


'''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_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[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 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase=None      ,      **__lowercase						):

  """simple docstring"""







  logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
  __A  :		str       =			model
  __A  :		Optional[int]       =			kwargs.get('model_save_dir'      ,      __lowercase						)
  __A  :		List[str]       =			kwargs.get('latest_model_name'      ,      __lowercase						)


 def __call__(      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Optional[Any]       =			{k: np.array(__lowercase						) for k, v in kwargs.items()}
  return self.model.run(__lowercase      ,      __lowercase						)


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







  if provider is None:
   logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
   __A  :		Dict       =			'CPUExecutionProvider'

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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			file_name if file_name is not None else ONNX_WEIGHTS_NAME

  __A  :		int       =			self.model_save_dir.joinpath(self.latest_model_name						)
  __A  :		str       =			Path(__lowercase						).joinpath(__lowercase						)
  try:
   shutil.copyfile(__lowercase      ,      __lowercase						)
  except shutil.SameFileError:
   pass

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


 def  snake_case__   (      self      ,      __lowercase      ,      **__lowercase      ,      ):

  """simple docstring"""







  if os.path.isfile(__lowercase						):
   logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""						)
   return

  os.makedirs(__lowercase      ,      exist_ok=__lowercase						)

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


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = False      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		List[str]       =			file_name if file_name is not None else ONNX_WEIGHTS_NAME
  # load model from local directory
  if os.path.isdir(__lowercase						):
   __A  :		Tuple       =			OnnxRuntimeModel.load_model(
       os.path.join(__lowercase      ,      __lowercase						)      ,      provider=__lowercase      ,      sess_options=__lowercase						)
   __A  :		List[str]       =			Path(__lowercase						)
  # load model from hub
  else:
   # download model
   __A  :		int       =			hf_hub_download(
       repo_id=__lowercase      ,      filename=__lowercase      ,      use_auth_token=__lowercase      ,      revision=__lowercase      ,      cache_dir=__lowercase      ,      force_download=__lowercase      ,      )
   __A  :		int       =			Path(__lowercase						).parent
   __A  :		List[Any]       =			Path(__lowercase						).name
   __A  :		int       =			OnnxRuntimeModel.load_model(__lowercase      ,      provider=__lowercase      ,      sess_options=__lowercase						)
  return cls(model=__lowercase      ,      **__lowercase						)







 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			None
  if len(str(__lowercase						).split('@'						)						) == 2:
   __A     ,__A  :		Tuple       =			model_id.split('@'						)

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










'''simple docstring'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      input('Enter numbers separated by a comma:\n').strip()
 if user_input == "":
  UpperCAmelCase_ :     Union[str, Any]								=      []
 else:
  UpperCAmelCase_ :     str								=      [int(item.strip()) for item in user_input.split(',')]
 print(iter_merge_sort(unsorted))






