
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


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

lowercase__	:Dict										=					{
    "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        lowercase	:




							def __init__(       self     ,A__=None     ,**A__):
												logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''')
												lowercase					=		model
												lowercase					=		kwargs.get('''model_save_dir'''     ,A__)
												lowercase					=		kwargs.get('''latest_model_name'''     ,A__)




							def __call__(       self     ,**A__):
												lowercase					=		{k: np.array(A__) for k, v in kwargs.items()}
												return self.model.run(A__     ,A__)




							@staticmethod
							def 				A__   (       A__     ,A__=None     ,A__=None):
												if provider is None:
																	logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''')
																	lowercase					=		'''CPUExecutionProvider'''

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




							def 				A__   (       self     ,A__     ,A__ = None     ,**A__):
												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(A__).joinpath(A__)
												try:
																	shutil.copyfile(A__     ,A__)
												except shutil.SameFileError:
																	pass

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




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





							@classmethod
							def 				A__   (       cls     ,A__     ,A__ = True     ,A__ = None     ,A__ = None     ,**A__     ,):
												lowercase					=		None
												if len(str(A__).split('''@''')) == 2:
																	lowercase			,		lowercase					=		model_id.split('''@''')

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






def  UpperCamelCase					(							lowerCAmelCase__ = 1000	):
					'''simple docstring'''
					lowercase					=		-1
					lowercase					=		0
					for a in range(1				,    n // 3	):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowercase					=		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowercase					=		n - a - b
										if c * c == (a * a + b * b):
															lowercase					=		a * b * c
															if candidate >= product:
																				lowercase					=		candidate
					return product


if __name__ == "__main__":
							print(F'{solution() = }')




