






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__		:							Optional[Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Optional[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 				UpperCAmelCase_  :




     def __init__(				self	,						_lowerCAmelCase=None	,						**_lowerCAmelCase		):
          logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""		)
          UpperCAmelCase__      :  Optional[int]				      =			model
          UpperCAmelCase__      :  Any				      =			kwargs.get("""model_save_dir"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			kwargs.get("""latest_model_name"""	,						_lowerCAmelCase		)




     def __call__(				self	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			{k: np.array(_lowerCAmelCase		) for k, v in kwargs.items()}
          return self.model.run(_lowerCAmelCase	,						_lowerCAmelCase		)




     @staticmethod
     def   __UpperCAmelCase  (				_lowerCAmelCase	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None		):
          if provider is None:
               logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""		)
               UpperCAmelCase__      :  Dict				      =			"""CPUExecutionProvider"""

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			file_name if file_name is not None else ONNX_WEIGHTS_NAME

          UpperCAmelCase__      :  Optional[Any]				      =			self.model_save_dir.joinpath(self.latest_model_name		)
          UpperCAmelCase__      :  Tuple				      =			Path(_lowerCAmelCase		).joinpath(_lowerCAmelCase		)
          try:
               shutil.copyfile(_lowerCAmelCase	,						_lowerCAmelCase		)
          except shutil.SameFileError:
               pass

          # copy external weights (for models >2GB)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.model_save_dir.joinpath(_lowerCAmelCase		)
          if src_path.exists():
               UpperCAmelCase__      :  str				      =			Path(_lowerCAmelCase		).joinpath(_lowerCAmelCase		)
               try:
                    shutil.copyfile(_lowerCAmelCase	,						_lowerCAmelCase		)
               except shutil.SameFileError:
                    pass




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




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase	,						_lowerCAmelCase = True	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  int				      =			None
          if len(str(_lowerCAmelCase		).split("""@"""		)		) == 2:
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Union[str, Any]				      =			model_id.split("""@"""		)

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










'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = " "							):




     '''simple docstring'''





     __A	:       List[str]      =							[]

     __A	:       Tuple      =							0
     for index, char in enumerate(SCREAMING_SNAKE_CASE							):
          if char == separator:
               split_words.append(string[last_index:index]							)
               __A	:       Union[str, Any]      =							index + 1
          elif index + 1 == len(SCREAMING_SNAKE_CASE							):
               split_words.append(string[last_index : index + 1]							)
     return split_words


if __name__ == "__main__":
      from doctest import testmod

      testmod()

