





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__						:			Union[str, Any]									=logging.get_logger(__name__)

UpperCAmelCase__						:			List[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       __A     :


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


				def __call__(		self   , **UpperCAmelCase_							):
								lowerCamelCase	 ={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							):
								if provider is None:
												logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""							)
												lowerCamelCase	 ="""CPUExecutionProvider"""

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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_							):
								lowerCamelCase	 =file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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


				def _snake_case       (		self   , UpperCAmelCase_   , **UpperCAmelCase_   , ):
								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_   , ):
								lowerCamelCase	 =file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(UpperCAmelCase_							):
												lowerCamelCase	 =OnnxRuntimeModel.load_model(
												    os.path.join(UpperCAmelCase_   , UpperCAmelCase_							)   , provider=UpperCAmelCase_   , sess_options=UpperCAmelCase_							)
												lowerCamelCase	 =Path(UpperCAmelCase_							)
								# load model from hub
								else:
												# download model
												lowerCamelCase	 =hf_hub_download(
												    repo_id=UpperCAmelCase_   , filename=UpperCAmelCase_   , use_auth_token=UpperCAmelCase_   , revision=UpperCAmelCase_   , cache_dir=UpperCAmelCase_   , force_download=UpperCAmelCase_   , )
												lowerCamelCase	 =Path(UpperCAmelCase_							).parent
												lowerCamelCase	 =Path(UpperCAmelCase_							).name
												lowerCamelCase	 =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_   , ):
								lowerCamelCase	 =None
								if len(str(UpperCAmelCase_							).split("""@"""							)							) == 2:
												lowerCamelCase ,  lowerCamelCase	 =model_id.split("""@"""							)

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












def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =[0 for i in range(r + 1				)]
				# nc0 = 1
				lowerCamelCase	 =1
				for i in range(1			,      n + 1				):
								# to compute current row from previous row.
								lowerCamelCase	 =min(_UpperCAmelCase			,      _UpperCAmelCase				)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]




print(binomial_coefficient(n=10, r=5))





