



'''simple docstring'''






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__:   List[str]								=			logging.get_logger(__name__)

UpperCamelCase__:   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 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






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







	def __call__(						self			:     Optional[Any]		,			**__snake_case			:     Optional[Any]		)			->		List[Any]:
								UpperCAmelCase :       Optional[int] 							=		{k: np.array(__snake_case		) for k, v in kwargs.items()}
								return self.model.run(__snake_case		,			__snake_case		)







	@staticmethod
	def      A	(						__snake_case			:     Union[str, Path]		,			__snake_case			:     List[str]=None		,			__snake_case			:     Optional[int]=None		)			->		Optional[int]:
								if provider is None:
															logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''		)
															UpperCAmelCase :       List[Any] 							=		'''CPUExecutionProvider'''

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







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Union[str, Path]		,			__snake_case			:     Optional[str] = None		,			**__snake_case			:     int		)			->		str:
								UpperCAmelCase :       List[str] 							=		file_name if file_name is not None else ONNX_WEIGHTS_NAME

								UpperCAmelCase :       Optional[int] 							=		self.model_save_dir.joinpath(self.latest_model_name		)
								UpperCAmelCase :       int 							=		Path(__snake_case		).joinpath(__snake_case		)
								try:
															shutil.copyfile(__snake_case		,			__snake_case		)
								except shutil.SameFileError:
															pass

								# copy external weights (for models >2GB)
								UpperCAmelCase :       Tuple 							=		self.model_save_dir.joinpath(__snake_case		)
								if src_path.exists():
															UpperCAmelCase :       List[Any] 							=		Path(__snake_case		).joinpath(__snake_case		)
															try:
																						shutil.copyfile(__snake_case		,			__snake_case		)
															except shutil.SameFileError:
																						pass







	def      A	(						self			:     Optional[int]		,			__snake_case			:     Union[str, os.PathLike]		,			**__snake_case			:     int		,			)			->		List[Any]:
								if os.path.isfile(__snake_case		):
															logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""		)
															return

								os.makedirs(__snake_case		,			exist_ok=__snake_case		)

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







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







	@classmethod
	def      A	(						cls			:     Any		,			__snake_case			:     Union[str, Path]		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[str] = None		,			**__snake_case			:     Optional[int]		,			)			->		str:
								UpperCAmelCase :       str 							=		None
								if len(str(__snake_case		).split('''@'''		)		) == 2:
															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		model_id.split('''@'''		)

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










'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


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





