(					self      : int					):


            """simple docstring"""







            with tempfile.NamedTemporaryFile() as f:
                 shutil.copyfile(A__	,    f.name					)
                 __lowerCamelCase						:       int				   =				XLMRobertaTokenizer(f.name	,    keep_accents=A__					)
                 __lowerCamelCase						:       str				   =				pickle.dumps(A__					)
            pickle.loads(A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            if not self.test_rust_tokenizer:
                 return

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

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

            __lowerCamelCase						:       int				   =				tokenizer.tokenize(A__					)
            __lowerCamelCase						:       Optional[Any]				   =				rust_tokenizer.tokenize(A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				rust_tokenizer.encode(A__	,    add_special_tokens=A__					)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       Optional[Any]				   =				self.get_rust_tokenizer()
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				rust_tokenizer.encode(A__					)
            self.assertListEqual(A__	,    A__					)




       @slow
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""Hello World!"""
            __lowerCamelCase						:       str				   =				[0, 35378, 6661, 38, 2]
            # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
            # xlmr.eval()
            # xlmr.encode(symbols)

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




       @slow
       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				(
                """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"""
            )
            __lowerCamelCase						:       Optional[Any]				   =				[
                0,
                3293,
                83,
                10,
                4552,
                4989,
                7986,
                678,
                10,
                5915,
                111,
                179459,
                124850,
                4,
                6044,
                237,
                12,
                6,
                5,
                6,
                4,
                6780,
                705,
                15,
                1388,
                44,
                378,
                10114,
                711,
                152,
                20,
                6,
                5,
                22376,
                642,
                1221,
                15190,
                34153,
                450,
                5608,
                959,
                1119,
                57702,
                136,
                186,
                47,
                1098,
                29367,
                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,
                50901,
                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(A__	,    self.big_tokenizer.encode(A__					)					)




       @slow
       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				{"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=A__	,    model_name="""xlm-roberta-base"""	,    revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""	,    )



