code: 
  
  import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     {
      """source_spm""": """source.spm""",
      """target_spm""": """target.spm""",
      """vocab""": """vocab.json""",
      """target_vocab_file""": """target_vocab.json""",
      """tokenizer_config_file""": """tokenizer_config.json""",
  }
  
  _A						=     {
      """source_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
      },
      """target_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
      },
      """vocab""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
      },
      """tokenizer_config_file""": {
          """Helsinki-NLP/opus-mt-en-de""": (
              """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
          )
      },
  }
  
  _A						=     {"""Helsinki-NLP/opus-mt-en-de""": 5_12}
  _A						=     {}
  
  class 			lowerCamelCase  (					a__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE							=     PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE							=     PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE							=     ['input_ids', 'attention_mask']
    SCREAMING_SNAKE_CASE							=     re.compile('>>.+<<'      )  # type: re.Pattern
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase="<unk>"       ,					_lowerCamelCase="</s>"       ,					_lowerCamelCase="<pad>"       ,					_lowerCamelCase=512       ,					_lowerCamelCase = None       ,					_lowerCamelCase=False       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						{} if sp_model_kwargs is None else sp_model_kwargs
  
          super().__init__(
              # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
              source_lang=__A       ,					target_lang=__A       ,					unk_token=__A       ,					eos_token=__A       ,					pad_token=__A       ,					model_max_length=__A       ,					sp_model_kwargs=self.sp_model_kwargs       ,					target_vocab_file=__A       ,					separate_vocabs=__A       ,					**__A       ,					)
          assert Path(__A  ).exists(), F"""cannot find spm source {source_spm}"""
  
          UpperCAmelCase__ :       Dict						=						separate_vocabs
          UpperCAmelCase__ :       Any						=						load_json(__A  )
          if self.unk_token not in self.encoder:
                raise KeyError("""<unk> token must be in vocab"""  )
          assert self.pad_token in self.encoder
  
          if separate_vocabs:
                UpperCAmelCase__ :       Union[str, Any]						=						load_json(__A  )
                UpperCAmelCase__ :       Dict						=						{v: k for k, v in self.target_encoder.items()}
                UpperCAmelCase__ :       Optional[int]						=						[]
          else:
                UpperCAmelCase__ :       Dict						=						{v: k for k, v in self.encoder.items()}
                UpperCAmelCase__ :       list						=						[k for k in self.encoder if k.startswith(""">>"""  ) and k.endswith("""<<"""  )]
  
          UpperCAmelCase__ :       List[Any]						=						source_lang
          UpperCAmelCase__ :       Tuple						=						target_lang
          UpperCAmelCase__ :       Union[str, Any]						=						[source_spm, target_spm]
  
          # load SentencePiece model for pre-processing
          UpperCAmelCase__ :       List[str]						=						load_spm(__A       ,					self.sp_model_kwargs  )
          UpperCAmelCase__ :       List[Any]						=						load_spm(__A       ,					self.sp_model_kwargs  )
          UpperCAmelCase__ :       Optional[int]						=						self.spm_source
          UpperCAmelCase__ :       int						=						self.encoder
  
          # Multilingual target side: default to using first supported language code.
  
          self._setup_normalizer()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          try:
                from sacremoses import MosesPunctNormalizer
  
                UpperCAmelCase__ :       Tuple						=						MosesPunctNormalizer(self.source_lang  ).normalize
          except (ImportError, FileNotFoundError):
                warnings.warn("""Recommended: pip install sacremoses."""  )
                UpperCAmelCase__ :       Tuple						=						lambda _lowerCamelCase  : x
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.punc_normalizer(__A  ) if x else ""
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.current_encoder.get(__A       ,					self.current_encoder[self.unk_token]  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.language_code_re.match(__A  )
          UpperCAmelCase__ :       list						=						[match.group(0  )] if match else []
          return code, self.language_code_re.sub(""""""       ,					__A  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.remove_language_code(__A  )
          UpperCAmelCase__ :       List[str]						=						self.current_spm.encode(__A       ,					out_type=__A  )
          return code + pieces
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.decoder.get(__A       ,					self.unk_token  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return super().batch_decode(__A       ,					**__A  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return super().decode(__A       ,					**__A  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          UpperCAmelCase__ :       List[str]						=						[]
          UpperCAmelCase__ :       List[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 += sp_model.decode_pieces(__A  ) + token + " "
                      UpperCAmelCase__ :       Tuple						=						[]
                else:
                      current_sub_tokens.append(__A  )
          out_string += sp_model.decode_pieces(__A  )
          return out_string.strip()
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.spm_source
          UpperCAmelCase__ :       Optional[Any]						=						self.encoder
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.spm_target
          if self.separate_vocabs:
                UpperCAmelCase__ :       Optional[Any]						=						self.target_encoder
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return len(self.encoder  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          if not os.path.isdir(__A  ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
                return
          UpperCAmelCase__ :       Optional[Any]						=						[]
  
          if self.separate_vocabs:
                UpperCAmelCase__ :       int						=						os.path.join(
                    __A       ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]       ,					)
                UpperCAmelCase__ :       Union[str, Any]						=						os.path.join(
                    __A       ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]       ,					)
                save_json(self.encoder       ,					__A  )
                save_json(self.target_encoder       ,					__A  )
                saved_files.append(__A  )
                saved_files.append(__A  )
          else:
                UpperCAmelCase__ :       int						=						os.path.join(
                    __A       ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]  )
                save_json(self.encoder       ,					__A  )
                saved_files.append(__A  )
  
          for spm_save_filename, spm_orig_path, spm_model in zip(
              [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]       ,					self.spm_files       ,					[self.spm_source, self.spm_target]       ,					):
                UpperCAmelCase__ :       List[str]						=						os.path.join(
                    __A       ,					(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename  )
                if os.path.abspath(__A  ) != os.path.abspath(__A  ) and os.path.isfile(__A  ):
                      copyfile(__A       ,					__A  )
                      saved_files.append(__A  )
                elif not os.path.isfile(__A  ):
                      with open(__A       ,					"""wb"""  ) as fi:
                            UpperCAmelCase__ :       List[Any]						=						spm_model.serialized_model_proto()
                            fi.write(__A  )
                      saved_files.append(__A  )
  
          return tuple(__A  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return self.get_src_vocab()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return dict(self.encoder       ,					**self.added_tokens_encoder  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return dict(self.target_encoder       ,					**self.added_tokens_decoder  )
  
  
  
    def __getstate__(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.__dict__.copy()
          state.update(
              {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}  )
          return state
  
  
  
    def __setstate__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						d
  
          # for backward compatibility
          if not hasattr(self       ,					"""sp_model_kwargs"""  ):
                UpperCAmelCase__ :       Dict						=						{}
  
          UpperCAmelCase__ :       Union[str, Any]						=						(load_spm(__A       ,					self.sp_model_kwargs  ) for f in self.spm_files)
          UpperCAmelCase__ :       int						=						self.spm_source
          self._setup_normalizer()
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return 1
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						set(self.all_special_ids  )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special
          return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = False  ):
  
  
          """simple docstring"""
  
          if already_has_special_tokens:
                return self._special_token_mask(__A  )
          elif token_ids_a is None:
                return self._special_token_mask(__A  ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   sentencepiece.SentencePieceProcessor:
        UpperCAmelCase__ :       Optional[Any]						=						sentencepiece.SentencePieceProcessor(**lowerCAmelCase						)
        spm.Load(lowerCAmelCase						)
        return spm
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   None:
  
        with open(lowerCAmelCase ,  """w"""						) as f:
              json.dump(lowerCAmelCase ,  lowerCAmelCase ,  indent=2						)
  
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Union[Dict, List]:
  
        with open(lowerCAmelCase ,  """r"""						) as f:
              return json.load(lowerCAmelCase						)
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[Any]						=						{1}
        UpperCAmelCase__ :       Optional[int]						=						[1]
        for _ in range(2 ,  depth + 1						):
              UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
              UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
              UpperCAmelCase__ :       Dict						=						[]
              for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                    coefficients.add(x + y						)
                    previous_coefficients.append(x + y						)
        return coefficients
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[str]						=						set()
        for number in unique_coefficients:
              UpperCAmelCase__ :       int						=						2
              UpperCAmelCase__ :       List[Any]						=						number
              while divisor**2 <= copy_number:
                    UpperCAmelCase__ :       Tuple						=						0
                    while copy_number % divisor == 0:
                          copy_number //= divisor
                          multiplicity += 1
                    if multiplicity >= 2:
                          non_squarefrees.add(lowerCAmelCase						)
                          break
                    divisor += 1
  
        return unique_coefficients.difference(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase = 51						)  ->   int:
        UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
        return sum(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  