




"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class      UpperCAmelCase_  (						A_							):
				lowercase__						 =				'''dpt'''




				def __init__(   self						:	List[Any]     ,			snake_case_						:	Union[str, Any]=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	Tuple=0.0     ,			snake_case_						:	int=0.0     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Union[str, Any]=1e-12     ,			snake_case_						:	Tuple=384     ,			snake_case_						:	Tuple=16     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Dict=False     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=[2, 5, 8, 11]     ,			snake_case_						:	Union[str, Any]="project"     ,			snake_case_						:	Union[str, Any]=[4, 2, 1, 0.5]     ,			snake_case_						:	List[str]=[96, 192, 384, 768]     ,			snake_case_						:	int=256     ,			snake_case_						:	Tuple=-1     ,			snake_case_						:	List[str]=False     ,			snake_case_						:	int=True     ,			snake_case_						:	List[Any]=0.4     ,			snake_case_						:	Optional[Any]=255     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	List[str]=[1, 1_024, 24, 24]     ,			snake_case_						:	Union[str, Any]=[0, 1]     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]     ,			)				->							Optional[int]:




					'''simple docstring'''

					super().__init__(**snake_case_				)

					A__	=    hidden_size
					A__	=    is_hybrid

					if self.is_hybrid:
						if backbone_config is None:
							logger.info("Initializing the config with a `BiT` backbone."				)
							A__	=    {
							    "global_padding": "same",
							    "layer_type": "bottleneck",
							    "depths": [3, 4, 9],
							    "out_features": ["stage1", "stage2", "stage3"],
							    "embedding_dynamic_padding": True,
							}
							A__	=    BitConfig(**snake_case_				)
						elif isinstance(snake_case_     ,			snake_case_				):
							logger.info("Initializing the config with a `BiT` backbone."				)
							A__	=    BitConfig(**snake_case_				)
						elif isinstance(snake_case_     ,			snake_case_				):
							A__	=    backbone_config
						else:
							raise ValueError(
							    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""				)

						A__	=    backbone_featmap_shape
						A__	=    neck_ignore_stages

						if readout_type != "project":
							raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."				)
					else:
						A__	=    None
						A__	=    None
						A__	=    []

					A__	=    num_hidden_layers
					A__	=    num_attention_heads
					A__	=    intermediate_size
					A__	=    hidden_act
					A__	=    hidden_dropout_prob
					A__	=    attention_probs_dropout_prob
					A__	=    initializer_range
					A__	=    layer_norm_eps
					A__	=    image_size
					A__	=    patch_size
					A__	=    num_channels
					A__	=    qkv_bias
					A__	=    backbone_out_indices
					if readout_type not in ["ignore", "add", "project"]:
						raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"				)
					A__	=    readout_type
					A__	=    reassemble_factors
					A__	=    neck_hidden_sizes
					A__	=    fusion_hidden_size
					A__	=    head_in_index
					A__	=    use_batch_norm_in_fusion_residual
					# auxiliary head attributes (semantic segmentation)
					A__	=    use_auxiliary_head
					A__	=    auxiliary_loss_weight
					A__	=    semantic_loss_ignore_index
					A__	=    semantic_classifier_dropout




				def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




					'''simple docstring'''

					A__	=    copy.deepcopy(self.__dict__				)

					if output["backbone_config"] is not None:
						A__	=    self.backbone_config.to_dict()

					A__	=    self.__class__.model_type
					return output











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

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

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

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	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)))





