


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


_snake_case  :	Tuple			      =       logging.get_logger(__name__)

_snake_case  :	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        a	:




							"""simple docstring"""


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

							def __call__(							self :						Union[str, Any]					,			**lowerCamelCase :						Optional[Any]       )			->					Union[str, Any]:
														__snake_case	:    Dict											=     {k: np.array(lowerCamelCase       ) for k, v in kwargs.items()}
														return self.model.run(lowerCamelCase					,			lowerCamelCase       )

							@staticmethod
							def 					__snake_case      (							lowerCamelCase :						Union[str, Path]					,			lowerCamelCase :						Any=None					,			lowerCamelCase :						List[Any]=None       )			->					Tuple:
														if provider is None:
																					logger.info("No onnxruntime provider specified, using CPUExecutionProvider"       )
																					__snake_case	:    Union[str, Any]											=     "CPUExecutionProvider"

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

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Union[str, Path]					,			lowerCamelCase :						Optional[str] = None					,			**lowerCamelCase :						List[str]       )			->					Tuple:
														__snake_case	:    Tuple											=     file_name if file_name is not None else ONNX_WEIGHTS_NAME

														__snake_case	:    Any											=     self.model_save_dir.joinpath(self.latest_model_name       )
														__snake_case	:    Any											=     Path(lowerCamelCase       ).joinpath(lowerCamelCase       )
														try:
																					shutil.copyfile(lowerCamelCase					,			lowerCamelCase       )
														except shutil.SameFileError:
																					pass

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

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






							@classmethod
							def 					__snake_case      (							cls :						int					,			lowerCamelCase :						Union[str, Path]					,			lowerCamelCase :						bool = True					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[str] = None					,			**lowerCamelCase :						str					,			)			->					int:
														__snake_case	:    Union[str, Any]											=     None
														if len(str(lowerCamelCase       ).split("@"       )       ) == 2:
																					__snake_case				,    __snake_case	:    int											=     model_id.split("@"       )

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





import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


_snake_case  :	List[str]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Any					,			**lowerCamelCase :						Union[str, Any]       )			->					None:
														warnings.warn(
														    "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use PoolFormerImageProcessor instead."					,			lowerCamelCase					,			)
														super().__init__(*lowerCamelCase					,			**lowerCamelCase       )

