




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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """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  __magic_name__						:
				'''simple docstring'''







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


				def __call__(							self:   List[Any]		,					**_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {k: np.array(_lowerCamelCase		) for k, v in kwargs.items()}
											return self.model.run(_lowerCamelCase		,					_lowerCamelCase		)


				@staticmethod
				def     _A							(							_lowerCamelCase:   Union[str, Path]		,					_lowerCamelCase:   str=None		,					_lowerCamelCase:   Tuple=None		):
											if provider is None:
																		logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''		)
																		SCREAMING_SNAKE_CASE_   	=    '''CPUExecutionProvider'''

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


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Union[str, Path]		,					_lowerCamelCase:   Optional[str] = None		,					**_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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


				def     _A							(							self:   str		,					_lowerCamelCase:   Union[str, os.PathLike]		,					**_lowerCamelCase:   Dict		,					):
											if os.path.isfile(_lowerCamelCase		):
																		logger.error(f"Provided path ({save_directory}) should be a directory, not a file"		)
																		return

											os.makedirs(_lowerCamelCase		,					exist_ok=_lowerCamelCase		)

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


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





				@classmethod
				def     _A							(							cls:   int		,					_lowerCamelCase:   Union[str, Path]		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[str] = None		,					**_lowerCamelCase:   Optional[int]		,					):
											SCREAMING_SNAKE_CASE_   	=    None
											if len(str(_lowerCamelCase		).split('''@'''		)		) == 2:
																		SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    model_id.split('''@'''		)

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








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=input("""Enter numbers separated by a comma:\n""").strip()
			__SCREAMING_SNAKE_CASE 						=[int(item) for item in user_input.split(""",""")]
			print(*merge_sort(unsorted), sep=""",""")


