






'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			RobertaTokenizer
       snake_case__    :     Optional[int]   		=			RobertaTokenizerFast
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     Tuple   		=			{'cls_token': '<s>'}




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


            """simple docstring"""







            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __lowerCamelCase						:       Tuple				   =				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            __lowerCamelCase						:       Tuple				   =				dict(zip(A__	,    range(len(A__					)					)					)					)
            __lowerCamelCase						:       List[str]				   =				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __lowerCamelCase						:       Tuple				   =				{"""unk_token""": """<unk>"""}

            __lowerCamelCase						:       Optional[int]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]					)
            __lowerCamelCase						:       Union[str, Any]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""merges_file"""]					)
            with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write(json.dumps(A__					) + """\n"""					)
            with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write("""\n""".join(A__					)					)




       def 					a_					(					self      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **A__					)




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


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : Tuple	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""lower newer"""
            __lowerCamelCase						:       int				   =				"""lower newer"""
            return input_text, output_text




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


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.tokenizer_class(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map					)
            __lowerCamelCase						:       Any				   =				"""lower newer"""
            __lowerCamelCase						:       Union[str, Any]				   =				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.tokenize(A__					)  # , add_prefix_space=True)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       List[str]				   =				tokens + [tokenizer.unk_token]
            __lowerCamelCase						:       str				   =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,    A__					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,    )




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode("""sequence builders"""	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				tokenizer.encode("""multi-sequence build"""	,    add_special_tokens=A__					)

            __lowerCamelCase						:       Dict				   =				tokenizer.encode(
                """sequence builders"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.encode(
                """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.build_inputs_with_special_tokens(A__					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.build_inputs_with_special_tokens(A__	,    A__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_tokenizer()

            __lowerCamelCase						:       str				   =				"""Encode this sequence."""
            __lowerCamelCase						:       str				   =				tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

            # Testing encoder arguments
            __lowerCamelCase						:       Union[str, Any]				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(A__	,    A__					)

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(A__	,    A__					)

            # Testing spaces after special tokens
            __lowerCamelCase						:       str				   =				"""<mask>"""
            tokenizer.add_special_tokens(
                {"""mask_token""": AddedToken(A__	,    lstrip=A__	,    rstrip=A__					)}					)  # mask token has a left space
            __lowerCamelCase						:       str				   =				tokenizer.convert_tokens_to_ids(A__					)

            __lowerCamelCase						:       str				   =				"""Encode <mask> sequence"""
            __lowerCamelCase						:       Optional[int]				   =				"""Encode <mask>sequence"""

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				encoded.index(A__					)
            __lowerCamelCase						:       Any				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				encoded.index(A__					)
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(A__	,    A__					)




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


            """simple docstring"""







            pass




       def 					a_					(					self      : str					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				"""A, <mask> AllenNLP sentence."""
                      __lowerCamelCase						:       int				   =				tokenizer_r.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]					)	,    sum(tokens_p["""token_type_ids"""]					)					)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)	,    sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)	,    )

                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                      __lowerCamelCase						:       Any				   =				tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2					):
                 __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowerCamelCase						:       Dict				   =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]	,    A__					)

                 self.assertEqual(post_processor_state["""add_prefix_space"""]	,    A__					)
                 self.assertEqual(post_processor_state["""trim_offsets"""]	,    A__					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowerCamelCase						:       Optional[Any]				   =				f"{text_of_1_token} {text_of_1_token}"

                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       int				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Tuple				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       str				   =				f" {text}"

                      # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                      # )
                      # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                      # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                      # self.assertEqual(
                      #     encoding.offset_mapping[1],
                      #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                      # )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (1, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					) + 1, 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Dict				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Dict				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )











'''simple docstring'''

import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


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

UpperCAmelCase__						:Dict          =			{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

UpperCAmelCase__						:List[Any]          =			{
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

UpperCAmelCase__						:Optional[Any]          =			{
    """abeja/gpt-neox-japanese-2.7b""": 2_048,
}
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Dict				   =				json.loads(f.read()     )

     __lowerCamelCase						:       Dict				   =				collections.OrderedDict()
     __lowerCamelCase						:       int				   =				collections.OrderedDict()
     __lowerCamelCase						:       List[Any]				   =				collections.OrderedDict()
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Union[str, Any]				   =				f.readlines()
     __lowerCamelCase						:       Any				   =				[[t.rstrip("""\n"""     )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""     ).split(""","""     ) for t in token]
     for idx, b in enumerate(_lowercase     ):
          __lowerCamelCase						:       str				   =				b
          __lowerCamelCase						:       List[str]				   =				idx
          for wd in b:
               __lowerCamelCase						:       Optional[Any]				   =				idx

     return vocab, raw_vocab, ids_to_tokens, emoji







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




       def __init__(					self      : Tuple	,    A__      : int	,    A__      : List[str]	,    A__      : Any="<|endoftext|>"	,    A__      : Tuple="<|endoftext|>"	,    A__      : Dict="<|startoftext|>"	,    A__      : Dict="<|endoftext|>"	,    A__      : List[str]=False	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            super().__init__(
                unk_token=A__	,    pad_token=A__	,    bos_token=A__	,    eos_token=A__	,    do_clean_text=A__	,    **A__	,    )
            if not os.path.isfile(A__					):
                 raise ValueError(
                     f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
                     """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
            if not os.path.isfile(A__					):
                 raise ValueError(
                     f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
                     """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
            __lowerCamelCase						:       Tuple				   =				do_clean_text
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				load_vocab_and_emoji(A__	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				SubWordJapaneseTokenizer(
                vocab=self.vocab	,    ids_to_tokens=self.ids_to_tokens	,    emoji=self.emoji					)




       @property
       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            return len(self.raw_vocab					)




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


            """simple docstring"""







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




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


            """simple docstring"""







            return self.subword_tokenizer.tokenize(A__	,    clean=self.do_clean_text					)




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


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            return self.subword_tokenizer.convert_id_to_token(A__					)




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


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""""".join(A__					).strip()
            return out_string




       def 					a_					(					self      : str	,    A__      : "Conversation"					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(A__	,    add_special_tokens=A__					) + [self.eos_token_id]					)

            if len(A__					) > self.model_max_length:
                 __lowerCamelCase						:       str				   =				input_ids[-self.model_max_length :]
            return input_ids




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


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				0
            if os.path.isdir(A__					):
                 __lowerCamelCase						:       Union[str, Any]				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
                 __lowerCamelCase						:       int				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]					)
            else:
                 __lowerCamelCase						:       str				   =				(
                     (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
                 )
                 __lowerCamelCase						:       int				   =				(
                     (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
                 )
            with open(A__	,    """w"""	,    encoding="""utf-8"""					) as writer:
                 for token_index, token in self.ids_to_tokens.items():
                      if index != token_index:
                           logger.warning(
                               f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                               """ Please check that the vocabulary is not corrupted!"""					)
                           __lowerCamelCase						:       Union[str, Any]				   =				token_index
                      writer.write(""",""".join(A__					) + """\n"""					)
                      index += 1
            with open(A__	,    """w"""	,    encoding="""utf-8"""					) as writer:
                 json.dump(self.emoji	,    A__					)
            return vocab_file, emoji_file







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




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


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				vocab  # same as swe
            __lowerCamelCase						:       Union[str, Any]				   =				ids_to_tokens  # same as bpe
            __lowerCamelCase						:       List[str]				   =				emoji
            __lowerCamelCase						:       int				   =				np.max([len(A__					) for w in self.vocab.keys()]					)
            __lowerCamelCase						:       List[Any]				   =				re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""					)
            __lowerCamelCase						:       Optional[Any]				   =				re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""					)
            __lowerCamelCase						:       str				   =				re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""					)
            __lowerCamelCase						:       Optional[int]				   =				re.compile(
                r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
            __lowerCamelCase						:       Union[str, Any]				   =				re.compile(
                r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
            __lowerCamelCase						:       Tuple				   =				re.compile(
                r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""					)
            __lowerCamelCase						:       List[str]				   =				"""─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
            __lowerCamelCase						:       List[str]				   =				"""▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
            __lowerCamelCase						:       Optional[int]				   =				str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}					)




       def __len__(					self      : Tuple					):


            """simple docstring"""







            return len(self.ids_to_tokens					)




       def 					a_					(					self      : Dict	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<URL>"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<EMAIL>"""	,    A__					)
            __lowerCamelCase						:       str				   =				self.content_repattera.sub("""<TEL>"""	,    A__					)
            __lowerCamelCase						:       Optional[int]				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       List[str]				   =				self.content_repattera.sub("""<PRICE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				content.translate(self.content_transa					)
            while "<BLOCK><BLOCK>" in content:
                 __lowerCamelCase						:       Dict				   =				content.replace("""<BLOCK><BLOCK>"""	,    """<BLOCK>"""					)
            return content




       def 					a_					(					self      : List[Any]	,    A__      : Dict	,    A__      : Optional[Any]=False					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				text.replace(""" """	,    """<SP>"""					)
            __lowerCamelCase						:       Optional[int]				   =				text.replace("""　"""	,    """<SP>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r"""	,    """<BR>"""					)
            __lowerCamelCase						:       List[str]				   =				text.replace("""\t"""	,    """<TAB>"""					)
            __lowerCamelCase						:       List[Any]				   =				text.replace("""—"""	,    """ー"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""−"""	,    """ー"""					)
            for k, v in self.emoji["emoji"].items():
                 if k in text:
                      __lowerCamelCase						:       Dict				   =				text.replace(A__	,    A__					)
            if clean:
                 __lowerCamelCase						:       Optional[int]				   =				self.clean_text(A__					)

            def check_simbol(A__      : Union[str, Any]					):
                 __lowerCamelCase						:       str				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 2:
                      __lowerCamelCase						:       Optional[int]				   =				(int(e[0]					) << 8) + int(e[1]					)
                      if (
                          (c >= 0XC2_A1 and c <= 0XC2_BF)
                          or (c >= 0XC7_80 and c <= 0XC7_83)
                          or (c >= 0XCA_B9 and c <= 0XCB_BF)
                          or (c >= 0XCC_80 and c <= 0XCD_A2)
                      ):
                           return True
                 return False

            def checkuae(A__      : Tuple					):
                 __lowerCamelCase						:       List[Any]				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 3:
                      __lowerCamelCase						:       List[Any]				   =				(int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
                      if c >= 0XE2_80_80 and c <= 0XE2_B0_7F:
                           return True
                 return False

            __lowerCamelCase						:       List[Any]				   =				0
            __lowerCamelCase						:       Optional[Any]				   =				[]
            while pos < len(A__					):
                 __lowerCamelCase						:       Tuple				   =				min(len(A__					)	,    pos + self.maxlen + 1					) if text[pos] == """<""" else pos + 3
                 __lowerCamelCase						:       Optional[int]				   =				[]  # (token_id, token, pos)
                 for e in range(A__	,    A__	,    -1					):
                      __lowerCamelCase						:       Dict				   =				text[pos:e]
                      if wd in self.vocab:
                           if wd[0] == "<" and len(A__					) > 2:
                                __lowerCamelCase						:       Any				   =				[(self.vocab[wd], wd, e)]
                                break
                           else:
                                candidates.append((self.vocab[wd], wd, e)					)
                 if len(A__					) > 0:
                      # the smallest token_id is adopted
                      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				sorted(A__	,    key=lambda A__					: x[0]					)[0]
                      result.append(A__					)
                      __lowerCamelCase						:       Optional[Any]				   =				e
                 else:
                      __lowerCamelCase						:       Any				   =				pos + 1
                      __lowerCamelCase						:       Any				   =				text[pos:end]
                      if check_simbol(A__					):
                           result.append("""<KIGOU>"""					)
                      elif checkuae(A__					):
                           result.append("""<U2000U2BFF>"""					)
                      else:
                           for i in wd.encode("""utf-8"""					):
                                result.append("""<|byte%d|>""" % i					)
                      __lowerCamelCase						:       Optional[int]				   =				end
            return result




       def 					a_					(					self      : Optional[Any]	,    A__      : Optional[int]	,    A__      : Any="\n"					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				[]
            __lowerCamelCase						:       List[Any]				   =				[]
            __lowerCamelCase						:       Optional[int]				   =				self.ids_to_tokens[index][0]
            if word[:6] == "<|byte" and word[-2:] == "|>":
                 byte_tokens.append(int(word[6:-2]					)					)
            else:
                 if len(A__					) > 0:
                      words.append(bytearray(A__					).decode("""utf-8"""	,    errors="""replace"""					)					)
                      __lowerCamelCase						:       Tuple				   =				[]
                 if word[:7] == "<|emoji" and word[-2:] == "|>":
                      words.append(self.emoji["""emoji_inv"""][word]					)
                 elif word == "<SP>":
                      words.append(""" """					)
                 elif word == "<BR>":
                      words.append(A__					)
                 elif word == "<TAB>":
                      words.append("""\t"""					)
                 elif word == "<BLOCK>":
                      words.append("""▀"""					)
                 elif word == "<KIGOU>":
                      words.append("""ǀ"""					)
                 elif word == "<U2000U2BFF>":
                      words.append("""‖"""					)
                 else:
                      words.append(A__					)
            if len(A__					) > 0:
                 words.append(bytearray(A__					).decode("""utf-8"""	,    errors="""replace"""					)					)
            __lowerCamelCase						:       str				   =				"""""".join(A__					)
            return text



