



'''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						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {'''vocab_file''': '''spiece.model'''}

__lowerCAmelCase						      =       {
    '''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						      =       {
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}

class      __magic_name__						(   _UpperCamelCase       ):
	lowerCAmelCase	:   Tuple           = VOCAB_FILES_NAMES
	lowerCAmelCase	:   Dict           = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   List[Any]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   int           = ['input_ids', 'attention_mask']
	lowerCAmelCase	:   List[int]           = []







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

			_a					:				Optional[int]				=							{} 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     ,)

			_a					:				int				=							vocab_file

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







	@property
	def 							__lowercase      (							self :	Union[str, Any]		):
			return self.sp_model.get_piece_size()







	def 							__lowercase      (							self :	str		):
			_a					:				Dict				=							{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]		):
			_a					:				List[str]				=							self.__dict__.copy()
			_a					:				Union[str, Any]				=							None
			return state







	def __setstate__(							self :	List[Any]     ,_UpperCAmelCase :	Dict		):
			_a					:				List[str]				=							d

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

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







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







	def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Any		):
			return self.sp_model.piece_to_id(_UpperCAmelCase		)







	def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Tuple		):
			_a					:				Tuple				=							self.sp_model.IdToPiece(_UpperCAmelCase		)
			return token







	def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Optional[int]		):
			_a					:				Union[str, Any]				=							[]
			_a					:				int				=							''
			_a					:				List[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(_UpperCAmelCase		) + token
							_a					:				Dict				=							True
							_a					:				Tuple				=							[]
					else:
							current_sub_tokens.append(_UpperCAmelCase		)
							_a					:				List[str]				=							False
			out_string += self.sp_model.decode(_UpperCAmelCase		)
			return out_string.strip()







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

			_a					:				List[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
			_a					:				str				=							[]
			_a					:				int				=							[]
			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		)		)
									_a					:				Dict				=							[]
							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:
					_a					:				List[str]				=							re.sub(R' (\[(MASK|SEP)\])'     ,R'\1'     ,' '.join(_UpperCAmelCase		)		)
			else:
					_a					:				str				=							''.join(_UpperCAmelCase		)

			_a					:				str				=							(
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
					_a					:				List[str]				=							self.clean_up_tokenization(_UpperCAmelCase		)
					return clean_text
			else:
					return text







	def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[str] = None		):
			if not os.path.isdir(_UpperCAmelCase		):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
					return
			_a					:				str				=							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:
							_a					:				Optional[int]				=							self.sp_model.serialized_model_proto()
							fi.write(_UpperCAmelCase		)

			return (out_vocab_file,)







	def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			_a					:				Any				=							[self.cls_token_id]
			_a					:				str				=							[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







	def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None     ,_UpperCAmelCase :	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 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
			_a					:				Any				=							[self.sep_token_id]
			_a					:				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'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




