
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   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	"vision-encoder-decoder"
     UpperCAmelCase__       :							Any     =	True





     def __init__(self   :					Any       ,					**_A   :					int     )		->		Tuple:
         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}'     )

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

         snake_case				  =      AutoConfig.for_model(_A       ,					**_A     )
         snake_case				  =      AutoConfig.for_model(_A       ,					**_A     )
         snake_case				  =      True





     @classmethod
     def 	UpperCAmelCase(cls   :					List[Any]       ,					_A   :					PretrainedConfig       ,					_A   :					PretrainedConfig       ,					**_A   :					str     )		->		PretrainedConfig:
         logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
         snake_case				  =      True
         snake_case				  =      True

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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		str:
         snake_case				  =      copy.deepcopy(self.__dict__     )
         snake_case				  =      self.encoder.to_dict()
         snake_case				  =      self.decoder.to_dict()
         snake_case				  =      self.__class__.model_type
         return output







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							str     =	version.parse("1.11"						)





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]     )





     @property
     def 	UpperCAmelCase(self   :					List[Any]     )		->		float:
         return 1E-4





     @property
     def 	UpperCAmelCase(self   :					str     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}     )







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		Mapping[str, Mapping[int, str]]:
         snake_case				  =      OrderedDict()
         snake_case				  =      {0: "batch", 1: "past_decoder_sequence + sequence"}
         snake_case				  =      {0: "batch", 1: "past_decoder_sequence + sequence"}
         snake_case				  =      {0: "batch", 1: "encoder_sequence"}

         return common_inputs





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					"PreTrainedTokenizerBase"       ,					_A   :					int = -1       ,					_A   :					int = -1       ,					_A   :					bool = False       ,					_A   :					Optional["TensorType"] = None       ,					)		->		Mapping[str, Any]:
         import torch

         snake_case				  =      OrderedDict()

         snake_case				  =      super().generate_dummy_inputs(
             _A       ,					batch_size=_A       ,					seq_length=_A       ,					is_pair=_A       ,					framework=_A     )

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

         return common_inputs







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					Tuple     )		->		None:
         pass





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					PretrainedConfig     )		->		OnnxConfig:
         return VisionEncoderDecoderEncoderOnnxConfig(_A     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					PretrainedConfig       ,					_A   :					PretrainedConfig       ,					_A   :					str = "default"     )		->		OnnxConfig:
         snake_case				  =      encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(_A       ,					_A     )



def 		lowercase_    (     A__ = 1000	)							->							int:
    """simple docstring"""

    snake_case				  =      -1
    snake_case				  =      0
    for a in range(1				,	n // 3	):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        snake_case				  =      (n * n - 2 * a * n) // (2 * n - 2 * a)
        snake_case				  =      n - a - b
        if c * c == (a * a + b * b):
            snake_case				  =      a * b * c
            if candidate >= product:
                snake_case				  =      candidate
    return product


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

