





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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """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      :					Optional[Any]      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





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







  def __call__(      self      :					List[str]      ,				**UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	{k: np.array(UpperCamelCase							) for k, v in kwargs.items()}
    return self.model.run(UpperCamelCase      ,				UpperCamelCase							)







  @staticmethod
  def UpperCamelCase_ (      UpperCamelCase      :					Union[str, Path]      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					List[Any]=None							):


    '''simple docstring'''





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

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







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					Union[str, Path]      ,				UpperCamelCase      :					Optional[str] = None      ,				**UpperCamelCase      :					str							):


    '''simple docstring'''





    _snake_case   :				int														=	file_name if file_name is not None else ONNX_WEIGHTS_NAME

    _snake_case   :				Union[str, Any]														=	self.model_save_dir.joinpath(self.latest_model_name							)
    _snake_case   :				Dict														=	Path(UpperCamelCase							).joinpath(UpperCamelCase							)
    try:
      shutil.copyfile(UpperCamelCase      ,				UpperCamelCase							)
    except shutil.SameFileError:
      pass

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







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Union[str, os.PathLike]      ,				**UpperCamelCase      :					Union[str, Any]      ,				):


    '''simple docstring'''





    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 UpperCamelCase_ (      cls      :					Tuple      ,				UpperCamelCase      :					Union[str, Path]      ,				UpperCamelCase      :					Optional[Union[bool, str, None]] = None      ,				UpperCamelCase      :					Optional[Union[str, None]] = None      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional["ort.SessionOptions"] = None      ,				**UpperCamelCase      :					List[Any]      ,				):


    '''simple docstring'''





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







  @classmethod
  def UpperCamelCase_ (      cls      :					List[str]      ,				UpperCamelCase      :					Union[str, Path]      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    _snake_case   :				List[str]														=	None
    if len(str(UpperCamelCase							).split('@'							)							) == 2:
      _snake_case							,					_snake_case   :				List[Any]														=	model_id.split('@'							)

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











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	int(lowerCAmelCase				)
  if n_element < 1:
    _snake_case   :				int														=	ValueError('a should be a positive number'				)
    raise my_error

  _snake_case   :				Union[str, Any]														=	[1]
  _snake_case							,					_snake_case							,					_snake_case   :				Any														=	(0, 0, 0)
  _snake_case   :				str														=	1
  while index < n_element:
    while hamming_list[i] * 2 <= hamming_list[-1]:
      i += 1
    while hamming_list[j] * 3 <= hamming_list[-1]:
      j += 1
    while hamming_list[k] * 5 <= hamming_list[-1]:
      k += 1
    hamming_list.append(
        min(hamming_list[i] * 2    ,							hamming_list[j] * 3    ,							hamming_list[k] * 5				)				)
    index += 1
  return hamming_list


if __name__ == "__main__":
      lowerCAmelCase_						   =    input("""Enter the last number (nth term) of the Hamming Number Series: """)
      print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
      lowerCAmelCase_						   =    hamming(int(n))
      print("""-----------------------------------------------------""")
      print(F"""The list with nth numbers is: {hamming_numbers}""")
      print("""-----------------------------------------------------""")




