





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_			=      logging.get_logger(__name__)

a_			=      {
    """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 ,						__lowerCamelCase=None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''  )
     __A   :						List[str]									=					model
     __A   :						str									=					kwargs.get('''model_save_dir''' ,						__lowerCamelCase  )
     __A   :						List[Any]									=					kwargs.get('''latest_model_name''' ,						__lowerCamelCase  )

 def __call__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Tuple									=					{k: np.array(__lowerCamelCase  ) for k, v in kwargs.items()}
     return self.model.run(__lowerCamelCase ,						__lowerCamelCase  )

 @staticmethod
 def  UpperCamelCase__(   __lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase=None  ):



     '''simple docstring'''




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

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					file_name if file_name is not None else ONNX_WEIGHTS_NAME

     __A   :						str									=					self.model_save_dir.joinpath(self.latest_model_name  )
     __A   :						Dict									=					Path(__lowerCamelCase  ).joinpath(__lowerCamelCase  )
     try:
         shutil.copyfile(__lowerCamelCase ,						__lowerCamelCase  )
     except shutil.SameFileError:
         pass

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						**__lowerCamelCase ,						):



     '''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  UpperCamelCase__(   cls ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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







 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A   :						str									=					None
     if len(str(__lowerCamelCase  ).split('''@'''  )  ) == 2:
         __A   :						List[Any]									=					model_id.split('''@'''  )

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











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




