






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_rembert import RemBertTokenizer
else:
				A        =			None

A        =			logging.get_logger(__name__)
A        =			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

A        =			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

A        =			{
    'google/rembert': 256,
}

A        =			'▁'


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				int				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				List[str]				    =						RemBertTokenizer


		def __init__(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__=False ,    UpperCamelCase__="[CLS]" ,    UpperCamelCase__="[SEP]" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="[SEP]" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__="[CLS]" ,    UpperCamelCase__="[MASK]" ,    **UpperCamelCase__ ,    ):
						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE_   :	Tuple													=   AddedToken(UpperCamelCase__ ,    lstrip=UpperCamelCase__ ,    rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ) else mask_token

						super().__init__(
						    UpperCamelCase__ ,    tokenizer_file=UpperCamelCase__ ,    do_lower_case=UpperCamelCase__ ,    remove_space=UpperCamelCase__ ,    keep_accents=UpperCamelCase__ ,    bos_token=UpperCamelCase__ ,    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    sep_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    cls_token=UpperCamelCase__ ,    mask_token=UpperCamelCase__ ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_lower_case
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   remove_space
						SCREAMING_SNAKE_CASE_   :	Dict													=   keep_accents
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   vocab_file
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   False if not self.vocab_file else True


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	Any													=   [self.cls_token_id]
						if token_ids_a is None:
										return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):

						if already_has_special_tokens:
										if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'       )
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
										return [1] + ([0] * len(UpperCamelCase__       )) + [1] + ([0] * len(UpperCamelCase__       )) + [1]
						return [1] + ([0] * len(UpperCamelCase__       )) + [1]


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	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       ) * [0] + len(token_ids_a + sep       ) * [1]






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error('Vocabulary path ({}) should be a directory'.format(UpperCamelCase__       )       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   os.path.join(
						    UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

						if os.path.abspath(self.vocab_file       ) != os.path.abspath(UpperCamelCase__       ):
										copyfile(self.vocab_file ,    UpperCamelCase__       )

						return (out_vocab_file,)







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""Translation""" ,	init=__A ,	repr=__A						)


		def __call__(						self       ):
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






		def   __snake_case			(						self       ):
						from .features import Value

						return {k: Value('string'       ) for k in sorted(self.languages       )}






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[List]				    =						None
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""TranslationVariableLanguages""" ,	init=__A ,	repr=__A						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(self.languages       ) if self.languages else None


		def __call__(						self       ):
						return pa.struct({'language': pa.list_(pa.string()       ), 'translation': pa.list_(pa.string()       )}       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for lang, text in translation_dict.items():
										if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														translation_tuples.append((lang, text)       )
										else:
														translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

						return {"language": languages, "translation": translations}






		def   __snake_case			(						self       ):
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'       )       ),
						    "translation": Sequence(Value('string'       )       ),
						}