
import copy

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


lowercase  : Optional[Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{
    '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 __snake_case		(				UpperCamelCase__					):
    _a		:						Union[str, Any]=    """dpt"""
    def __init__(  self		,snake_case=768		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.02		,snake_case=1e-12		,snake_case=384		,snake_case=16		,snake_case=3		,snake_case=False		,snake_case=True		,snake_case=[2, 5, 8, 11]		,snake_case="project"		,snake_case=[4, 2, 1, 0.5]		,snake_case=[96, 192, 384, 768]		,snake_case=256		,snake_case=-1		,snake_case=False		,snake_case=True		,snake_case=0.4		,snake_case=255		,snake_case=0.1		,snake_case=[1, 1024, 24, 24]		,snake_case=[0, 1]		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**__a				)

           lowercase							:					str	     =				hidden_size
           lowercase							:					Optional[Any]	     =				is_hybrid

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

                  lowercase							:					Dict	     =				backbone_featmap_shape
                  lowercase							:					List[Any]	     =				neck_ignore_stages

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

           lowercase							:					Optional[Any]	     =				num_hidden_layers
           lowercase							:					List[str]	     =				num_attention_heads
           lowercase							:					Union[str, Any]	     =				intermediate_size
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Union[str, Any]	     =				hidden_dropout_prob
           lowercase							:					Union[str, Any]	     =				attention_probs_dropout_prob
           lowercase							:					Tuple	     =				initializer_range
           lowercase							:					str	     =				layer_norm_eps
           lowercase							:					int	     =				image_size
           lowercase							:					int	     =				patch_size
           lowercase							:					Optional[Any]	     =				num_channels
           lowercase							:					Optional[int]	     =				qkv_bias
           lowercase							:					Optional[Any]	     =				backbone_out_indices
           if readout_type not in ["ignore", "add", "project"]:
                  raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""				)
           lowercase							:					List[str]	     =				readout_type
           lowercase							:					Union[str, Any]	     =				reassemble_factors
           lowercase							:					Any	     =				neck_hidden_sizes
           lowercase							:					Union[str, Any]	     =				fusion_hidden_size
           lowercase							:					Union[str, Any]	     =				head_in_index
           lowercase							:					List[Any]	     =				use_batch_norm_in_fusion_residual
           # auxiliary head attributes (semantic segmentation)
           lowercase							:					Dict	     =				use_auxiliary_head
           lowercase							:					Optional[Any]	     =				auxiliary_loss_weight
           lowercase							:					List[Any]	     =				semantic_loss_ignore_index
           lowercase							:					str	     =				semantic_classifier_dropout
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				copy.deepcopy(self.__dict__				)

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

           lowercase							:					Union[str, Any]	     =				self.__class__.model_type
           return output


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
