





'''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


a__					:			Tuple          =     logging.get_logger(__name__)

a__					:			int          =     {
    '''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 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_=None      ,       **UpperCamelCase_		)      ->		List[str]:
		logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'		)
		snake_case__	       =   model
		snake_case__	       =   kwargs.get('model_save_dir'      ,       UpperCamelCase_		)
		snake_case__	       =   kwargs.get('latest_model_name'      ,       UpperCamelCase_		)





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





	@staticmethod
	def   _snake_case   (		UpperCamelCase_      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None		)      ->		Optional[int]:
		if provider is None:
			logger.info('No onnxruntime provider specified, using CPUExecutionProvider'		)
			snake_case__	       =   'CPUExecutionProvider'

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_		)      ->		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(UpperCamelCase_		).joinpath(UpperCamelCase_		)
		try:
			shutil.copyfile(UpperCamelCase_      ,       UpperCamelCase_		)
		except shutil.SameFileError:
			pass

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





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





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

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










'''simple docstring'''




from __future__ import annotations

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

	if depth < 0:
		raise ValueError('Depth cannot be less than 0'							)

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



