





"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			[]
						UpperCamelCase				:		Optional[int]				   =			2
						UpperCamelCase				:		Tuple				   =			int(math.sqrt(SCREAMING_SNAKE_CASE     )     )  # Size of every segment
						UpperCamelCase				:		Union[str, Any]				   =			[True] * (end + 1)
						UpperCamelCase				:		Any				   =			[]

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

						UpperCamelCase				:		List[str]				   =			end + 1
						UpperCamelCase				:		Optional[Any]				   =			min(2 * end							,     SCREAMING_SNAKE_CASE     )

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

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

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

												UpperCamelCase				:		List[str]				   =			high + 1
												UpperCamelCase				:		Any				   =			min(high + end							,     SCREAMING_SNAKE_CASE     )

						return prime
print(sieve(1_0**6))




"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__			=    {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTBigCodeForSequenceClassification""",
        """GPTBigCodeForTokenClassification""",
        """GPTBigCodeForCausalLM""",
        """GPTBigCodeModel""",
        """GPTBigCodePreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
