
from typing import TYPE_CHECKING

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


lowercase  :					str						    =							{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Any						    =							["""MaskFormerFeatureExtractor"""]
  lowercase  :					int						    =							["""MaskFormerImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[str]						    =							[
      """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MaskFormerForInstanceSegmentation""",
      """MaskFormerModel""",
      """MaskFormerPreTrainedModel""",
  ]
  lowercase  :					Union[str, Any]						    =							[
      """MaskFormerSwinBackbone""",
      """MaskFormerSwinModel""",
      """MaskFormerSwinPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  lowercase  :					Union[str, Any]						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure)





import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case__	:      List[str]     		=	0
				while count < prec:
								snake_case__	:      List[str]     		=	random.randint(2  ,  n - 1      )
								snake_case__	:      Optional[Any]     		=	bin_exp_mod(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
								if b != 1:
												snake_case__	:      List[Any]     		=	True
												for _ in range(__lowerCAmelCase      ):
																if b == n - 1:
																				snake_case__	:      List[str]     		=	False
																				break
																snake_case__	:      str     		=	b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
				A__								=						abs(int(input('''Enter bound : ''').strip()))
				print('''Here\'s the list of primes:''')
				print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))

