





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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    """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_					)						->							int:
										logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'					)
										_snake_case													=			model
										_snake_case													=			kwargs.get('model_save_dir'			,  lowerCAmelCase_					)
										_snake_case													=			kwargs.get('latest_model_name'			,  lowerCAmelCase_					)




					def __call__(						self			,  **lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			{k: np.array(lowerCAmelCase_					) for k, v in kwargs.items()}
										return self.model.run(lowerCAmelCase_			,  lowerCAmelCase_					)




					@staticmethod
					def 		lowerCAmelCase     (						lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None					)						->							Tuple:
										if provider is None:
															logger.info('No onnxruntime provider specified, using CPUExecutionProvider'					)
															_snake_case													=			'CPUExecutionProvider'

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_					)						->							List[Any]:
										_snake_case													=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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




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






					@classmethod
					def 		lowerCAmelCase     (						cls			,  lowerCAmelCase_			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							str:
										_snake_case													=			None
										if len(str(lowerCAmelCase_					).split('@'					)					) == 2:
															_snake_case							,       _snake_case													=			model_id.split('@'					)

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










import random





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    float    ,      UpperCamelCase__     :    bool = False  )     ->	dict:



					'''simple docstring'''




					_snake_case													=			{i: [] for i in range(UpperCamelCase__  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(UpperCamelCase__  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(UpperCamelCase__  ):
										for j in range(i + 1    ,      UpperCamelCase__  ):
															if random.random() < probability:
																				graph[i].append(UpperCamelCase__  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(UpperCamelCase__  )
					return graph





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	dict:



					'''simple docstring'''




					return {
					    i: [j for j in range(UpperCamelCase__  ) if i != j] for i in range(UpperCamelCase__  )
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



