



'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

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

__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

__lowerCAmelCase						      =       {'''bert_for_seq_generation''': 512}

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







							def __init__(							self :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Union[str, Any]="<s>"     ,_UpperCAmelCase :	Any="</s>"     ,_UpperCAmelCase :	Any="<unk>"     ,_UpperCAmelCase :	Optional[Any]="<pad>"     ,_UpperCAmelCase :	str="<::::>"     ,_UpperCAmelCase :	Optional[Dict[str, Any]] = None     ,**_UpperCAmelCase :	Union[str, Any]     ,):
									_a					:				Union[str, Any]				=							{} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=_UpperCAmelCase     ,eos_token=_UpperCAmelCase     ,unk_token=_UpperCAmelCase     ,pad_token=_UpperCAmelCase     ,sep_token=_UpperCAmelCase     ,sp_model_kwargs=self.sp_model_kwargs     ,**_UpperCAmelCase     ,)

									_a					:				Tuple				=							vocab_file

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







							@property
							def 							__lowercase      (							self :	Any		):
									return self.sp_model.get_piece_size()







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







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

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

									_a					:				Any				=							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	str		):
									return self.sp_model.encode(_UpperCAmelCase     ,out_type=_UpperCAmelCase		)







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







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Union[str, Any]				=							self.sp_model.IdToPiece(_UpperCAmelCase		)
									return token







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Dict		):
									_a					:				Tuple				=							[]
									_a					:				Union[str, Any]				=							''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_UpperCAmelCase		) + token
													_a					:				str				=							[]
											else:
													current_sub_tokens.append(_UpperCAmelCase		)
									out_string += self.sp_model.decode(_UpperCAmelCase		)
									return out_string.strip()







							def 							__lowercase      (							self :	List[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					:				int				=							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					:				Union[str, Any]				=							self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase		)

									return (out_vocab_file,)









'''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))




