






import os
import re
import unicodedata
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


__UpperCAmelCase										=		logging.get_logger(__name__)
__UpperCAmelCase										=		{'vocab_file': 'spiece.model'}

__UpperCAmelCase										=		{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

__UpperCAmelCase										=		{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

__UpperCAmelCase										=		'▁'



class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					List[Any] 		=						VOCAB_FILES_NAMES
		_snake_case					:					Optional[Any] 		=						PRETRAINED_VOCAB_FILES_MAP
		_snake_case					:					Any 		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case					:					List[str] 		=						['''input_ids''', '''token_type_ids''']



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase=False       ,	_UpperCamelCase=True       ,	_UpperCamelCase=True       ,	_UpperCamelCase="<unk>"       ,	_UpperCamelCase="[SEP]"       ,	_UpperCamelCase="<pad>"       ,	_UpperCamelCase="[CLS]"       ,	_UpperCamelCase="[MASK]"       ,	_UpperCamelCase = None       ,	**_UpperCamelCase       ,	)			->							None:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							UpperCAmelCase_					:   Any   =		(
							    AddedToken(_UpperCamelCase       ,	lstrip=_UpperCamelCase       ,	rstrip=_UpperCamelCase       ,	normalized=_UpperCamelCase     )
							    if isinstance(_UpperCamelCase       ,	_UpperCamelCase     )
							    else mask_token
							)

							UpperCAmelCase_					:   Optional[Any]   =		{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=_UpperCamelCase       ,	remove_space=_UpperCamelCase       ,	keep_accents=_UpperCamelCase       ,	unk_token=_UpperCamelCase       ,	sep_token=_UpperCamelCase       ,	pad_token=_UpperCamelCase       ,	cls_token=_UpperCamelCase       ,	mask_token=_UpperCamelCase       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**_UpperCamelCase       ,	)

							UpperCAmelCase_					:   Dict   =		do_lower_case
							UpperCAmelCase_					:   str   =		remove_space
							UpperCAmelCase_					:   Tuple   =		keep_accents
							UpperCAmelCase_					:   Optional[Any]   =		vocab_file

							UpperCAmelCase_					:   List[str]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs     )
							self.sp_model.Load(_UpperCamelCase     )



		@property
		def       __UpperCAmelCase    (							self     )			->							Dict:
							return len(self.sp_model     )



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							UpperCAmelCase_					:   int   =		{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     )			->							List[Any]:
							UpperCAmelCase_					:   Optional[int]   =		self.__dict__.copy()
							UpperCAmelCase_					:   Optional[Any]   =		None
							return state



		def __setstate__(							self       ,	_UpperCamelCase     )			->							Union[str, Any]:
							UpperCAmelCase_					:   int   =		d

							# for backward compatibility
							if not hasattr(self       ,	'sp_model_kwargs'     ):
												UpperCAmelCase_					:   Optional[Any]   =		{}

							UpperCAmelCase_					:   List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs     )
							self.sp_model.Load(self.vocab_file     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Optional[Any]:
							if self.remove_space:
												UpperCAmelCase_					:   Optional[int]   =		' '.join(inputs.strip().split()     )
							else:
												UpperCAmelCase_					:   str   =		inputs
							UpperCAmelCase_					:   List[Any]   =		outputs.replace('``'       ,	'"'     ).replace('\'\''       ,	'"'     )

							if not self.keep_accents:
												UpperCAmelCase_					:   Union[str, Any]   =		unicodedata.normalize('NFKD'       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   str   =		''.join([c for c in outputs if not unicodedata.combining(_UpperCamelCase     )]     )
							if self.do_lower_case:
												UpperCAmelCase_					:   Union[str, Any]   =		outputs.lower()

							return outputs



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[str]:
							UpperCAmelCase_					:   Union[str, Any]   =		self.preprocess_text(_UpperCamelCase     )
							UpperCAmelCase_					:   Tuple   =		self.sp_model.encode(_UpperCamelCase       ,	out_type=_UpperCamelCase     )
							UpperCAmelCase_					:   Tuple   =		[]
							for piece in pieces:
												if len(_UpperCamelCase     ) > 1 and piece[-1] == str(','     ) and piece[-2].isdigit():
																	UpperCAmelCase_					:   str   =		self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCamelCase       ,	''     )     )
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]     ) == 1:
																											UpperCAmelCase_					:   Any   =		cur_pieces[1:]
																						else:
																											UpperCAmelCase_					:   Tuple   =		cur_pieces[0][1:]
																	cur_pieces.append(piece[-1]     )
																	new_pieces.extend(_UpperCamelCase     )
												else:
																	new_pieces.append(_UpperCamelCase     )

							return new_pieces



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Any:
							return self.sp_model.PieceToId(_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Union[str, Any]:
							return self.sp_model.IdToPiece(_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		[]
							UpperCAmelCase_					:   List[str]   =		''
							UpperCAmelCase_					:   str   =		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_					:   Optional[Any]   =		True
																	UpperCAmelCase_					:   str   =		[]
												else:
																	current_sub_tokens.append(_UpperCamelCase     )
																	UpperCAmelCase_					:   str   =		False
							out_string += self.sp_model.decode(_UpperCamelCase     )
							return out_string.strip()



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = False       ,	_UpperCamelCase = None       ,	_UpperCamelCase = True       ,	**_UpperCamelCase       ,	)			->							str:
							UpperCAmelCase_					:   Tuple   =		kwargs.pop('use_source_tokenizer'       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Union[str, Any]   =		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_					:   List[Any]   =		[]
							UpperCAmelCase_					:   Dict   =		[]
							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_					:   Union[str, Any]   =		[]
																	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 after <unk>
							if spaces_between_special_tokens:
												UpperCAmelCase_					:   str   =		re.sub(r'(<unk>) '       ,	r'\1'       ,	' '.join(_UpperCamelCase     )     )
							else:
												UpperCAmelCase_					:   int   =		''.join(_UpperCamelCase     )

							UpperCAmelCase_					:   int   =		(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
												UpperCAmelCase_					:   Dict   =		self.clean_up_tokenization(_UpperCamelCase     )
												return clean_text
							else:
												return text



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:
							UpperCAmelCase_					:   str   =		[self.sep_token_id]
							UpperCAmelCase_					:   Dict   =		[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       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None       ,	_UpperCamelCase = False     )			->							List[int]:

							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 not None:
												return [1] + ([0] * len(_UpperCamelCase     )) + [1] + ([0] * len(_UpperCamelCase     )) + [1]
							return [1] + ([0] * len(_UpperCamelCase     )) + [1]



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							List[int]:
							UpperCAmelCase_					:   Tuple   =		[self.sep_token_id]
							UpperCAmelCase_					:   Union[str, 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       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None     )			->							Tuple[str]:
							if not os.path.isdir(_UpperCamelCase     ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
												return
							UpperCAmelCase_					:   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     ) 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_					:   Optional[Any]   =		self.sp_model.serialized_model_proto()
																	fi.write(_UpperCamelCase     )

							return (out_vocab_file,)














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






