



import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
 import onnxruntime as ort


__a     =			logging.get_logger(__name__)

__a     =			{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}






class   __lowercase				:





      def __init__(		self						:					List[Any]					,       __lowerCamelCase						:					Tuple=None					,       **__lowerCamelCase						:					str       )    ->   int:






         """simple docstring"""







         logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""       )
         UpperCAmelCase							=     model
         UpperCAmelCase							=     kwargs.get("""model_save_dir"""					,       _lowerCamelCase       )
         UpperCAmelCase							=     kwargs.get("""latest_model_name"""					,       _lowerCamelCase       )





      def __call__(		self						:					List[Any]					,       **__lowerCamelCase						:					List[str]       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     {k: np.array(_lowerCamelCase       ) for k, v in kwargs.items()}
         return self.model.run(_lowerCamelCase					,       _lowerCamelCase       )





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					Union[str, Path]					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Tuple=None       )    ->   Tuple:






         """simple docstring"""







         if provider is None:
            logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""       )
            UpperCAmelCase							=     """CPUExecutionProvider"""

         return ort.InferenceSession(_lowerCamelCase					,       providers=[provider]					,       sess_options=_lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Union[str, Path]					,       __lowerCamelCase						:					Optional[str] = None					,       **__lowerCamelCase						:					int       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     file_name if file_name is not None else ONNX_WEIGHTS_NAME

         UpperCAmelCase							=     self.model_save_dir.joinpath(self.latest_model_name       )
         UpperCAmelCase							=     Path(_lowerCamelCase       ).joinpath(_lowerCamelCase       )
         try:
            shutil.copyfile(_lowerCamelCase					,       _lowerCamelCase       )
         except shutil.SameFileError:
            pass

         # copy external weights (for models >2GB)
         UpperCAmelCase							=     self.model_save_dir.joinpath(_lowerCamelCase       )
         if src_path.exists():
            UpperCAmelCase							=     Path(_lowerCamelCase       ).joinpath(_lowerCamelCase       )
            try:
               shutil.copyfile(_lowerCamelCase					,       _lowerCamelCase       )
            except shutil.SameFileError:
               pass





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					Union[str, os.PathLike]					,       **__lowerCamelCase						:					Dict					,       )    ->   str:






         """simple docstring"""







         if os.path.isfile(_lowerCamelCase       ):
            logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""       )
            return

         os.makedirs(_lowerCamelCase					,       exist_ok=_lowerCamelCase       )

         # saving model weights/files
         self._save_pretrained(_lowerCamelCase					,       **_lowerCamelCase       )





      @classmethod
      def        _lowercase				(		cls						:					Optional[Any]					,       __lowerCamelCase						:					Union[str, Path]					,       __lowerCamelCase						:					Optional[Union[bool, str, None]] = None					,       __lowerCamelCase						:					Optional[Union[str, None]] = None					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					Optional[str] = None					,       __lowerCamelCase						:					Optional[str] = None					,       __lowerCamelCase						:					Optional[str] = None					,       __lowerCamelCase						:					Optional["ort.SessionOptions"] = None					,       **__lowerCamelCase						:					Any					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCamelCase       ):
            UpperCAmelCase							=     OnnxRuntimeModel.load_model(
                os.path.join(_lowerCamelCase					,       _lowerCamelCase       )					,       provider=_lowerCamelCase					,       sess_options=_lowerCamelCase       )
            UpperCAmelCase							=     Path(_lowerCamelCase       )
         # load model from hub
         else:
            # download model
            UpperCAmelCase							=     hf_hub_download(
                repo_id=_lowerCamelCase					,       filename=_lowerCamelCase					,       use_auth_token=_lowerCamelCase					,       revision=_lowerCamelCase					,       cache_dir=_lowerCamelCase					,       force_download=_lowerCamelCase					,       )
            UpperCAmelCase							=     Path(_lowerCamelCase       ).parent
            UpperCAmelCase							=     Path(_lowerCamelCase       ).name
            UpperCAmelCase							=     OnnxRuntimeModel.load_model(_lowerCamelCase					,       provider=_lowerCamelCase					,       sess_options=_lowerCamelCase       )
         return cls(model=_lowerCamelCase					,       **_lowerCamelCase       )





      @classmethod
      def        _lowercase				(		cls						:					int					,       __lowerCamelCase						:					Union[str, Path]					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Optional[str] = None					,       __lowerCamelCase						:					Optional[str] = None					,       **__lowerCamelCase						:					Optional[int]					,       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     None
         if len(str(_lowerCamelCase       ).split("""@"""       )       ) == 2:
            UpperCAmelCase				,  UpperCAmelCase							=     model_id.split("""@"""       )

         return cls._from_pretrained(
             model_id=_lowerCamelCase					,       revision=_lowerCamelCase					,       cache_dir=_lowerCamelCase					,       force_download=_lowerCamelCase					,       use_auth_token=_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() = }""")



