
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					: Optional[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: 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_       )  ->					List[str]:
				"""simple docstring"""
				logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'       )
				UpperCamelCase		=     model
				UpperCamelCase		=     kwargs.get('model_save_dir'				,      A_       )
				UpperCamelCase		=     kwargs.get('latest_model_name'				,      A_       )



		def __call__( self				,      **A_       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     {k: np.array(A_       ) for k, v in kwargs.items()}
				return self.model.run(A_				,      A_       )



		@staticmethod
		def __UpperCamelCase ( A_				,      A_=None				,      A_=None       )  ->					Optional[Any]:
				"""simple docstring"""
				if provider is None:
						logger.info('No onnxruntime provider specified, using CPUExecutionProvider'       )
						UpperCamelCase		=     'CPUExecutionProvider'

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



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      **A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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



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



		@classmethod
		def __UpperCamelCase ( cls				,      A_				,      A_ = True				,      A_ = None				,      A_ = None				,      **A_				,      )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     None
				if len(str(A_       ).split('@'       )       ) == 2:
						UpperCamelCase	,  UpperCamelCase		=     model_id.split('@'       )

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


def       A       (					lowercase )   ->  list:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		for i in range(1    ,       lowercase ):
				UpperCamelCase		=     collection[i]
				UpperCamelCase		=     0
				UpperCamelCase		=     i - 1

				while low <= high:
						UpperCamelCase		=     (low + high) // 2
						if val < collection[mid]:
								UpperCamelCase		=     mid - 1
						else:
								UpperCamelCase		=     mid + 1
				for j in range(lowercase    ,       lowercase    ,       -1 ):
						UpperCamelCase		=     collection[j - 1]
				UpperCamelCase		=     val
		return collection


if __name__ == "__main__":
					_UpperCAmelCase					: List[Any]      =       input("Enter numbers separated by a comma:\n").strip()
					_UpperCAmelCase					: Optional[int]      =       [int(item) for item in user_input.split(",")]
					print(binary_insertion_sort(unsorted))
