





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			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """pixel_values"""
       lowerCAmelCase__      		=     False
       lowerCAmelCase__      		=     TimmBackboneConfig




       def __init__(  self :    Optional[Any]							,				A :    Tuple							,				**A :    Optional[Any]		):
         requires_backends(self							,				"""timm"""		)
         super().__init__(A		)
         __snake_case:						int   =						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(A							,				"""out_features"""		) and config.out_features is not None:
           raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""		)

         __snake_case:						List[str]   =						getattr(A							,				"""use_pretrained_backbone"""							,				A		)
         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.
         __snake_case:						Union[str, Any]   =						config.out_indices if getattr(A							,				"""out_indices"""							,				A		) is not None else (-1,)

         __snake_case:						int   =						timm.create_model(
             config.backbone							,				pretrained=A							,				features_only=config.features_only							,				in_chans=config.num_channels							,				out_indices=A							,				**A							,				)
         # 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.
         __snake_case:						Tuple   =						self._backbone.return_layers
         __snake_case:						List[Any]   =						{layer["""module"""]: str(A		) for i, layer in enumerate(self._backbone.feature_info.info		)}
         super()._init_backbone(A		)




       @classmethod
       def 							UpperCAmelCase__ (  cls :    Tuple							,				A :    Tuple							,				*A :    List[str]							,				**A :    Optional[Any]		):
         requires_backends(cls							,				["""vision""", """timm"""]		)
         from ...models.timm_backbone import TimmBackboneConfig

         __snake_case:						Any   =						kwargs.pop("""config"""							,				TimmBackboneConfig()		)

         __snake_case:						List[Any]   =						kwargs.pop("""use_timm_backbone"""							,				A		)
         if not use_timm:
           raise ValueError("""use_timm_backbone must be True for timm backbones"""		)

         __snake_case:						Optional[int]   =						kwargs.pop("""num_channels"""							,				config.num_channels		)
         __snake_case:						Tuple   =						kwargs.pop("""features_only"""							,				config.features_only		)
         __snake_case:						Optional[int]   =						kwargs.pop("""use_pretrained_backbone"""							,				config.use_pretrained_backbone		)
         __snake_case:						Dict   =						kwargs.pop("""out_indices"""							,				config.out_indices		)
         __snake_case:						Dict   =						TimmBackboneConfig(
             backbone=A							,				num_channels=A							,				features_only=A							,				use_pretrained_backbone=A							,				out_indices=A							,				)
         return super()._from_config(A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Union[str, Any]		):
         pass






       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]							,				A :    Optional[int]=None							,				A :    Tuple=None							,				A :    str=None							,				**A :    Any		):
         __snake_case:						Union[str, Any]   =						return_dict if return_dict is not None else self.config.use_return_dict
         __snake_case:						Dict   =						(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __snake_case:						Optional[int]   =						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
           __snake_case:						Optional[int]   =						self._all_layers
           __snake_case:						str   =						self._backbone(A							,				**A		)
           __snake_case:						List[Any]   =						self._return_layers
           __snake_case:						List[Any]   =						tuple(hidden_states[i] for i in self.out_indices		)
         else:
           __snake_case:						Any   =						self._backbone(A							,				**A		)
           __snake_case:						Tuple   =						None

         __snake_case:						Optional[Any]   =						tuple(A		)
         __snake_case:						List[str]   =						tuple(A		) if hidden_states is not None else None

         if not return_dict:
           __snake_case:						Any   =						(feature_maps,)
           if output_hidden_states:
             __snake_case:						Optional[int]   =						output + (hidden_states,)
           return output

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













from __future__ import annotations

import typing
from collections import Counter







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    typing.Counter[int]:
  __snake_case:						typing.Counter[int]   =						Counter()
  for base in range(1					,					max_perimeter + 1):
    for perpendicular in range(SCREAMING_SNAKE_CASE__					,					max_perimeter + 1):
      __snake_case:						Dict   =						(base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(SCREAMING_SNAKE_CASE__):
        __snake_case:						Any   =						int(base + perpendicular + hypotenuse)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1000)   ->    int:
  __snake_case:						List[str]   =						pythagorean_triple(SCREAMING_SNAKE_CASE__)
  return triplets.most_common(1)[0][0]


if __name__ == "__main__":
      print(f'Perimeter {solution()} has maximum solutions')






