





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__   :				Optional[int]      =   logging.get_logger(__name__)

lowercase__   :				Union[str, Any]      =   {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''table-transformer'''
     lowerCAmelCase		      =	['''past_key_values''']
     lowerCAmelCase		      =	{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(						self						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     _UpperCAmelCase=3						,     _UpperCAmelCase=100						,     _UpperCAmelCase=6						,     _UpperCAmelCase=2048						,     _UpperCAmelCase=8						,     _UpperCAmelCase=6						,     _UpperCAmelCase=2048						,     _UpperCAmelCase=8						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=True						,     _UpperCAmelCase="relu"						,     _UpperCAmelCase=256						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1.0						,     _UpperCAmelCase=False						,     _UpperCAmelCase="sine"						,     _UpperCAmelCase="resnet50"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=False						,     _UpperCAmelCase=1						,     _UpperCAmelCase=5						,     _UpperCAmelCase=2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=1						,     _UpperCAmelCase=5						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.1						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          if backbone_config is not None and use_timm_backbone:
               raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
                    __A						:			List[Any]								=	CONFIG_MAPPING['resnet'](out_features=['stage4'])
               elif isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
                    __A						:			List[Any]								=	backbone_config.get('model_type')
                    __A						:			Optional[Any]								=	CONFIG_MAPPING[backbone_model_type]
                    __A						:			Union[str, Any]								=	config_class.from_dict(_UpperCAmelCase)
               # set timm attributes to None
               __A			,__A			,__A						:			List[str]								=	None, None, None

          __A						:			int								=	use_timm_backbone
          __A						:			Tuple								=	backbone_config
          __A						:			Tuple								=	num_channels
          __A						:			Tuple								=	num_queries
          __A						:			Optional[int]								=	d_model
          __A						:			Any								=	encoder_ffn_dim
          __A						:			Tuple								=	encoder_layers
          __A						:			List[str]								=	encoder_attention_heads
          __A						:			List[str]								=	decoder_ffn_dim
          __A						:			List[str]								=	decoder_layers
          __A						:			List[str]								=	decoder_attention_heads
          __A						:			List[Any]								=	dropout
          __A						:			Union[str, Any]								=	attention_dropout
          __A						:			Optional[Any]								=	activation_dropout
          __A						:			Any								=	activation_function
          __A						:			Optional[int]								=	init_std
          __A						:			List[Any]								=	init_xavier_std
          __A						:			Any								=	encoder_layerdrop
          __A						:			Optional[Any]								=	decoder_layerdrop
          __A						:			Optional[int]								=	encoder_layers
          __A						:			int								=	auxiliary_loss
          __A						:			List[str]								=	position_embedding_type
          __A						:			Union[str, Any]								=	backbone
          __A						:			Tuple								=	use_pretrained_backbone
          __A						:			List[Any]								=	dilation
          # Hungarian matcher
          __A						:			List[Any]								=	class_cost
          __A						:			Optional[Any]								=	bbox_cost
          __A						:			Dict								=	giou_cost
          # Loss coefficients
          __A						:			int								=	mask_loss_coefficient
          __A						:			Tuple								=	dice_loss_coefficient
          __A						:			int								=	bbox_loss_coefficient
          __A						:			List[Any]								=	giou_loss_coefficient
          __A						:			int								=	eos_coefficient
          super().__init__(is_encoder_decoder=_UpperCAmelCase						,     **_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.encoder_attention_heads


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.d_model




class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	version.parse('''1.11'''							)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                  ('pixel_mask', {0: 'batch'}),
              ])


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 1e-5


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 12






'''simple docstring'''




from __future__ import annotations

from math import gcd






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			int = 2			,							__snake_case  :			int = 1			,							__snake_case  :			int = 3			,							)					->					int | None:
     # A value less than 2 can cause an infinite loop in the algorithm.
     if num < 2:
          raise ValueError('The input value cannot be less than 2'      )

     # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
     # algorithm struggles to find factors that are divisible by two.
     # As a workaround, we specifically check for two and even inputs.
     #   See: https://math.stackexchange.com/a/2856214/165820
     if num > 2 and num % 2 == 0:
          return 2

     # Pollard's Rho algorithm requires a function that returns pseudorandom
     # values between 0 <= X < ``num``.  It doesn't need to be random in the
     # sense that the output value is cryptographically secure or difficult
     # to calculate, it only needs to be random in the sense that all output
     # values should be equally likely to appear.
     # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
     # However, the success of Pollard's algorithm isn't guaranteed and is
     # determined in part by the initial seed and the chosen random function.
     # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
     # where ``C`` is a value that we can modify between each attempt.
     def rand_fn(__snake_case  :			int			,							__snake_case  :			int			,							__snake_case  :			int      ) -> int:
          return (pow(__snake_case			,							2      ) + step) % modulus

     for _ in range(__snake_case      ):
          # These track the position within the cycle detection logic.
          __A						:			int								=	seed
          __A						:			Union[str, Any]								=	seed

          while True:
               # At each iteration, the tortoise moves one step and the hare moves two.
               __A						:			List[Any]								=	rand_fn(__snake_case			,							__snake_case			,							__snake_case      )
               __A						:			Optional[Any]								=	rand_fn(__snake_case			,							__snake_case			,							__snake_case      )
               __A						:			Any								=	rand_fn(__snake_case			,							__snake_case			,							__snake_case      )

               # At some point both the tortoise and the hare will enter a cycle whose
               # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
               # the tortoise and hare will end up on the same value modulo ``p``.
               # We can detect when this happens because the position difference between
               # the tortoise and the hare will share a common divisor with ``num``.
               __A						:			Optional[int]								=	gcd(hare - tortoise			,							__snake_case      )

               if divisor == 1:
                    # No common divisor yet, just keep searching.
                    continue
               else:
                    # We found a common divisor!
                    if divisor == num:
                         # Unfortunately, the divisor is ``num`` itself and is useless.
                         break
                    else:
                         # The divisor is a nontrivial factor of ``num``!
                         return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
          __A						:			Union[str, Any]								=	hare

          # The new step value for the random function can just be incremented.
          # At first the results will be similar to what the old function would
          # have produced, but the value will quickly diverge after a bit.
          step += 1

     # We haven't found a divisor within the requested number of attempts.
     # We were unlucky or ``num`` itself is actually prime.
     return None


if __name__ == "__main__":
  import argparse

  lowercase__   :				str      =   argparse.ArgumentParser()
  parser.add_argument(
      '''num''',
      type=int,
      help='''The value to find a divisor of''',
  )
  parser.add_argument(
      '''--attempts''',
      type=int,
      default=3,
      help='''The number of attempts before giving up''',
  )
  lowercase__   :				Optional[int]      =   parser.parse_args()

  lowercase__   :				int      =   pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(f"""{args.num} is probably prime""")
  else:
    lowercase__   :				List[str]      =   args.num // divisor
    print(f"""{args.num} = {divisor} * {quotient}""")