

'''simple docstring'''
from __future__ import annotations


def        _lowerCAmelCase					(						_UpperCamelCase						:     list[int]							, _UpperCamelCase						:     list[int]							, _UpperCamelCase						:     int				)      ->			tuple[float, list[float]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =list(range(len(_UpperCamelCase				)				)				)
     _SCREAMING_SNAKE_CASE							    =[v / w for v, w in zip(_UpperCamelCase							, _UpperCamelCase				)]
     index.sort(key=lambda _UpperCamelCase				: ratio[i]							, reverse=_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =[0] * len(_UpperCamelCase				)
     for i in index:
          if weight[i] <= capacity:
               _SCREAMING_SNAKE_CASE							    =1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _SCREAMING_SNAKE_CASE							    =capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()






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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    '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     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[Any]    ,       ):
             if os.path.isfile(UpperCamelCase_   ):
                    logger.error(F'Provided path ({save_directory}) should be a directory, not a file'   )
                    return

             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )

             # saving model weights/files
             self._save_pretrained(UpperCamelCase_    ,       **UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: str    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[Union[bool, str, None]] = None    ,       UpperCamelCase_: Optional[Union[str, None]] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional["ort.SessionOptions"] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

             return cls._from_pretrained(
                 model_id=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

