
'''simple docstring'''






import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
  from ... import PreTrainedTokenizerBase, TensorType

UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       lowercase__ ):
    lowerCAmelCase_				    =		'vision-encoder-decoder'
    lowerCAmelCase_				    =		True



    def __init__(	self      :       int,**__A      :       List[Any]     ):
          super().__init__(**__A     )
          if "encoder" not in kwargs or "decoder" not in kwargs:
                raise ValueError(
                    f'A configuraton of type {self.model_type} cannot be instantiated because '
                    f'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'     )

          _lowerCamelCase				:   Optional[Any]								=   kwargs.pop("encoder"     )
          _lowerCamelCase				:   Dict								=   encoder_config.pop("model_type"     )
          _lowerCamelCase				:   Any								=   kwargs.pop("decoder"     )
          _lowerCamelCase				:   Optional[int]								=   decoder_config.pop("model_type"     )

          _lowerCamelCase				:   Optional[int]								=   AutoConfig.for_model(__A,**__A     )
          _lowerCamelCase				:   Dict								=   AutoConfig.for_model(__A,**__A     )
          _lowerCamelCase				:   str								=   True



    @classmethod
    def   lowerCamelCase_  (	cls      :       Any,__A      :       int,__A      :       Dict,**__A      :       int     ):
          logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
          _lowerCamelCase				:   List[str]								=   True
          _lowerCamelCase				:   int								=   True

          return cls(encoder=encoder_config.to_dict(),decoder=decoder_config.to_dict(),**__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   List[Any]								=   copy.deepcopy(self.__dict__     )
          _lowerCamelCase				:   List[Any]								=   self.encoder.to_dict()
          _lowerCamelCase				:   str								=   self.decoder.to_dict()
          _lowerCamelCase				:   List[str]								=   self.__class__.model_type
          return output


class  UpperCAmelCase__						(       lowercase__ ):
    lowerCAmelCase_				    =		version.parse('1.11' )



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]     )



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return 1e-4



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}     )


class  UpperCAmelCase__						(       lowercase__ ):



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Optional[int]								=   OrderedDict()
          _lowerCamelCase				:   Any								=   {0: "batch", 1: "past_decoder_sequence + sequence"}
          _lowerCamelCase				:   str								=   {0: "batch", 1: "past_decoder_sequence + sequence"}
          _lowerCamelCase				:   List[Any]								=   {0: "batch", 1: "encoder_sequence"}

          return common_inputs



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[str],__A      :       int = -1,__A      :       Dict = -1,__A      :       str = False,__A      :       Optional[Any] = None,):
          import torch

          _lowerCamelCase				:   Any								=   OrderedDict()

          _lowerCamelCase				:   List[Any]								=   super().generate_dummy_inputs(
              __A,batch_size=__A,seq_length=__A,is_pair=__A,framework=__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   dummy_input["input_ids"].shape
          _lowerCamelCase				:   Dict								=   (batch, encoder_sequence, self._config.encoder_hidden_size)
          _lowerCamelCase				:   List[Any]								=   dummy_input.pop("input_ids"     )
          _lowerCamelCase				:   int								=   dummy_input.pop("attention_mask"     )
          _lowerCamelCase				:   int								=   torch.zeros(__A     )

          return common_inputs


class  UpperCAmelCase__						(       lowercase__ ):



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          pass



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Dict     ):
          return VisionEncoderDecoderEncoderOnnxConfig(__A     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Union[str, Any],__A      :       Optional[int],__A      :       Optional[Any] = "default"     ):
          _lowerCamelCase				:   Optional[Any]								=   encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(__A,__A     )

'''simple docstring'''






from math import sqrt







def 		A_       (	_lowerCAmelCase   :    int = 1000000							):



      """simple docstring"""




      _lowerCamelCase				:   int								=   0
      _lowerCamelCase				:   int								=   0
      _lowerCamelCase				:   int

      while num_cuboids <= limit:
            max_cuboid_size += 1
            for sum_shortest_sides in range(2				,	2 * max_cuboid_size + 1							):
                  if sqrt(sum_shortest_sides**2 + max_cuboid_size**2							).is_integer():
                        num_cuboids += (
                            min(_lowerCAmelCase				,	sum_shortest_sides // 2							)
                            - max(1				,	sum_shortest_sides - max_cuboid_size							)
                            + 1
                        )

      return max_cuboid_size


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