

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:    Dict 							=			logging.get_logger(__name__)

_a:    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	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, Any]=None		,	**lowerCAmelCase	:						Optional[Any]							):


  '''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	:						Union[str, Any]		,	**lowerCAmelCase	:						int							):


  '''simple docstring'''







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




 @staticmethod
 def       __A					(		lowerCAmelCase	:						Union[str, Path]		,	lowerCAmelCase	:						Optional[Any]=None		,	lowerCAmelCase	:						int=None							):


  '''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       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, Path]		,	lowerCAmelCase	:						Optional[str] = None		,	**lowerCAmelCase	:						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       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Union[str, os.PathLike]		,	**lowerCAmelCase	:						int		,	):


  '''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       __A					(		cls	:						List[str]		,	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	:						List[str]		,	):


  '''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       __A					(		cls	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Path]		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[str] = None		,	**lowerCAmelCase	:						Optional[int]		,	):


  '''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 collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


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

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')