



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


a_   :Tuple        =      logging.get_logger(__name__)

a_   :str        =      {
    "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 					snake_case__						:






     """simple docstring"""


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






     def __call__(       self  :  Tuple, **_snake_case  :  int					)   ->Any:
            snake_case__							:	Union[str, Any]   	=	{k: np.array(__UpperCAmelCase					) for k, v in kwargs.items()}
            return self.model.run(__UpperCAmelCase, __UpperCAmelCase					)






     @staticmethod
     def    lowercase_   (       _snake_case  :  Union[str, Path], _snake_case  :  Tuple=None, _snake_case  :  Dict=None					)   ->List[str]:



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

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






     def    lowercase_   (       self  :  Dict, _snake_case  :  Union[str, Path], _snake_case  :  Optional[str] = None, **_snake_case  :  List[str]					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	file_name if file_name is not None else ONNX_WEIGHTS_NAME

            snake_case__							:	List[str]   	=	self.model_save_dir.joinpath(self.latest_model_name					)
            snake_case__							:	List[str]   	=	Path(__UpperCAmelCase					).joinpath(__UpperCAmelCase					)
            try:
                   shutil.copyfile(__UpperCAmelCase, __UpperCAmelCase					)
            except shutil.SameFileError:
                   pass

            # copy external weights (for models >2GB)
            snake_case__							:	int   	=	self.model_save_dir.joinpath(__UpperCAmelCase					)
            if src_path.exists():
                   snake_case__							:	str   	=	Path(__UpperCAmelCase					).joinpath(__UpperCAmelCase					)
                   try:
                          shutil.copyfile(__UpperCAmelCase, __UpperCAmelCase					)
                   except shutil.SameFileError:
                          pass






     def    lowercase_   (       self  :  List[str], _snake_case  :  Union[str, os.PathLike], **_snake_case  :  Tuple, )   ->Union[str, Any]:



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






     @classmethod
     def    lowercase_   (       cls  :  Union[str, Any], _snake_case  :  Union[str, Path], _snake_case  :  bool = True, _snake_case  :  Optional[str] = None, _snake_case  :  Optional[str] = None, **_snake_case  :  int, )   ->int:
            snake_case__							:	int   	=	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, )







from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__											=					[
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')




def    lowerCAmelCase_ (     __A,  __A=None							)     ->				Dict:







      '''simple docstring'''


      require_version(deps[pkg],  __A							)



