
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor

class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase					):
    _a		:						str=    "pixel_values"
    _a		:						Dict=    False
    _a		:						Optional[int]=    TimmBackboneConfig
    def __init__(  self		,snake_case		,**snake_case				):



           '''simple docstring'''







           requires_backends(self		,"""timm"""				)
           super().__init__(snake_case				)
           lowercase							:					Any	     =				config

           if config.backbone is None:
                  raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""				)

           if config.backbone not in timm.list_models():
                  raise ValueError(f"backbone {config.backbone} is not supported by timm."				)

           if hasattr(snake_case		,"""out_features"""				) and config.out_features is not None:
                  raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""				)

           lowercase							:					List[Any]	     =				getattr(snake_case		,"""use_pretrained_backbone"""		,snake_case				)
           if pretrained is None:
                  raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""				)

           # We just take the final layer by default. This matches the default for the transformers models.
           lowercase							:					Union[str, Any]	     =				config.out_indices if getattr(snake_case		,"""out_indices"""		,snake_case				) is not None else (-1,)

           lowercase							:					str	     =				timm.create_model(
               config.backbone		,pretrained=snake_case		,features_only=config.features_only		,in_chans=config.num_channels		,out_indices=snake_case		,**snake_case		,)
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           lowercase							:					List[str]	     =				self._backbone.return_layers
           lowercase							:					str	     =				{layer["""module"""]: str(snake_case				) for i, layer in enumerate(self._backbone.feature_info.info				)}
           super()._init_backbone(snake_case				)
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls		,snake_case		,*snake_case		,**snake_case				):



           '''simple docstring'''







           requires_backends(cls		,["""vision""", """timm"""]				)
           from ...models.timm_backbone import TimmBackboneConfig

           lowercase							:					Optional[Any]	     =				kwargs.pop("""config"""		,TimmBackboneConfig()				)

           lowercase							:					int	     =				kwargs.pop("""use_timm_backbone"""		,snake_case				)
           if not use_timm:
                  raise ValueError("""use_timm_backbone must be True for timm backbones"""				)

           lowercase							:					List[str]	     =				kwargs.pop("""num_channels"""		,config.num_channels				)
           lowercase							:					Union[str, Any]	     =				kwargs.pop("""features_only"""		,config.features_only				)
           lowercase							:					Dict	     =				kwargs.pop("""use_pretrained_backbone"""		,config.use_pretrained_backbone				)
           lowercase							:					Optional[Any]	     =				kwargs.pop("""out_indices"""		,config.out_indices				)
           lowercase							:					List[str]	     =				TimmBackboneConfig(
               backbone=snake_case		,num_channels=snake_case		,features_only=snake_case		,use_pretrained_backbone=snake_case		,out_indices=snake_case		,)
           return super()._from_config(snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=None		,snake_case=None		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				return_dict if return_dict is not None else self.config.use_return_dict
           lowercase							:					Tuple	     =				(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:					List[str]	     =				output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                  raise ValueError("""Cannot output attentions for timm backbones at the moment"""				)

           if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  lowercase							:					str	     =				self._all_layers
                  lowercase							:					str	     =				self._backbone(snake_case		,**snake_case				)
                  lowercase							:					Optional[Any]	     =				self._return_layers
                  lowercase							:					List[Any]	     =				tuple(hidden_states[i] for i in self.out_indices				)
           else:
                  lowercase							:					List[Any]	     =				self._backbone(snake_case		,**snake_case				)
                  lowercase							:					Any	     =				None

           lowercase							:					Tuple	     =				tuple(snake_case				)
           lowercase							:					Any	     =				tuple(snake_case				) if hidden_states is not None else None

           if not return_dict:
                  lowercase							:					Tuple	     =				(feature_maps,)
                  if output_hidden_states:
                         lowercase							:					Dict	     =				output + (hidden_states,)
                  return output

           return BackboneOutput(feature_maps=snake_case		,hidden_states=snake_case		,attentions=snake_case				)


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]}.''')
