






import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(snake_case_   )   )  # Size of every segment
			SCREAMING_SNAKE_CASE           =					[True] * (end + 1)
			SCREAMING_SNAKE_CASE           =					[]

			while start <= end:
						if temp[start] is True:
									in_prime.append(snake_case_   )
									for i in range(start * start					,	end + 1					,	snake_case_   ):
												SCREAMING_SNAKE_CASE           =					False
						start += 1
			prime += in_prime

			SCREAMING_SNAKE_CASE           =					end + 1
			SCREAMING_SNAKE_CASE           =					min(2 * end					,	snake_case_   )

			while low <= n:
						SCREAMING_SNAKE_CASE           =					[True] * (high - low + 1)
						for each in in_prime:
									SCREAMING_SNAKE_CASE           =					math.floor(low / each   ) * each
									if t < low:
												t += each

									for j in range(snake_case_					,	high + 1					,	snake_case_   ):
												SCREAMING_SNAKE_CASE           =					False

						for j in range(len(snake_case_   )   ):
									if temp[j] is True:
												prime.append(j + low   )

						SCREAMING_SNAKE_CASE           =					high + 1
						SCREAMING_SNAKE_CASE           =					min(high + end					,	snake_case_   )

			return prime




print(sieve(1_0**6))












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


__A						:							Union[str, Any]							=  logging.get_logger(__name__)

__A						:							str							=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """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"""
        ),
    },
}

__A						:							Tuple							=  {
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     CodeGenTokenizer





							def __init__(					self       :	Tuple       ,     a       :	Dict=None       ,     a       :	Tuple=None       ,     a       :	Tuple=None       ,     a       :	Optional[Any]="<|endoftext|>"       ,     a       :	Any="<|endoftext|>"       ,     a       :	Dict="<|endoftext|>"       ,     a       :	Optional[int]=False       ,     **a       :	Tuple       ,     )			->					str:
										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       :	Optional[Any]       ,     *a       :	Optional[Any]       ,     **a       :	List[Any]			)			->					BatchEncoding:
										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       :	Union[str, Any]       ,     *a       :	Tuple       ,     **a       :	List[Any]			)			->					BatchEncoding:
										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       :	Union[str, Any]       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										SCREAMING_SNAKE_CASE           =					self._tokenizer.model.save(a       ,     name=a			)
										return tuple(a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]       ,     a       :	bool = False       ,     a       :	bool = None       ,     a       :	Optional[List[str]] = None       ,     **a       :	List[Any]       ,     )			->					str:
										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       :	Any       ,     a       :	Optional[Any]       ,     a       :	List[str]			)			->					Any:
										def find_re(a       :	Tuple       ,     a       :	Any       ,     a       :	List[str]			):
													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






