

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_big_bird import BigBirdTokenizer
else:
		A_ :				Dict						 =    None

A_ :				Tuple						 =    logging.get_logger(__name__)
A_ :				Dict						 =    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A_ :				Optional[Any]						 =    {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

A_ :				int						 =    {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


A_ :				List[str]						 =    """▁"""



class 					_a		(UpperCamelCase_       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[int]													= VOCAB_FILES_NAMES
					UpperCAmelCase__:     Any													= PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:     int													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:     Any													= BigBirdTokenizer
					UpperCAmelCase__:     str													= ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:     List[str]													= []






					def __init__(				self					, A__=None					, A__=None					, A__="<unk>"					, A__="<s>"					, A__="</s>"					, A__="<pad>"					, A__="[SEP]"					, A__="[MASK]"					, A__="[CLS]"					, **A__					, ):
						A__							:					Tuple       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else bos_token
						A__							:					Union[str, Any]       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else eos_token
						A__							:					Union[str, Any]       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else unk_token
						A__							:					Optional[int]       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else pad_token
						A__							:					int       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else cls_token
						A__							:					str       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__							:					Union[str, Any]       =	AddedToken(__A					, lstrip=__A					, rstrip=__A    ) if isinstance(__A					, __A    ) else mask_token

						super().__init__(
						    __A					, tokenizer_file=__A					, bos_token=__A					, eos_token=__A					, unk_token=__A					, sep_token=__A					, pad_token=__A					, cls_token=__A					, mask_token=__A					, **__A					, )

						A__							:					List[Any]       =	vocab_file
						A__							:					Tuple       =	False if not self.vocab_file else True






					def 						__A    (				self					, A__					, A__ = None    ):
						A__							:					str       =	[self.sep_token_id]
						A__							:					List[str]       =	[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 						__A    (				self					, A__					, A__ = None					, A__ = 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 None:
							return [1] + ([0] * len(__A    )) + [1]
						return [1] + ([0] * len(__A    )) + [1] + ([0] * len(__A    )) + [1]






					def 						__A    (				self					, A__					, A__ = None    ):
						A__							:					Union[str, Any]       =	[self.sep_token_id]
						A__							:					List[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    ) * [0] + len(token_ids_a + sep    ) * [1]






					def 						__A    (				self					, A__					, A__ = None    ):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""    )

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

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

						return (out_vocab_file,)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A_ :				Optional[Any]						 =    {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				str						 =    ['BartphoTokenizer']

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	A_ :				Optional[int]						 =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



