


'''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


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

lowerCAmelCase				:Optional[int]        =      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase				:List[str]        =      {
    '''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'''
        ),
    }
}

lowerCAmelCase				:Tuple        =      {
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      str				   =    VOCAB_FILES_NAMES
		A_     :      int				   =    PRETRAINED_VOCAB_FILES_MAP
		A_     :      int				   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_     :      List[Any]				   =    ["""input_ids""", """attention_mask"""]
		A_     :      List[int]				   =    []







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

			__magic_name__	:   int					     =					{} if sp_model_kwargs is None else sp_model_kwargs

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

			__magic_name__	:   int					     =					vocab_file

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







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			return self.sp_model.get_piece_size()







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			__magic_name__	:   Optional[Any]					     =					{self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
			vocab.update(self.added_tokens_encoder      )
			return vocab







		def __getstate__( self		:						str      )  ->			Dict:
			__magic_name__	:   Optional[int]					     =					self.__dict__.copy()
			__magic_name__	:   List[str]					     =					None
			return state







		def __setstate__( self		:						Optional[int]  ,					_A		:						List[Any]      )  ->			List[Any]:
			__magic_name__	:   Optional[Any]					     =					d

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

			__magic_name__	:   Optional[int]					     =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
			self.sp_model.Load(self.vocab_file      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[str]:
			return self.sp_model.encode(_A  ,					out_type=_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						str      )  ->			List[str]:
			return self.sp_model.piece_to_id(_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						int      )  ->			Optional[Any]:
			__magic_name__	:   Union[str, Any]					     =					self.sp_model.IdToPiece(_A      )
			return token







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Optional[int]      )  ->			str:
			__magic_name__	:   List[str]					     =					[]
			__magic_name__	:   str					     =					''
			__magic_name__	:   Union[str, Any]					     =					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(_A      ) + token
					__magic_name__	:   Any					     =					True
					__magic_name__	:   Any					     =					[]
				else:
					current_sub_tokens.append(_A      )
					__magic_name__	:   Union[str, Any]					     =					False
			out_string += self.sp_model.decode(_A      )
			return out_string.strip()







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[int]  ,					_A		:						bool = False  ,					_A		:						bool = None  ,					_A		:						bool = True  ,					**_A		:						Any  ,					)  ->			str:
			__magic_name__	:   str					     =					kwargs.pop('use_source_tokenizer'  ,					_A      )

			__magic_name__	:   Union[str, Any]					     =					self.convert_ids_to_tokens(_A  ,					skip_special_tokens=_A      )

			# 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
			__magic_name__	:   Optional[int]					     =					[]
			__magic_name__	:   Tuple					     =					[]
			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(_A      )      )
						__magic_name__	:   List[str]					     =					[]
					sub_texts.append(_A      )
				else:
					current_sub_text.append(_A      )
			if current_sub_text:
				sub_texts.append(self.convert_tokens_to_string(_A      )      )

			# Mimic the behavior of the Rust tokenizer:
			# No space before [MASK] and [SEP]
			if spaces_between_special_tokens:
				__magic_name__	:   Any					     =					re.sub(R' (\[(MASK|SEP)\])'  ,					R'\1'  ,					' '.join(_A      )      )
			else:
				__magic_name__	:   Optional[Any]					     =					''.join(_A      )

			__magic_name__	:   List[Any]					     =					(
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
				__magic_name__	:   int					     =					self.clean_up_tokenization(_A      )
				return clean_text
			else:
				return text







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

			return (out_vocab_file,)







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None      )  ->			List[int]:
			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__magic_name__	:   Union[str, Any]					     =					[self.cls_token_id]
			__magic_name__	:   Optional[int]					     =					[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None  ,					_A		:						bool = False      )  ->			List[int]:
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_A  ,					token_ids_a=_A  ,					already_has_special_tokens=_A      )

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







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[int]  ,					_A		:						Optional[List[int]] = None      )  ->			List[int]:
			__magic_name__	:   Any					     =					[self.sep_token_id]
			__magic_name__	:   Optional[int]					     =					[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'''

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    LxmertTokenizer
		A_     :      List[Any]				   =    LxmertTokenizerFast
		A_     :      int				   =    True
		A_     :      Any				   =    True







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			super().setUp()

			__magic_name__	:   str					     =					[
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			__magic_name__	:   List[str]					     =					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']      )
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'      ) as vocab_writer:
				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					'UNwant\u00E9d,running'
			__magic_name__	:   Dict					     =					'unwanted, running'
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					self.tokenizer_class(self.vocab_file      )

			__magic_name__	:   List[str]					     =					tokenizer.tokenize('UNwant\u00E9d,running'      )
			self.assertListEqual(_A  ,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )  ,					[7, 4, 5, 10, 8, 9]      )







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__	:   Any					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_rust_tokenizer()

			__magic_name__	:   Union[str, Any]					     =					'I was born in 92000, and this is falsé.'

			__magic_name__	:   List[Any]					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Dict					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   int					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   Union[str, Any]					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   str					     =					tokenizer.encode(_A      )
			__magic_name__	:   Optional[int]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )