"""simple docstring"""




import argparse


_UpperCAmelCase            =		"""docs/source/_static/js/custom.js"""




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		with open(__lowerCAmelCase						,encoding='utf-8'						,newline='\n'     ) as f:
				A_		:    Tuple         =				f.readlines()
		A_		:    int         =				0

		# First let's put the right version
		while not lines[index].startswith('const stableVersion ='     ):
				index += 1
		A_		:    Dict         =				f'''const stableVersion = "v{version}"\n'''

		# Then update the dictionary
		while not lines[index].startswith('const versionMapping = {'     ):
				index += 1

		# We go until the end
		while not lines[index].startswith('}'     ):
				index += 1
		# We add the new version at the end
		lines[index - 1] += f'''    "v{version}": "v{version}",\n'''

		with open(__lowerCAmelCase						,'w'						,encoding='utf-8'						,newline='\n'     ) as f:
				f.writelines(__lowerCAmelCase     )


if __name__ == "__main__":
	_UpperCAmelCase            =		argparse.ArgumentParser()
	parser.add_argument("""--version""", help="""Release version.""")
	_UpperCAmelCase            =		parser.parse_args()
	update_custom_js(args.version)







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            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """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 UpperCAmelCase						:


				'''simple docstring'''






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



						"""simple docstring"""





						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
						A_		:    List[Any]         =				model
						A_		:    Dict         =				kwargs.get('model_save_dir'		,     lowercase )
						A_		:    List[str]         =				kwargs.get('latest_model_name'		,     lowercase )
				def __call__(						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				{k: np.array(lowercase ) for k, v in kwargs.items()}
						return self.model.run(lowercase		,     lowercase )
				@staticmethod
				def   lowerCAmelCase_       (						lowercase		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
								A_		:    List[Any]         =				'CPUExecutionProvider'

						return ort.InferenceSession(lowercase		,     providers=[provider]		,     sess_options=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A_		:    Optional[int]         =				self.model_save_dir.joinpath(self.latest_model_name )
						A_		:    int         =				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_		:    int         =				Path(lowercase ).joinpath(lowercase )
								try:
										shutil.copyfile(lowercase		,     lowercase )
								except shutil.SameFileError:
										pass
				def   lowerCAmelCase_       (						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   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase ):
								A_		:    Optional[int]         =				OnnxRuntimeModel.load_model(
								    os.path.join(lowercase		,     lowercase )		,     provider=lowercase		,     sess_options=lowercase )
								A_		:    Dict         =				Path(lowercase )
						# load model from hub
						else:
								# download model
								A_		:    List[str]         =				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_		:    Optional[Any]         =				Path(lowercase ).name
								A_		:    Any         =				OnnxRuntimeModel.load_model(lowercase		,     provider=lowercase		,     sess_options=lowercase )
						return cls(model=lowercase		,     **lowercase )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				None
						if len(str(lowercase ).split('@' ) ) == 2:
								A_       ,      A_		:    int         =				model_id.split('@' )

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






