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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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     lowerCAmelCase_     :







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







							def __call__(   self      :     Union[str, Any] ,			**_A      :     Tuple		):
												_UpperCamelCase			     =     {k: np.array(_A		) for k, v in kwargs.items()}
												return self.model.run(_A ,			_A		)







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Union[str, Path] ,			_A      :     Dict=None ,			_A      :     Tuple=None		):
												if provider is None:
																	logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''		)
																	_UpperCamelCase			     =     '''CPUExecutionProvider'''

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







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Union[str, Path] ,			_A      :     Optional[str] = None ,			**_A      :     int		):
												_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(_A		).joinpath(_A		)
												try:
																	shutil.copyfile(_A ,			_A		)
												except shutil.SameFileError:
																	pass

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







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Union[str, os.PathLike] ,			**_A      :     Dict ,			):
												if os.path.isfile(_A		):
																	logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""		)
																	return

												os.makedirs(_A ,			exist_ok=_A		)

												# saving model weights/files
												self._save_pretrained(_A ,			**_A		)







							@classmethod
							def  UpperCamelCase_						(   cls      :     Any ,			_A      :     Union[str, Path] ,			_A      :     Optional[Union[bool, str, None]] = None ,			_A      :     Optional[Union[str, None]] = None ,			_A      :     bool = False ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			_A      :     Optional["ort.SessionOptions"] = None ,			**_A      :     List[Any] ,			):
												_UpperCamelCase			     =     file_name if file_name is not None else ONNX_WEIGHTS_NAME
												# load model from local directory
												if os.path.isdir(_A		):
																	_UpperCamelCase			     =     OnnxRuntimeModel.load_model(
																	    os.path.join(_A ,			_A		) ,			provider=_A ,			sess_options=_A		)
																	_UpperCamelCase			     =     Path(_A		)
												# load model from hub
												else:
																	# download model
																	_UpperCamelCase			     =     hf_hub_download(
																	    repo_id=_A ,			filename=_A ,			use_auth_token=_A ,			revision=_A ,			cache_dir=_A ,			force_download=_A ,			)
																	_UpperCamelCase			     =     Path(_A		).parent
																	_UpperCamelCase			     =     Path(_A		).name
																	_UpperCamelCase			     =     OnnxRuntimeModel.load_model(_A ,			provider=_A ,			sess_options=_A		)
												return cls(model=_A ,			**_A		)







							@classmethod
							def  UpperCamelCase_						(   cls      :     Optional[int] ,			_A      :     Union[str, Path] ,			_A      :     bool = True ,			_A      :     Optional[str] = None ,			_A      :     Optional[str] = None ,			**_A      :     List[str] ,			):
												_UpperCamelCase			     =     None
												if len(str(_A		).split('''@'''		)		) == 2:
																	_UpperCamelCase     ,    _UpperCamelCase			     =     model_id.split('''@'''		)

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



'''simple docstring'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)			)
    return edges


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





