






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


snake_case			 =						logging.get_logger(__name__)

snake_case			 =						{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
   import detectrona


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		'''layoutlmv2'''


     def __init__( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		Optional[int]=3_0522    ,       UpperCAmelCase_				:		Any=768    ,       UpperCAmelCase_				:		Optional[Any]=12    ,       UpperCAmelCase_				:		Dict=12    ,       UpperCAmelCase_				:		List[str]=3072    ,       UpperCAmelCase_				:		int="gelu"    ,       UpperCAmelCase_				:		int=0.1    ,       UpperCAmelCase_				:		Union[str, Any]=0.1    ,       UpperCAmelCase_				:		Any=512    ,       UpperCAmelCase_				:		Dict=2    ,       UpperCAmelCase_				:		List[str]=0.02    ,       UpperCAmelCase_				:		Optional[Any]=1E-12    ,       UpperCAmelCase_				:		Dict=0    ,       UpperCAmelCase_				:		Union[str, Any]=1024    ,       UpperCAmelCase_				:		List[str]=128    ,       UpperCAmelCase_				:		List[Any]=32    ,       UpperCAmelCase_				:		Any=True    ,       UpperCAmelCase_				:		Dict=256    ,       UpperCAmelCase_				:		str=64    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=[7, 7, 256]    ,       UpperCAmelCase_				:		Tuple=128    ,       UpperCAmelCase_				:		int=128    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		Optional[int]=True    ,       UpperCAmelCase_				:		List[str]=False    ,       UpperCAmelCase_				:		Dict=None    ,       **UpperCAmelCase_				:		Optional[Any]    ,       ):
       super().__init__(
           vocab_size=UpperCAmelCase_    ,       hidden_size=UpperCAmelCase_    ,       num_hidden_layers=UpperCAmelCase_    ,       num_attention_heads=UpperCAmelCase_    ,       intermediate_size=UpperCAmelCase_    ,       hidden_act=UpperCAmelCase_    ,       hidden_dropout_prob=UpperCAmelCase_    ,       attention_probs_dropout_prob=UpperCAmelCase_    ,       max_position_embeddings=UpperCAmelCase_    ,       type_vocab_size=UpperCAmelCase_    ,       initializer_range=UpperCAmelCase_    ,       layer_norm_eps=UpperCAmelCase_    ,       pad_token_id=UpperCAmelCase_    ,       **UpperCAmelCase_    ,       )
       SCREAMING_SNAKE_CASE				:					Tuple		  =			max_ad_position_embeddings
       SCREAMING_SNAKE_CASE				:					Tuple		  =			max_rel_pos
       SCREAMING_SNAKE_CASE				:					Tuple		  =			rel_pos_bins
       SCREAMING_SNAKE_CASE				:					str		  =			fast_qkv
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			max_rel_ad_pos
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			rel_ad_pos_bins
       SCREAMING_SNAKE_CASE				:					Any		  =			convert_sync_batchnorm
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_feature_pool_shape
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			coordinate_size
       SCREAMING_SNAKE_CASE				:					Dict		  =			shape_size
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			has_relative_attention_bias
       SCREAMING_SNAKE_CASE				:					List[str]		  =			has_spatial_attention_bias
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			has_visual_segment_embedding
       SCREAMING_SNAKE_CASE				:					List[str]		  =			(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


     @classmethod
     def  _A				( self				:		Dict					):
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					int		  =			detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			k.split("."					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			detectrona_config
         for attribute in attributes[:-1]:
           SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			getattr(UpperCAmelCase_    ,       UpperCAmelCase_					)
         setattr(UpperCAmelCase_    ,       attributes[-1]    ,       UpperCAmelCase_					)

       return detectrona_config














import math
from collections.abc import Generator


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    # only need to check for factors up to sqrt(i)
    SCREAMING_SNAKE_CASE				:					str		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1)  ,   2 ))
  # It's useless to test even numbers as they will not be prime
  if max_n > 2:
    yield 2  # Because 2 will not be tested, it's necessary to yield it now
  for i in (n for n in numbers if n > 1):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(3  ,   lowercase  ,   2 ):
      # As we removed the even numbers, we don't need them now
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   ):

  """simple docstring"""






  from timeit import timeit

  SCREAMING_SNAKE_CASE				:					Tuple		  =			"from __main__ import slow_primes, primes, fast_primes"
  print(timeit("slow_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("fast_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )


if __name__ == "__main__":
   snake_case			 =						int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()






