




'''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_		:				Union[str, Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Dict									=     {
    "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      :

						def __init__(		self   :Optional[Any]    , __snake_case   :List[Any]=None    , **__snake_case   :int ):
										'''simple docstring'''

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

						def __call__(		self   :Union[str, Any]    , **__snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								={k: np.array(lowerCamelCase_ ) for k, v in kwargs.items()}
										return self.model.run(lowerCamelCase_    , lowerCamelCase_ )

						@staticmethod
						def     A__						(		__snake_case   :Union[str, Path]    , __snake_case   :Union[str, Any]=None    , __snake_case   :int=None ):
										'''simple docstring'''

										if provider is None:
														logger.info("""No onnxruntime provider specified, using CPUExecutionProvider""" )
														__magic_name__	:	Optional[int]								="""CPUExecutionProvider"""

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

						def     A__						(		self   :Optional[Any]    , __snake_case   :Union[str, Path]    , __snake_case   :Optional[str] = None    , **__snake_case   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Dict								=file_name if file_name is not None else ONNX_WEIGHTS_NAME

										__magic_name__	:	List[Any]								=self.model_save_dir.joinpath(self.latest_model_name )
										__magic_name__	:	List[Any]								=Path(lowerCamelCase_ ).joinpath(lowerCamelCase_ )
										try:
														shutil.copyfile(lowerCamelCase_    , lowerCamelCase_ )
										except shutil.SameFileError:
														pass

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

						def     A__						(		self   :int    , __snake_case   :Union[str, os.PathLike]    , **__snake_case   :int    , ):
										'''simple docstring'''

										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   :Union[str, Any]    , __snake_case   :Union[str, Path]    , __snake_case   :Optional[Union[bool, str, None]] = None    , __snake_case   :Optional[Union[str, None]] = None    , __snake_case   :bool = False    , __snake_case   :Optional[str] = None    , __snake_case   :Optional[str] = None    , __snake_case   :Optional[str] = None    , __snake_case   :Optional["ort.SessionOptions"] = None    , **__snake_case   :List[Any]    , ):
										'''simple docstring'''

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


						@classmethod
						def     A__						(		cls   :Any    , __snake_case   :Union[str, Path]    , __snake_case   :bool = True    , __snake_case   :Optional[str] = None    , __snake_case   :Optional[str] = None    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=None
										if len(str(lowerCamelCase_ ).split("""@""" ) ) == 2:
														__magic_name__	:	str								=model_id.split("""@""" )

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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




