






"""simple docstring"""





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				str					    =						{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				Tuple					    =						{
    '''Salesforce/codegen-350M-mono''': 2048,
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     str						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     List[Any]						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Tuple						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     List[Any]						= ["""input_ids""", """attention_mask"""]
    SCREAMING_SNAKE_CASE						:     Optional[int]						= CodeGenTokenizer






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Any=None       ,						__SCREAMING_SNAKE_CASE     :							str=None       ,						__SCREAMING_SNAKE_CASE     :							Dict=None       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							List[str]="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="<|endoftext|>"       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=False       ,						**__SCREAMING_SNAKE_CASE     :							int       ,						)     ->		List[Any]:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						bos_token=__SCREAMING_SNAKE_CASE       ,						eos_token=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      if kwargs.pop('add_bos_token'       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						kwargs.pop('name_or_path'       ,						''					)
        raise ValueError(
            'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
            'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
            F'''`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'''
            F'''`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'''
            'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
            ' so that the fast tokenizer works correctly.'					)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
      if pre_tok_state.get('add_prefix_space'       ,						__SCREAMING_SNAKE_CASE					) != add_prefix_space:
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						pre_tok_state.pop('type'					)					)
        lowerCamelCase_         =						add_prefix_space
        lowerCamelCase_         =						pre_tok_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						add_prefix_space






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						*__SCREAMING_SNAKE_CASE     :							Tuple       ,						**__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		BatchEncoding:
      lowerCamelCase_         =						kwargs.get('is_split_into_words'       ,						__SCREAMING_SNAKE_CASE					)
      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						*__SCREAMING_SNAKE_CASE     :							Any       ,						**__SCREAMING_SNAKE_CASE     :							int					)     ->		BatchEncoding:
      lowerCamelCase_         =						kwargs.get('is_split_into_words'       ,						__SCREAMING_SNAKE_CASE					)

      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							bool = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[str]] = None       ,						**__SCREAMING_SNAKE_CASE     :							Tuple       ,						)     ->		str:
      lowerCamelCase_         =						super().decode(
          token_ids=__SCREAMING_SNAKE_CASE       ,						skip_special_tokens=__SCREAMING_SNAKE_CASE       ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      if truncate_before_pattern is not None and len(__SCREAMING_SNAKE_CASE					) > 0:
        lowerCamelCase_         =						self.truncate(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      return decoded_text






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]					)     ->		Optional[Any]:
      def find_re(__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					):
        lowerCamelCase_         =						pattern.search(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        return m.start() if m else -1

      lowerCamelCase_         =						[re.compile(__SCREAMING_SNAKE_CASE       ,						re.MULTILINE					) for pattern in truncate_before_pattern]

      lowerCamelCase_         =						list(re.finditer('^print'       ,						__SCREAMING_SNAKE_CASE       ,						re.MULTILINE					)					)

      if len(__SCREAMING_SNAKE_CASE					) > 1:
        lowerCamelCase_         =						completion[: prints[1].start()]

      lowerCamelCase_         =						list(re.finditer('^def'       ,						__SCREAMING_SNAKE_CASE       ,						re.MULTILINE					)					)

      if len(__SCREAMING_SNAKE_CASE					) > 1:
        lowerCamelCase_         =						completion[: defs[1].start()]

      lowerCamelCase_         =						0

      lowerCamelCase_         =						[
          pos for pos in [find_re(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					) for terminal in terminals] if pos != -1
      ]

      if len(__SCREAMING_SNAKE_CASE					) > 0:
        return completion[: min(__SCREAMING_SNAKE_CASE					)]
      else:
        return completion












"""simple docstring"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


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




