


'''simple docstring'''


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


_UpperCamelCase          =     logging.get_logger(__name__)

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

_UpperCamelCase          =     {
    """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"""
        )
    },
}

_UpperCamelCase          =     {"""Helsinki-NLP/opus-mt-en-de""": 512}
_UpperCamelCase          =     {}







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     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 ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase="<unk>" ,    lowerCamelCase="</s>" ,    lowerCamelCase="<pad>" ,    lowerCamelCase=512 ,    lowerCamelCase = None ,    lowerCamelCase=False ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Union[str, 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=lowerCamelCase ,    target_lang=lowerCamelCase ,    unk_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    model_max_length=lowerCamelCase ,    sp_model_kwargs=self.sp_model_kwargs ,    target_vocab_file=lowerCamelCase ,    separate_vocabs=lowerCamelCase ,    **lowerCamelCase ,    )
          assert Path(lowerCamelCase		).exists(), f"cannot find spm source {source_spm}"

          __A  :					Union[str, Any]						     =       separate_vocabs
          __A  :					Tuple						     =       load_json(lowerCamelCase		)
          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:
               __A  :					Optional[Any]						     =       load_json(lowerCamelCase		)
               __A  :					str						     =       {v: k for k, v in self.target_encoder.items()}
               __A  :					List[str]						     =       []
          else:
               __A  :					List[Any]						     =       {v: k for k, v in self.encoder.items()}
               __A  :					list						     =       [k for k in self.encoder if k.startswith(">>"		) and k.endswith("<<"		)]

          __A  :					Optional[int]						     =       source_lang
          __A  :					Dict						     =       target_lang
          __A  :					Optional[int]						     =       [source_spm, target_spm]

          # load SentencePiece model for pre-processing
          __A  :					Dict						     =       load_spm(lowerCamelCase ,    self.sp_model_kwargs		)
          __A  :					Dict						     =       load_spm(lowerCamelCase ,    self.sp_model_kwargs		)
          __A  :					int						     =       self.spm_source
          __A  :					Tuple						     =       self.encoder

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

          self._setup_normalizer()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          try:
               from sacremoses import MosesPunctNormalizer

               __A  :					Optional[Any]						     =       MosesPunctNormalizer(self.source_lang		).normalize
          except (ImportError, FileNotFoundError):
               warnings.warn("Recommended: pip install sacremoses."		)
               __A  :					Union[str, Any]						     =       lambda lowerCamelCase		: x


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.punc_normalizer(lowerCamelCase		) if x else ""


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.current_encoder.get(lowerCamelCase ,    self.current_encoder[self.unk_token]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       self.language_code_re.match(lowerCamelCase		)
          __A  :					list						     =       [match.group(0		)] if match else []
          return code, self.language_code_re.sub("" ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       self.remove_language_code(lowerCamelCase		)
          __A  :					Dict						     =       self.current_spm.encode(lowerCamelCase ,    out_type=lowerCamelCase		)
          return code + pieces


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.decoder.get(lowerCamelCase ,    self.unk_token		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return super().batch_decode(lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return super().decode(lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          __A  :					Optional[int]						     =       []
          __A  :					str						     =       ""
          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(lowerCamelCase		) + token + " "
                    __A  :					Dict						     =       []
               else:
                    current_sub_tokens.append(lowerCamelCase		)
          out_string += sp_model.decode_pieces(lowerCamelCase		)
          return out_string.strip()


     def lowerCAmelCase__     (      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 lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.spm_source
          __A  :					List[str]						     =       self.encoder


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       self.spm_target
          if self.separate_vocabs:
               __A  :					str						     =       self.target_encoder


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.encoder		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Union[str, Any]						     =       []

          if self.separate_vocabs:
               __A  :					Union[str, Any]						     =       os.path.join(
                   lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] ,    )
               __A  :					int						     =       os.path.join(
                   lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"] ,    )
               save_json(self.encoder ,    lowerCamelCase		)
               save_json(self.target_encoder ,    lowerCamelCase		)
               saved_files.append(lowerCamelCase		)
               saved_files.append(lowerCamelCase		)
          else:
               __A  :					List[Any]						     =       os.path.join(
                   lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]		)
               save_json(self.encoder ,    lowerCamelCase		)
               saved_files.append(lowerCamelCase		)

          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] ,    ):
               __A  :					Optional[int]						     =       os.path.join(
                   lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + spm_save_filename		)
               if os.path.abspath(lowerCamelCase		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(lowerCamelCase		):
                    copyfile(lowerCamelCase ,    lowerCamelCase		)
                    saved_files.append(lowerCamelCase		)
               elif not os.path.isfile(lowerCamelCase		):
                    with open(lowerCamelCase ,    "wb"		) as fi:
                         __A  :					Union[str, Any]						     =       spm_model.serialized_model_proto()
                         fi.write(lowerCamelCase		)
                    saved_files.append(lowerCamelCase		)

          return tuple(lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self.get_src_vocab()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return dict(self.encoder ,    **self.added_tokens_encoder		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return dict(self.target_encoder ,    **self.added_tokens_decoder		)


     def __getstate__(      self		):


          '''simple docstring'''





          __A  :					Union[str, 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'''





          __A  :					Dict						     =       d

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

          __A  :					Tuple						     =       (load_spm(lowerCamelCase ,    self.sp_model_kwargs		) for f in self.spm_files)
          __A  :					Any						     =       self.spm_source
          self._setup_normalizer()


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return 1


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return self._special_token_mask(lowerCamelCase		)
          elif token_ids_a is None:
               return self._special_token_mask(lowerCamelCase		) + [1]
          else:
               return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE       )
     spm.Load(SCREAMING_SNAKE_CASE       )
     return spm


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE					,				"w"       ) as f:
          json.dump(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				indent=2       )






def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE					,				"r"       ) as f:
          return json.load(SCREAMING_SNAKE_CASE       )





'''simple docstring'''


import math
from collections.abc import Generator


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)       ))
     for i in (n for n in numbers if n > 1):
          for j in range(2					,				SCREAMING_SNAKE_CASE       ):
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)       ))
     for i in (n for n in numbers if n > 1):
          # only need to check for factors up to sqrt(i)
          __A  :					Optional[Any]						     =       int(math.sqrt(SCREAMING_SNAKE_CASE       )       ) + 1
          for j in range(2					,				SCREAMING_SNAKE_CASE       ):
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Generator						     =       (i for i in range(1					,				(max_n + 1)					,				2       ))
     # It's useless to test even numbers as they will not be prime
     if max_n > 2:
          yield 2  # Because 2 will not be tested, it's necessary to yield it now
     for i in (n for n in numbers if n > 1):
          __A  :					List[str]						     =       int(math.sqrt(SCREAMING_SNAKE_CASE       )       ) + 1
          for j in range(3					,				SCREAMING_SNAKE_CASE					,				2       ):
               # As we removed the even numbers, we don't need them now
               if (i % j) == 0:
                    break
          else:
               yield i


def 		_lowercase   ():




     '''simple docstring'''





     from timeit import timeit

     __A  :					List[str]						     =       "from __main__ import slow_primes, primes, fast_primes"
     print(timeit("slow_primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )
     print(timeit("primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )
     print(timeit("fast_primes(1_000_000_000_000)"					,				setup=SCREAMING_SNAKE_CASE					,				number=100_0000       )       )


if __name__ == "__main__":
      _UpperCamelCase          =     int(input("""Calculate primes up to:\n>> """).strip())
      for ret in primes(number):
            print(ret)
      benchmark()

