

'''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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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_			:






					'''simple docstring'''



					def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Any=None					,      **lowercase_			:					Optional[int]	)						->     Optional[int]:
						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'	)
						UpperCAmelCase	:     List[str]						 =			model
						UpperCAmelCase	:     Dict						 =			kwargs.get('model_save_dir'					,      lowercase_	)
						UpperCAmelCase	:     Union[str, Any]						 =			kwargs.get('latest_model_name'					,      lowercase_	)


					def __call__(		self			:					List[str]					,      **lowercase_			:					Tuple	)						->     Optional[int]:
						UpperCAmelCase	:     Any						 =			{k: np.array(lowercase_	) for k, v in kwargs.items()}
						return self.model.run(lowercase_					,      lowercase_	)


					@staticmethod
					def        UpperCAmelCase_	(		lowercase_			:					Any					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					Optional[int]=None	)						->     Optional[int]:
						if provider is None:
							logger.info('No onnxruntime provider specified, using CPUExecutionProvider'	)
							UpperCAmelCase	:     List[str]						 =			"CPUExecutionProvider"

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


					def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any] = None					,      **lowercase_			:					Optional[Any]	)						->     Any:
						UpperCAmelCase	:     Dict						 =			file_name if file_name is not None else ONNX_WEIGHTS_NAME

						UpperCAmelCase	:     Optional[Any]						 =			self.model_save_dir.joinpath(self.latest_model_name	)
						UpperCAmelCase	:     str						 =			Path(lowercase_	).joinpath(lowercase_	)
						try:
							shutil.copyfile(lowercase_					,      lowercase_	)
						except shutil.SameFileError:
							pass

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


					def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					str					,      **lowercase_			:					str					,      )						->     Dict:
						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        UpperCAmelCase_	(		cls			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Tuple = None					,      lowercase_			:					Tuple = None					,      lowercase_			:					Any = False					,      lowercase_			:					str = None					,      lowercase_			:					Tuple = None					,      lowercase_			:					Optional[int] = None					,      lowercase_			:					Optional[Any] = None					,      **lowercase_			:					Dict					,      )						->     List[Any]:
						UpperCAmelCase	:     Union[str, Any]						 =			file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase_	):
							UpperCAmelCase	:     str						 =			OnnxRuntimeModel.load_model(
							    os.path.join(lowercase_					,      lowercase_	)					,      provider=lowercase_					,      sess_options=lowercase_	)
							UpperCAmelCase	:     List[Any]						 =			Path(lowercase_	)
						# load model from hub
						else:
							# download model
							UpperCAmelCase	:     Union[str, Any]						 =			hf_hub_download(
							    repo_id=lowercase_					,      filename=lowercase_					,      use_auth_token=lowercase_					,      revision=lowercase_					,      cache_dir=lowercase_					,      force_download=lowercase_					,      )
							UpperCAmelCase	:     str						 =			Path(lowercase_	).parent
							UpperCAmelCase	:     Dict						 =			Path(lowercase_	).name
							UpperCAmelCase	:     Optional[int]						 =			OnnxRuntimeModel.load_model(lowercase_					,      provider=lowercase_					,      sess_options=lowercase_	)
						return cls(model=lowercase_					,      **lowercase_	)




					@classmethod
					def        UpperCAmelCase_	(		cls			:					int					,      lowercase_			:					List[Any]					,      lowercase_			:					Optional[int] = True					,      lowercase_			:					Union[str, Any] = None					,      lowercase_			:					Dict = None					,      **lowercase_			:					Dict					,      )						->     Tuple:
						UpperCAmelCase	:     Optional[int]						 =			None
						if len(str(lowercase_	).split('@'	)	) == 2:
							UpperCAmelCase	:     Dict						 =			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'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase	:     list						 =			[]
 for char_count in range(UpperCAmelCase_     ):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]     )
  if char_count < second_str_length:
   output_list.append(second_str[char_count]     )
 return "".join(UpperCAmelCase_     )


if __name__ == "__main__":
 print(alternative_string_arrange("AB", "XYZ"), end=" ")




