


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

A	:		str    =       {
    '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   :
					'''simple docstring'''






					def __init__(self   :						Optional[int]				, _UpperCAmelCase   :						Any=None				, **_UpperCAmelCase   :						Optional[int]		)						->       Union[str, Any]:




							"""simple docstring"""


							logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""		)
							lowercase__					      =		model
							lowercase__					      =		kwargs.get("""model_save_dir"""				, _UpperCAmelCase		)
							lowercase__					      =		kwargs.get("""latest_model_name"""				, _UpperCAmelCase		)

					def __call__(self   :						int				, **_UpperCAmelCase   :						Optional[int]		)						->       str:




							"""simple docstring"""


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

					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						Union[str, Path]				, _UpperCAmelCase   :						Union[str, Any]=None				, _UpperCAmelCase   :						List[Any]=None		)						->       Optional[int]:




							"""simple docstring"""


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

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

					def   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						Union[str, Path]				, _UpperCAmelCase   :						Optional[str] = None				, **_UpperCAmelCase   :						Any		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, os.PathLike]				, **_UpperCAmelCase   :						int				, )						->       Optional[int]:




							"""simple docstring"""


							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   lowerCamelCase__					(cls   :						List[Any]				, _UpperCAmelCase   :						Union[str, Path]				, _UpperCAmelCase   :						Optional[Union[bool, str, None]] = None				, _UpperCAmelCase   :						Optional[Union[str, None]] = None				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional["ort.SessionOptions"] = None				, **_UpperCAmelCase   :						Union[str, Any]				, )						->       int:




							"""simple docstring"""


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


					@classmethod
					def   lowerCamelCase__					(cls   :						int				, _UpperCAmelCase   :						Union[str, Path]				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[str] = None				, **_UpperCAmelCase   :						Optional[int]				, )						->       str:




							"""simple docstring"""


							lowercase__					      =		None
							if len(str(_UpperCAmelCase		).split("""@"""		)		) == 2:
									lowercase__      ,     lowercase__					      =		model_id.split("""@"""		)

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





def 			UpperCamelCase (     __magic_name__					:							str					)       ->  List[str]:  # noqa: E741
		"""simple docstring"""




		lowercase__					      =		len(__magic_name__					)
		lowercase__					      =		0
		lowercase__					      =		[0] * n
		lowercase__					      =		[False] * n
		lowercase__					      =		[False] * n

		def dfs(__magic_name__					:							str   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							str   ,   __magic_name__					:							Any					):
				if parent == root:
						out_edge_count += 1
				lowercase__					      =		True
				lowercase__					      =		at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
								lowercase__					      =		min(low[at]   ,   low[to]					)

								# AP found via bridge
								if at < low[to]:
										lowercase__					      =		True
								# AP found via cycle
								if at == low[to]:
										lowercase__					      =		True
						else:
								lowercase__					      =		min(low[at]   ,   __magic_name__					)
				return out_edge_count

		for i in range(__magic_name__					):
				if not visited[i]:
						lowercase__					      =		0
						lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   -1   ,   __magic_name__					)
						lowercase__					      =		out_edge_count > 1

		for x in range(len(__magic_name__					)					):
				if is_art[x] is True:
						print(__magic_name__					)


# Adjacency list of graph
A	:		List[str]    =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)

