

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


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							 =			{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_SCREAMING_SNAKE_CASE							 =			{
    '''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'''
        )
    },
}

_SCREAMING_SNAKE_CASE							 =			{'''Helsinki-NLP/opus-mt-en-de''': 5_1_2}
_SCREAMING_SNAKE_CASE							 =			{}


class      lowerCamelCase_ (			_snake_case):
 _lowerCAmelCase									=	VOCAB_FILES_NAMES
 _lowerCAmelCase									=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase									=	PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase									=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase									=	["""input_ids""", """attention_mask"""]
 _lowerCAmelCase									=	re.compile('''>>.+<<''')  # type: re.Pattern




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase="<unk>"  ,  _UpperCAmelCase="</s>"  ,  _UpperCAmelCase="<pad>"  ,  _UpperCAmelCase=512  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase=False  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  int		=     {} 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	:  int		=     separate_vocabs
   __UpperCAmelCase	:  List[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	:  Any		=     load_json(A_							)
     __UpperCAmelCase	:  str		=     {v: k for k, v in self.target_encoder.items()}
     __UpperCAmelCase	:  int		=     []
   else:
     __UpperCAmelCase	:  int		=     {v: k for k, v in self.encoder.items()}
     __UpperCAmelCase	:  Tuple		=     [k for k in self.encoder if k.startswith('>>'							) and k.endswith('<<'							)]

   __UpperCAmelCase	:  str		=     source_lang
   __UpperCAmelCase	:  Optional[int]		=     target_lang
   __UpperCAmelCase	:  List[Any]		=     [source_spm, target_spm]

   # load SentencePiece model for pre-processing
   __UpperCAmelCase	:  int		=     load_spm(A_  ,  self.sp_model_kwargs							)
   __UpperCAmelCase	:  Dict		=     load_spm(A_  ,  self.sp_model_kwargs							)
   __UpperCAmelCase	:  int		=     self.spm_source
   __UpperCAmelCase	:  Optional[int]		=     self.encoder

   # Multilingual target side: default to using first supported language code.

   self._setup_normalizer()




 def     a       (			self							):
   try:
     from sacremoses import MosesPunctNormalizer

     __UpperCAmelCase	:  Optional[int]		=     MosesPunctNormalizer(self.source_lang							).normalize
   except (ImportError, FileNotFoundError):
     warnings.warn('Recommended: pip install sacremoses.'							)
     __UpperCAmelCase	:  Optional[int]		=     lambda _UpperCAmelCase							: x




 def     a       (			self  ,  _UpperCAmelCase							):
   return self.punc_normalizer(A_							) if x else ""




 def     a       (			self  ,  _UpperCAmelCase							):
   return self.current_encoder.get(A_  ,  self.current_encoder[self.unk_token]							)




 def     a       (			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  List[Any]		=     self.language_code_re.match(A_							)
   __UpperCAmelCase	:  Optional[Any]		=     [match.group(0							)] if match else []
   return code, self.language_code_re.sub(''  ,  A_							)




 def     a       (			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.remove_language_code(A_							)
   __UpperCAmelCase	:  Optional[int]		=     self.current_spm.encode(A_  ,  out_type=A_							)
   return code + pieces




 def     a       (			self  ,  _UpperCAmelCase							):
   return self.decoder.get(A_  ,  self.unk_token							)




 def     a       (			self  ,  _UpperCAmelCase  ,  **_UpperCAmelCase							):
   return super().batch_decode(A_  ,  **A_							)




 def     a       (			self  ,  _UpperCAmelCase  ,  **_UpperCAmelCase							):
   return super().decode(A_  ,  **A_							)




 def     a       (			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     self.spm_source if self._decode_use_source_tokenizer else self.spm_target
   __UpperCAmelCase	:  Optional[int]		=     []
   __UpperCAmelCase	:  Dict		=     ''
   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	:  Any		=     []
     else:
       current_sub_tokens.append(A_							)
   out_string += sp_model.decode_pieces(A_							)
   return out_string.strip()




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=None							):
   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							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.spm_source
   __UpperCAmelCase	:  Any		=     self.encoder




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.spm_target
   if self.separate_vocabs:
     __UpperCAmelCase	:  Dict		=     self.target_encoder




 @property
 def     a       (			self							):
   return len(self.encoder							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None							):
   if not os.path.isdir(A_							):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
     return
   __UpperCAmelCase	:  Any		=     []

   if self.separate_vocabs:
     __UpperCAmelCase	:  Union[str, Any]		=     os.path.join(
         A_  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']  ,  )
     __UpperCAmelCase	:  Optional[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	:  Tuple		=     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	:  Optional[int]		=     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	:  Any		=     spm_model.serialized_model_proto()
         fi.write(A_							)
       saved_files.append(A_							)

   return tuple(A_							)




 def     a       (			self							):
   return self.get_src_vocab()




 def     a       (			self							):
   return dict(self.encoder  ,  **self.added_tokens_encoder							)




 def     a       (			self							):
   return dict(self.target_encoder  ,  **self.added_tokens_decoder							)




 def __getstate__(			self							):
   __UpperCAmelCase	:  Dict		=     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  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Union[str, Any]		=     d

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

   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     (load_spm(A_  ,  self.sp_model_kwargs							) for f in self.spm_files)
   __UpperCAmelCase	:  str		=     self.spm_source
   self._setup_normalizer()




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return 1




 def     a       (			self  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  str		=     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  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False							):
   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  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Union[str, Any]  )							->		List[Any]:
  __UpperCAmelCase	:  Dict		=     sentencepiece.SentencePieceProcessor(**__A  )
  spm.Load(__A  )
  return spm





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Union[str, Any]  )							->		Dict:
  with open(__A    ,       'w'  ) as f:
    json.dump(__A    ,       __A    ,       indent=2  )







def  _snake_case		(snake_case_					:     Optional[Any]  )							->		int:
  with open(__A    ,       'r'  ) as f:
    return json.load(__A  )








from __future__ import annotations





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    {1}
    SCREAMING_SNAKE_CASE__         =    [1]
    for _ in range(2			,							depth + 1						):
        SCREAMING_SNAKE_CASE__         =    [*previous_coefficients, 0]
        SCREAMING_SNAKE_CASE__         =    [0, *previous_coefficients]
        SCREAMING_SNAKE_CASE__         =    []
        for x, y in zip(__A			,							__A						):
            coefficients.add(x + y						)
            previous_coefficients.append(x + y						)
    return coefficients





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    set()
    for number in unique_coefficients:
        SCREAMING_SNAKE_CASE__         =    2
        SCREAMING_SNAKE_CASE__         =    number
        while divisor**2 <= copy_number:
            SCREAMING_SNAKE_CASE__         =    0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(__A						)
                break
            divisor += 1

    return unique_coefficients.difference(__A						)





def 				__a							(       __A = 51						):
    SCREAMING_SNAKE_CASE__         =    get_pascal_triangle_unique_coefficients(__A						)
    SCREAMING_SNAKE_CASE__         =    get_squarefrees(__A						)
    return sum(__A						)


if __name__ == "__main__":
  print(f'''{solution() = }''')


