

"""simple docstring"""




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase   :	List[Any]												=						logging.get_logger(__name__)

_lowercase   :	List[str]												=						{'vocab_file': 'spiece.model'}

_lowercase   :	List[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'
        ),
    }
}

_lowercase   :	int												=						{
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}
class      _UpperCAmelCase   (							_UpperCamelCase       ):
	a__		: Tuple					=					VOCAB_FILES_NAMES
	a__		: Any					=					PRETRAINED_VOCAB_FILES_MAP
	a__		: Optional[int]					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__		: Union[str, Any]					=					['input_ids', 'attention_mask']
	a__		: List[int]					=					[]



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

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

		__UpperCAmelCase      		= {} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=_UpperCAmelCase			,    eos_token=_UpperCAmelCase			,    unk_token=_UpperCAmelCase			,    pad_token=_UpperCAmelCase			,    sep_token=_UpperCAmelCase			,    mask_token=_UpperCAmelCase			,    cls_token=_UpperCAmelCase			,    sp_model_kwargs=self.sp_model_kwargs			,    **_UpperCAmelCase			,    )

		__UpperCAmelCase      		= vocab_file

		__UpperCAmelCase      		= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
		self.sp_model.Load(_UpperCAmelCase						)



	@property
	def        a  (  self     :       List[Any]						):
		return self.sp_model.get_piece_size()



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= {self.convert_ids_to_tokens(_UpperCAmelCase						): i for i in range(self.vocab_size						)}
		vocab.update(self.added_tokens_encoder						)
		return vocab



	def __getstate__(  self     :       Tuple						):
		__UpperCAmelCase      		= self.__dict__.copy()
		__UpperCAmelCase      		= None
		return state



	def __setstate__(  self     :       Any			,    _lowercase     :       Union[str, Any]						):
		__UpperCAmelCase      		= d

		# for backward compatibility
		if not hasattr(self			,    '''sp_model_kwargs'''						):
			__UpperCAmelCase      		= {}

		__UpperCAmelCase      		= spm.SentencePieceProcessor(**self.sp_model_kwargs						)
		self.sp_model.Load(self.vocab_file						)



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       str						):
		return self.sp_model.encode(_UpperCAmelCase			,    out_type=_UpperCAmelCase						)



	def        a  (  self     :       Dict			,    _lowercase     :       Tuple						):
		return self.sp_model.piece_to_id(_UpperCAmelCase						)



	def        a  (  self     :       List[str]			,    _lowercase     :       List[Any]						):
		__UpperCAmelCase      		= self.sp_model.IdToPiece(_UpperCAmelCase						)
		return token



	def        a  (  self     :       List[str]			,    _lowercase     :       Optional[int]						):
		__UpperCAmelCase      		= []
		__UpperCAmelCase      		= ''
		__UpperCAmelCase      		= False
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				if not prev_is_special:
					out_string += " "
				out_string += self.sp_model.decode(_UpperCAmelCase						) + token
				__UpperCAmelCase      		= True
				__UpperCAmelCase      		= []
			else:
				current_sub_tokens.append(_UpperCAmelCase						)
				__UpperCAmelCase      		= False
		out_string += self.sp_model.decode(_UpperCAmelCase						)
		return out_string.strip()



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       List[int]			,    _lowercase     :       bool = False			,    _lowercase     :       bool = None			,    _lowercase     :       bool = True			,    **_lowercase     :       Optional[int]			,    ):
		__UpperCAmelCase      		= kwargs.pop('''use_source_tokenizer'''			,    _UpperCAmelCase						)

		__UpperCAmelCase      		= self.convert_ids_to_tokens(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase						)

		# To avoid mixing byte-level and unicode for byte-level BPT
		# we need to build string separately for added tokens and byte-level tokens
		# cf. https://github.com/huggingface/transformers/issues/1133
		__UpperCAmelCase      		= []
		__UpperCAmelCase      		= []
		for token in filtered_tokens:
			if skip_special_tokens and token in self.all_special_ids:
				continue
			if token in self.added_tokens_encoder:
				if current_sub_text:
					sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase						)						)
					__UpperCAmelCase      		= []
				sub_texts.append(_UpperCAmelCase						)
			else:
				current_sub_text.append(_UpperCAmelCase						)
		if current_sub_text:
			sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase						)						)

		# Mimic the behavior of the Rust tokenizer:
		# No space before [MASK] and [SEP]
		if spaces_between_special_tokens:
			__UpperCAmelCase      		= re.sub(r''' (\[(MASK|SEP)\])'''			,    r'''\1'''			,    ''' '''.join(_UpperCAmelCase						)						)
		else:
			__UpperCAmelCase      		= ''.join(_UpperCAmelCase						)

		__UpperCAmelCase      		= (
		    clean_up_tokenization_spaces
		    if clean_up_tokenization_spaces is not None
		    else self.clean_up_tokenization_spaces
		)
		if clean_up_tokenization_spaces:
			__UpperCAmelCase      		= self.clean_up_tokenization(_UpperCAmelCase						)
			return clean_text
		else:
			return text



	def        a  (  self     :       Any			,    _lowercase     :       str			,    _lowercase     :       Optional[str] = None						):
		if not os.path.isdir(_UpperCAmelCase						):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
			return
		__UpperCAmelCase      		= 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						) and os.path.isfile(self.vocab_file						):
			copyfile(self.vocab_file			,    _UpperCAmelCase						)
		elif not os.path.isfile(self.vocab_file						):
			with open(_UpperCAmelCase			,    '''wb'''						) as fi:
				__UpperCAmelCase      		= self.sp_model.serialized_model_proto()
				fi.write(_UpperCAmelCase						)

		return (out_vocab_file,)



	def        a  (  self     :       Tuple			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None						):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		__UpperCAmelCase      		= [self.cls_token_id]
		__UpperCAmelCase      		= [self.sep_token_id]
		return cls + token_ids_a + sep + token_ids_a + sep



	def        a  (  self     :       Optional[int]			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None			,    _lowercase     :       bool = False						):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=_UpperCAmelCase			,    token_ids_a=_UpperCAmelCase			,    already_has_special_tokens=_UpperCAmelCase						)

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



	def        a  (  self     :       Tuple			,    _lowercase     :       List[int]			,    _lowercase     :       Optional[List[int]] = None						):
		__UpperCAmelCase      		= [self.sep_token_id]
		__UpperCAmelCase      		= [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]





'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       __magic_name__		(       _UpperCamelCase  ):







							@staticmethod
							@abstractmethod
							def 				__lowercase     (				_UpperCAmelCase   :			ArgumentParser						):
									raise NotImplementedError()







							@abstractmethod
							def 				__lowercase     (				self   :			str						):
									raise NotImplementedError()




