
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A  :			Any     				=				{
    'configuration_esm': ['ESM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EsmConfig'],
    'tokenization_esm': ['EsmTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A  :			Optional[Any]     				=				[
					    'ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'EsmForMaskedLM',
					    'EsmForSequenceClassification',
					    'EsmForTokenClassification',
					    'EsmModel',
					    'EsmPreTrainedModel',
					]
					A  :			Dict     				=				['EsmForProteinFolding', 'EsmFoldPreTrainedModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A  :			Union[str, Any]     				=				[
					    'TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFEsmForMaskedLM',
					    'TFEsmForSequenceClassification',
					    'TFEsmForTokenClassification',
					    'TFEsmModel',
					    'TFEsmPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
					from .tokenization_esm import EsmTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_esm import (
										    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EsmForMaskedLM,
										    EsmForSequenceClassification,
										    EsmForTokenClassification,
										    EsmModel,
										    EsmPreTrainedModel,
										)
										from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_esm import (
										    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFEsmForMaskedLM,
										    TFEsmForSequenceClassification,
										    TFEsmForTokenClassification,
										    TFEsmModel,
										    TFEsmPreTrainedModel,
										)


else:
					import sys

					A  :			List[str]     				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()