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


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		{
    """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    A_ :

      def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
       logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."       )
       __a       =  model
       __a       =  kwargs.get("model_save_dir"     ,							_UpperCAmelCase       )
       __a       =  kwargs.get("latest_model_name"     ,							_UpperCAmelCase       )

      def __call__(		self :      str     ,							**__SCREAMING_SNAKE_CASE :      Tuple       ):
       __a       =  {k: np.array(_UpperCAmelCase       ) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase     ,							_UpperCAmelCase       )

      @staticmethod
      def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None       ):
       if provider is None:
        logger.info("No onnxruntime provider specified, using CPUExecutionProvider"       )
        __a       =  '''CPUExecutionProvider'''

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

      def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[str] = None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
       __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(_UpperCAmelCase       ).joinpath(_UpperCAmelCase       )
       try:
        shutil.copyfile(_UpperCAmelCase     ,							_UpperCAmelCase       )
       except shutil.SameFileError:
        pass

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

      def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
       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 		_UpperCAmelCase   (		cls :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[str] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      List[Any] = False     ,							__SCREAMING_SNAKE_CASE :      Optional[Any] = None     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      Any = None     ,							__SCREAMING_SNAKE_CASE :      Dict = None     ,							**__SCREAMING_SNAKE_CASE :      Tuple     ,							):
       __a       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(_UpperCAmelCase       ):
        __a       =  OnnxRuntimeModel.load_model(
            os.path.join(_UpperCAmelCase     ,							_UpperCAmelCase       )     ,							provider=_UpperCAmelCase     ,							sess_options=_UpperCAmelCase       )
        __a       =  Path(_UpperCAmelCase       )
       # load model from hub
       else:
        # download model
        __a       =  hf_hub_download(
            repo_id=_UpperCAmelCase     ,							filename=_UpperCAmelCase     ,							use_auth_token=_UpperCAmelCase     ,							revision=_UpperCAmelCase     ,							cache_dir=_UpperCAmelCase     ,							force_download=_UpperCAmelCase     ,							)
        __a       =  Path(_UpperCAmelCase       ).parent
        __a       =  Path(_UpperCAmelCase       ).name
        __a       =  OnnxRuntimeModel.load_model(_UpperCAmelCase     ,							provider=_UpperCAmelCase     ,							sess_options=_UpperCAmelCase       )
       return cls(model=_UpperCAmelCase     ,							**_UpperCAmelCase       )

      @classmethod
      def 		_UpperCAmelCase   (		cls :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Any = True     ,							__SCREAMING_SNAKE_CASE :      str = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]     ,							):
       __a       =  None
       if len(str(_UpperCAmelCase       ).split("@"       )       ) == 2:
        __a       =  model_id.split("@"       )

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



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


