

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


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """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	:    str			,						lowerCamelCase__	:    Union[str, Any]=None			,						**lowerCamelCase__	:    List[str]       ) ->			Union[str, Any]:
         """simple docstring"""





         logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''       )
         A_							       =		model
         A_							       =		kwargs.get('''model_save_dir'''			,						lowerCamelCase__       )
         A_							       =		kwargs.get('''latest_model_name'''			,						lowerCamelCase__       )

   def __call__(				self	:    List[str]			,						**lowerCamelCase__	:    Optional[int]       ) ->			Optional[Any]:
         """simple docstring"""





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

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    Union[str, Path]			,						lowerCamelCase__	:    Any=None			,						lowerCamelCase__	:    Optional[int]=None       ) ->			int:
         """simple docstring"""





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

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

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    Union[str, Path]			,						lowerCamelCase__	:    Optional[str] = None			,						**lowerCamelCase__	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, os.PathLike]			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			Any:
         """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     UpperCamelCase					(				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__	:    int			,						) ->			Optional[Any]:
         """simple docstring"""





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





   @classmethod
   def     UpperCamelCase					(				cls	:    List[str]			,						lowerCamelCase__	:    Union[str, Path]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[str] = None			,						lowerCamelCase__	:    Optional[str] = None			,						**lowerCamelCase__	:    Union[str, Any]			,						) ->			Any:
         """simple docstring"""





         A_							       =		None
         if len(str(lowerCamelCase__       ).split('''@'''       )       ) == 2:
               A_       ,A_							       =		model_id.split('''@'''       )

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








import random

class    _lowercase				:

   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    str       ) ->			tuple[list[int], list[int]]:
         """simple docstring"""





         A_							       =		[ord(lowerCamelCase__       ) for i in text]
         A_							       =		[]
         A_							       =		[]
         for i in plain:
               A_							       =		random.randint(1			,						3_0_0       )
               A_							       =		(i + k) * k
               cipher.append(lowerCamelCase__       )
               key.append(lowerCamelCase__       )
         return cipher, key





   @staticmethod
   def     UpperCamelCase					(				lowerCamelCase__	:    list[int]			,						lowerCamelCase__	:    list[int]       ) ->			str:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               A_							       =		int((cipher[i] - (key[i]) ** 2) / key[i]       )
               plain.append(chr(lowerCamelCase__       )       )
         return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      __lowercase      ,				__lowercase				      =						Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))





