



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

__a     =			logging.get_logger(__name__)






class   __lowercase				(  lowercase__  ):
      UpperCamelCase								=       'vision-encoder-decoder'
      UpperCamelCase								=       True





      def __init__(		self						:					Optional[int]					,       **__lowerCamelCase						:					Dict       )    ->   str:






         """simple docstring"""







         super().__init__(**__lowerCamelCase       )
         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}"""       )

         UpperCAmelCase							=     kwargs.pop("""encoder"""       )
         UpperCAmelCase							=     encoder_config.pop("""model_type"""       )
         UpperCAmelCase							=     kwargs.pop("""decoder"""       )
         UpperCAmelCase							=     decoder_config.pop("""model_type"""       )

         UpperCAmelCase							=     AutoConfig.for_model(__lowerCamelCase					,       **__lowerCamelCase       )
         UpperCAmelCase							=     AutoConfig.for_model(__lowerCamelCase					,       **__lowerCamelCase       )
         UpperCAmelCase							=     True





      @classmethod
      def        _lowercase				(		cls						:					str					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					Union[str, Any]					,       **__lowerCamelCase						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""       )
         UpperCAmelCase							=     True
         UpperCAmelCase							=     True

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





      def        _lowercase				(		self						:					str       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     copy.deepcopy(self.__dict__       )
         UpperCAmelCase							=     self.encoder.to_dict()
         UpperCAmelCase							=     self.decoder.to_dict()
         UpperCAmelCase							=     self.__class__.model_type
         return output






class   __lowercase				(  lowercase__  ):
      UpperCamelCase								=       version.parse('''1.11'''  )





      @property
      def        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]       )





      @property
      def        _lowercase				(		self						:					List[Any]       )    ->   Optional[int]:






         """simple docstring"""







         return 1e-4





      @property
      def        _lowercase				(		self						:					Dict       )    ->   Optional[int]:






         """simple docstring"""







         return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}       )






class   __lowercase				(  lowercase__  ):





      @property
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     OrderedDict()
         UpperCAmelCase							=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
         UpperCAmelCase							=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
         UpperCAmelCase							=     {0: """batch""", 1: """encoder_sequence"""}

         return common_inputs





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					Union[str, Any]					,       __lowerCamelCase						:					Dict = -1					,       __lowerCamelCase						:					Optional[int] = -1					,       __lowerCamelCase						:					Optional[Any] = False					,       __lowerCamelCase						:					Union[str, Any] = None					,       )    ->   Tuple:






         """simple docstring"""







         import torch

         UpperCAmelCase							=     OrderedDict()

         UpperCAmelCase							=     super().generate_dummy_inputs(
             __lowerCamelCase					,       batch_size=__lowerCamelCase					,       seq_length=__lowerCamelCase					,       is_pair=__lowerCamelCase					,       framework=__lowerCamelCase       )

         UpperCAmelCase				,  UpperCAmelCase							=     dummy_input["""input_ids"""].shape
         UpperCAmelCase							=     (batch, encoder_sequence, self._config.encoder_hidden_size)
         UpperCAmelCase							=     dummy_input.pop("""input_ids"""       )
         UpperCAmelCase							=     dummy_input.pop("""attention_mask"""       )
         UpperCAmelCase							=     torch.zeros(__lowerCamelCase       )

         return common_inputs






class   __lowercase				(  lowercase__  ):





      @property
      def        _lowercase				(		self						:					List[Any]       )    ->   Optional[int]:






         """simple docstring"""







         pass





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         return VisionEncoderDecoderEncoderOnnxConfig(__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Any					,       __lowerCamelCase						:					List[str] = "default"       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(__lowerCamelCase					,       __lowerCamelCase       )







from math import sqrt
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0_0_0_0_0_0   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   UpperCAmelCase							=     42

   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() = }""")



