

'''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


UpperCAmelCase					:			Optional[Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			List[str]       =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:			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'
        ),
    },
}

UpperCAmelCase					:			Any       =	{
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}







class 		lowerCAmelCase__  ( __lowercase   ):



 """simple docstring"""



 lowerCAmelCase__       = VOCAB_FILES_NAMES
 lowerCAmelCase__       = PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       = ["input_ids", "attention_mask"]
 lowerCAmelCase__       = CodeGenTokenizer
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				__SCREAMING_SNAKE_CASE   :			List[str]="<|endoftext|>"   ,				__SCREAMING_SNAKE_CASE   :			Any="<|endoftext|>"   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="<|endoftext|>"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=False   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					int:


      """simple docstring"""




      super().__init__(
          _A   ,				_A   ,				tokenizer_file=_A   ,				unk_token=_A   ,				bos_token=_A   ,				eos_token=_A   ,				add_prefix_space=_A   ,				**_A   ,				)

      if kwargs.pop("""add_bos_token"""   ,				_A     ):
           __SCREAMING_SNAKE_CASE      = 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."""     )

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

      __SCREAMING_SNAKE_CASE      = add_prefix_space
 def  UpperCAmelCase__						(						self   :			int   ,				*__SCREAMING_SNAKE_CASE   :			int   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = kwargs.get("""is_split_into_words"""   ,				_A     )
      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(*_A   ,				**_A     )
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			int     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = kwargs.get("""is_split_into_words"""   ,				_A     )

      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(*_A   ,				**_A     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[str] = None     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(_A   ,				name=_A     )
      return tuple(_A     )
 def  UpperCAmelCase__						(						self   :			Tuple   ,				__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   :			Optional[int]   ,				)    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = super().decode(
          token_ids=_A   ,				skip_special_tokens=_A   ,				clean_up_tokenization_spaces=_A   ,				**_A   ,				)

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

      return decoded_text


 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					Tuple:


      """simple docstring"""




      def find_re(__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Tuple     ):
           __SCREAMING_SNAKE_CASE      = pattern.search(_A   ,				_A     )
           return m.start() if m else -1

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

      __SCREAMING_SNAKE_CASE      = list(re.finditer("""^print"""   ,				_A   ,				re.MULTILINE     )     )

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

      __SCREAMING_SNAKE_CASE      = list(re.finditer("""^def"""   ,				_A   ,				re.MULTILINE     )     )

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

      __SCREAMING_SNAKE_CASE      = 0

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

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







from __future__ import annotations

_lowerCAmelCase       =		[True] * 1_000_001
_lowerCAmelCase       =		2
while i * i <= 1_000_000:
					if seive[i]:
										for j in range(i * i, 1_000_001, i):
															_lowerCAmelCase       =		False
					i += 1
def 					_snake_case	(  __snake_case						):
					return seive[n]
def 					_snake_case	(  __snake_case						):
					return any(digit in '''02468''' for digit in str(__snake_case						)						)
def 					_snake_case	(  __snake_case = 1000000						):
					_UpperCamelCase			     =     [2]  # result already includes the number 2.
					for num in range(3  ,     limit + 1  ,     2						):
										if is_prime(__snake_case						) and not contains_an_even_digit(__snake_case						):
															_UpperCamelCase			     =     str(__snake_case						)
															_UpperCamelCase			     =     [int(str_num[j:] + str_num[:j]						) for j in range(len(__snake_case						)						)]
															if all(is_prime(__snake_case						) for i in list_nums						):
																				result.append(__snake_case						)
					return result
def 					_snake_case	(  ):
					return len(find_circular_primes()						)


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