
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


lowercase  :					Dict						    =							logging.get_logger(__name__)

lowercase  :					Union[str, 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 __snake_case		:
    def __init__(  self		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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

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



           '''simple docstring'''







           lowercase							:     Any       	=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

           lowercase							:     Union[str, Any]       	=      self.model_save_dir.joinpath(self.latest_model_name      )
           lowercase							:     Dict       	=      Path(snake_case      ).joinpath(snake_case      )
           try:
                  shutil.copyfile(snake_case		,snake_case      )
           except shutil.SameFileError:
                  pass

           # copy external weights (for models >2GB)
           lowercase							:     Dict       	=      self.model_save_dir.joinpath(snake_case      )
           if src_path.exists():
                  lowercase							:     Optional[int]       	=      Path(snake_case      ).joinpath(snake_case      )
                  try:
                         shutil.copyfile(snake_case		,snake_case      )
                  except shutil.SameFileError:
                         pass
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,**snake_case		,):



           '''simple docstring'''







           if os.path.isfile(snake_case      ):
                  logger.error(f"Provided path ({save_directory}) should be a directory, not a file"      )
                  return

           os.makedirs(snake_case		,exist_ok=snake_case      )

           # saving model weights/files
           self._save_pretrained(snake_case		,**snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,snake_case = None		,snake_case = None		,snake_case = False		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Any       	=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
           # load model from local directory
           if os.path.isdir(snake_case      ):
                  lowercase							:     Any       	=      OnnxRuntimeModel.load_model(
                      os.path.join(snake_case		,snake_case      )		,provider=snake_case		,sess_options=snake_case      )
                  lowercase							:     int       	=      Path(snake_case      )
           # load model from hub
           else:
                  # download model
                  lowercase							:     Union[str, Any]       	=      hf_hub_download(
                      repo_id=snake_case		,filename=snake_case		,use_auth_token=snake_case		,revision=snake_case		,cache_dir=snake_case		,force_download=snake_case		,)
                  lowercase							:     int       	=      Path(snake_case      ).parent
                  lowercase							:     Union[str, Any]       	=      Path(snake_case      ).name
                  lowercase							:     int       	=      OnnxRuntimeModel.load_model(snake_case		,provider=snake_case		,sess_options=snake_case      )
           return cls(model=snake_case		,**snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,snake_case = True		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







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

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



import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






