

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		,	)


def       __lowerCAmelCase      (						A    ):
	if len(A    ) <= 1:
		return lst

	UpperCAmelCase_       				=			1

	while i < len(A    ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				UpperCAmelCase_       				=			1

	return lst


if __name__ == "__main__":
				_a:    List[Any] 							=			input("""Enter numbers separated by a comma:\n""").strip()
				_a:    Optional[int] 							=			[int(item) for item in user_input.split(""",""")]
				print(gnome_sort(unsorted))