

'''simple docstring'''

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 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"pixel_values"
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			TimmBackboneConfig







       def __init__(				self      :			Any    ,   __lowercase      :			Optional[int]    ,   **__lowercase      :			Tuple   ):







            """simple docstring"""







            requires_backends(self    ,   'timm'   )
            super().__init__(__lowercase   )
            __lowercase      		=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(__lowercase    ,   'out_features'   ) and config.out_features is not None:
                 raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'   )

            __lowercase      		=getattr(__lowercase    ,   'use_pretrained_backbone'    ,   __lowercase   )
            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      		=config.out_indices if getattr(__lowercase    ,   'out_indices'    ,   __lowercase   ) is not None else (-1,)

            __lowercase      		=timm.create_model(
                config.backbone    ,   pretrained=__lowercase    ,   features_only=config.features_only    ,   in_chans=config.num_channels    ,   out_indices=__lowercase    ,   **__lowercase    ,   )
            # 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      		=self._backbone.return_layers
            __lowercase      		={layer['module']: str(__lowercase   ) for i, layer in enumerate(self._backbone.feature_info.info   )}
            super()._init_backbone(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			List[str]    ,   __lowercase      :			Any    ,   *__lowercase      :			Any    ,   **__lowercase      :			Any   ):







            """simple docstring"""







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

            __lowercase      		=kwargs.pop('config'    ,   TimmBackboneConfig()   )

            __lowercase      		=kwargs.pop('use_timm_backbone'    ,   __lowercase   )
            if not use_timm:
                 raise ValueError('use_timm_backbone must be True for timm backbones'   )

            __lowercase      		=kwargs.pop('num_channels'    ,   config.num_channels   )
            __lowercase      		=kwargs.pop('features_only'    ,   config.features_only   )
            __lowercase      		=kwargs.pop('use_pretrained_backbone'    ,   config.use_pretrained_backbone   )
            __lowercase      		=kwargs.pop('out_indices'    ,   config.out_indices   )
            __lowercase      		=TimmBackboneConfig(
                backbone=__lowercase    ,   num_channels=__lowercase    ,   features_only=__lowercase    ,   use_pretrained_backbone=__lowercase    ,   out_indices=__lowercase    ,   )
            return super()._from_config(__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Dict   ):







            """simple docstring"""







            pass







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Any=None    ,   __lowercase      :			Optional[Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   **__lowercase      :			int   ):







            """simple docstring"""







            __lowercase      		=return_dict if return_dict is not None else self.config.use_return_dict
            __lowercase      		=(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __lowercase      		=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      		=self._all_layers
                 __lowercase      		=self._backbone(__lowercase    ,   **__lowercase   )
                 __lowercase      		=self._return_layers
                 __lowercase      		=tuple(hidden_states[i] for i in self.out_indices   )
            else:
                 __lowercase      		=self._backbone(__lowercase    ,   **__lowercase   )
                 __lowercase      		=None

            __lowercase      		=tuple(__lowercase   )
            __lowercase      		=tuple(__lowercase   ) if hidden_states is not None else None

            if not return_dict:
                 __lowercase      		=(feature_maps,)
                 if output_hidden_states:
                      __lowercase      		=output + (hidden_states,)
                 return output

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





'''simple docstring'''

from collections import defaultdict
from math import gcd

def 					__UpperCamelCase				(						lowercase__    :	int = 1_50_00_00			):






     '''simple docstring'''





     __lowercase      		=defaultdict(lowercase__			)
     __lowercase      		=2
     while 2 * euclid_m * (euclid_m + 1) <= limit:
          for euclid_n in range((euclid_m % 2) + 1,       lowercase__,       2			):
               if gcd(lowercase__,       lowercase__			) > 1:
                    continue
               __lowercase      		=2 * euclid_m * (euclid_m + euclid_n)
               for perimeter in range(lowercase__,       limit + 1,       lowercase__			):
                    frequencies[perimeter] += 1
          euclid_m += 1
     return sum(1 for frequency in frequencies.values() if frequency == 1			)


if __name__ == "__main__":
       print(F'''{solution() = }''')


