
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     ["pixel_values"]



   def __init__(self		:					int    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Dict[str, int]] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          snake_case       :   Union[str, Any]							=      size if size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   Dict							=      get_size_dict(snake_case__ )
          snake_case       :   Optional[Any]							=      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   List[Any]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__    ,						param_name="crop_size" )

          snake_case       :   Tuple							=      do_resize
          snake_case       :   Optional[Any]							=      do_rescale
          snake_case       :   str							=      do_normalize
          snake_case       :   int							=      do_center_crop
          snake_case       :   Tuple							=      crop_size
          snake_case       :   int							=      size
          snake_case       :   Union[str, Any]							=      resample
          snake_case       :   int							=      rescale_factor
          snake_case       :   Any							=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          snake_case       :   List[str]							=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PILImageResampling.BILINEAR    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Any    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   List[str]							=      get_size_dict(snake_case__ )

          if "shortest_edge" in size:
                 snake_case       :   List[str]							=      get_resize_output_image_size(snake_case__    ,						size=size["shortest_edge"]    ,						default_to_square=snake_case__ )
                 # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
                 snake_case       :   List[Any]							=      (size["height"], size["width"])
          else:
                 raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
          return resize(snake_case__    ,						size=snake_case__    ,						resample=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[str]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Dict							=      get_size_dict(snake_case__ )
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
          return center_crop(snake_case__    ,						size=(size["height"], size["width"])    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					float    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[Any] )		->	np.ndarray:


          '''simple docstring'''


          return rescale(snake_case__    ,						scale=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[str]    ,						)		->	np.ndarray:


          '''simple docstring'''


          return normalize(snake_case__    ,						mean=snake_case__    ,						std=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					ImageInput    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					int = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[float] = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Optional[Any]    ,						)		->	BatchFeature:


          '''simple docstring'''


          snake_case       :   int							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   int							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   Union[str, Any]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   str							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   Tuple							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   Union[str, Any]							=      get_size_dict(snake_case__    ,						param_name="crop_size"    ,						default_to_square=snake_case__ )
          snake_case       :   List[str]							=      resample if resample is not None else self.resample
          snake_case       :   Dict							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   Optional[int]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   Tuple							=      image_std if image_std is not None else self.image_std

          snake_case       :   str							=      size if size is not None else self.size
          snake_case       :   List[str]							=      get_size_dict(snake_case__ )

          if not is_batched(snake_case__ ):
                 snake_case       :   Optional[int]							=      [images]

          if not valid_images(snake_case__ ):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray." )

          if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True." )

          if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True." )

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True." )

          # All transformations expect numpy arrays.
          snake_case       :   List[Any]							=      [to_numpy_array(snake_case__ ) for image in images]

          if do_resize:
                 snake_case       :   Tuple							=      [self.resize(image=snake_case__    ,						size=snake_case__    ,						resample=snake_case__ ) for image in images]

          if do_center_crop:
                 snake_case       :   List[str]							=      [self.center_crop(image=snake_case__    ,						size=snake_case__ ) for image in images]

          if do_rescale:
                 snake_case       :   Union[str, Any]							=      [self.rescale(image=snake_case__    ,						scale=snake_case__ ) for image in images]

          if do_normalize:
                 snake_case       :   Optional[Any]							=      [self.normalize(image=snake_case__    ,						mean=snake_case__    ,						std=snake_case__ ) for image in images]

          snake_case       :   Union[str, Any]							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Tuple							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )







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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """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   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


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



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {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__		:					Union[str, Path]    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Optional[int]=None )		->	Any:


          '''simple docstring'''


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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


          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       :   str							=      Path(snake_case__ ).joinpath(snake_case__ )
          try:
                 shutil.copyfile(snake_case__    ,						snake_case__ )
          except shutil.SameFileError:
                 pass

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''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		:					Tuple    ,						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__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(snake_case__ ):
                 snake_case       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ )
          # load model from hub
          else:
                 # download model
                 snake_case       :   Dict							=      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__    ,						)
                 snake_case       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      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		:					Optional[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      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__    ,						)





