





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

_snake_case  :    str						    =  logging.get_logger(__name__)

def   a_    ( lowerCAmelCase_      :			List[Any]	):



   if isinstance(A__,	(list, tuple)	) and isinstance(videos[0],	(list, tuple)	) and is_valid_image(videos[0][0]	):
      return videos

   elif isinstance(A__,	(list, tuple)	) and is_valid_image(videos[0]	):
      return [videos]

   elif is_valid_image(A__	):
      return [[videos]]

   raise ValueError(F"""Could not make batched video from {videos}"""	)




class 				_UpperCAmelCase    (    __lowerCamelCase    ):

  """simple docstring"""


  a_        = ['pixel_values']






  def __init__(				self :		Any					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		PILImageResampling = PILImageResampling.BILINEAR					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Union[int, float] = 1 / 2_5_5					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  **lowerCAmelCase_ :		Dict					,  )							->						Dict:
     super().__init__(**UpperCamelCase_     )
     __lowerCAmelCase								=	size if size is not None else {'shortest_edge': 2_5_6}
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_					,  default_to_square=UpperCamelCase_     )
     __lowerCAmelCase								=	crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_					,  param_name='crop_size'     )

     __lowerCAmelCase								=	do_resize
     __lowerCAmelCase								=	size
     __lowerCAmelCase								=	do_center_crop
     __lowerCAmelCase								=	crop_size
     __lowerCAmelCase								=	resample
     __lowerCAmelCase								=	do_rescale
     __lowerCAmelCase								=	rescale_factor
     __lowerCAmelCase								=	offset
     __lowerCAmelCase								=	do_normalize
     __lowerCAmelCase								=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __lowerCAmelCase								=	image_std if image_std is not None else IMAGENET_STANDARD_STD






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		Dict[str, int]					,  lowerCAmelCase_ :		PILImageResampling = PILImageResampling.BILINEAR					,  lowerCAmelCase_ :		Optional[Union[str, ChannelDimension]] = None					,  **lowerCAmelCase_ :		str					,  )							->						Tuple:
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_					,  default_to_square=UpperCamelCase_     )
     if "shortest_edge" in size:
        __lowerCAmelCase								=	get_resize_output_image_size(UpperCamelCase_					,  size['shortest_edge']					,  default_to_square=UpperCamelCase_     )
     elif "height" in size and "width" in size:
        __lowerCAmelCase								=	(size['height'], size['width'])
     else:
        raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""     )
     return resize(UpperCamelCase_					,  size=UpperCamelCase_					,  resample=UpperCamelCase_					,  data_format=UpperCamelCase_					,  **UpperCamelCase_     )






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		Dict[str, int]					,  lowerCAmelCase_ :		Optional[Union[str, ChannelDimension]] = None					,  **lowerCAmelCase_ :		List[Any]					,  )							->						Tuple:
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_     )
     if "height" not in size or "width" not in size:
        raise ValueError(f"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}"""     )
     return center_crop(UpperCamelCase_					,  size=(size['height'], size['width'])					,  data_format=UpperCamelCase_					,  **UpperCamelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		Union[int, float]					,  lowerCAmelCase_ :		bool = True					,  lowerCAmelCase_ :		Optional[Union[str, ChannelDimension]] = None					,  **lowerCAmelCase_ :		Union[str, Any]					,  )							->						int:
     __lowerCAmelCase								=	image.astype(np.floataa     )
     if offset:
        __lowerCAmelCase								=	image - (scale / 2)
     return rescale(UpperCamelCase_					,  scale=UpperCamelCase_					,  data_format=UpperCamelCase_					,  **UpperCamelCase_     )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		np.ndarray					,  lowerCAmelCase_ :		Union[float, List[float]]					,  lowerCAmelCase_ :		Union[float, List[float]]					,  lowerCAmelCase_ :		Optional[Union[str, ChannelDimension]] = None					,  **lowerCAmelCase_ :		Optional[int]					,  )							->						Union[str, Any]:
     return normalize(UpperCamelCase_					,  mean=UpperCamelCase_					,  std=UpperCamelCase_					,  data_format=UpperCamelCase_					,  **UpperCamelCase_     )






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		ImageInput					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		PILImageResampling = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		float = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  lowerCAmelCase_ :		Optional[ChannelDimension] = ChannelDimension.FIRST					,  )							->						List[Any]:
     if do_resize and size is None or resample is None:
        raise ValueError('Size and resample 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.'     )

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('Image mean and std must be specified if do_normalize is True.'     )

     if offset and not do_rescale:
        raise ValueError('For offset, do_rescale must also be set to True.'     )

     # All transformations expect numpy arrays.
     __lowerCAmelCase								=	to_numpy_array(UpperCamelCase_     )

     if do_resize:
        __lowerCAmelCase								=	self.resize(image=UpperCamelCase_					,  size=UpperCamelCase_					,  resample=UpperCamelCase_     )

     if do_center_crop:
        __lowerCAmelCase								=	self.center_crop(UpperCamelCase_					,  size=UpperCamelCase_     )

     if do_rescale:
        __lowerCAmelCase								=	self.rescale(image=UpperCamelCase_					,  scale=UpperCamelCase_					,  offset=UpperCamelCase_     )

     if do_normalize:
        __lowerCAmelCase								=	self.normalize(image=UpperCamelCase_					,  mean=UpperCamelCase_					,  std=UpperCamelCase_     )

     __lowerCAmelCase								=	to_channel_dimension_format(UpperCamelCase_					,  UpperCamelCase_     )
     return image






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		ImageInput					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		PILImageResampling = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Dict[str, int] = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		float = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  lowerCAmelCase_ :		Optional[Union[float, List[float]]] = None					,  lowerCAmelCase_ :		Optional[Union[str, TensorType]] = None					,  lowerCAmelCase_ :		ChannelDimension = ChannelDimension.FIRST					,  **lowerCAmelCase_ :		str					,  )							->						str:
     __lowerCAmelCase								=	do_resize if do_resize is not None else self.do_resize
     __lowerCAmelCase								=	resample if resample is not None else self.resample
     __lowerCAmelCase								=	do_center_crop if do_center_crop is not None else self.do_center_crop
     __lowerCAmelCase								=	do_rescale if do_rescale is not None else self.do_rescale
     __lowerCAmelCase								=	rescale_factor if rescale_factor is not None else self.rescale_factor
     __lowerCAmelCase								=	offset if offset is not None else self.offset
     __lowerCAmelCase								=	do_normalize if do_normalize is not None else self.do_normalize
     __lowerCAmelCase								=	image_mean if image_mean is not None else self.image_mean
     __lowerCAmelCase								=	image_std if image_std is not None else self.image_std

     __lowerCAmelCase								=	size if size is not None else self.size
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_					,  default_to_square=UpperCamelCase_     )
     __lowerCAmelCase								=	crop_size if crop_size is not None else self.crop_size
     __lowerCAmelCase								=	get_size_dict(UpperCamelCase_					,  param_name='crop_size'     )

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

     __lowerCAmelCase								=	make_batched(UpperCamelCase_     )

     __lowerCAmelCase								=	[
         [
             self._preprocess_image(
                 image=UpperCamelCase_					,  do_resize=UpperCamelCase_					,  size=UpperCamelCase_					,  resample=UpperCamelCase_					,  do_center_crop=UpperCamelCase_					,  crop_size=UpperCamelCase_					,  do_rescale=UpperCamelCase_					,  rescale_factor=UpperCamelCase_					,  offset=UpperCamelCase_					,  do_normalize=UpperCamelCase_					,  image_mean=UpperCamelCase_					,  image_std=UpperCamelCase_					,  data_format=UpperCamelCase_					,  )
             for img in video
         ]
         for video in videos
     ]

     __lowerCAmelCase								=	{'pixel_values': videos}
     return BatchFeature(data=UpperCamelCase_					,  tensor_type=UpperCamelCase_     )







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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    '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     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[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 			lowerCAmelCase__						(				cls: str    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[Union[bool, str, None]] = None    ,       UpperCamelCase_: Optional[Union[str, None]] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional["ort.SessionOptions"] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

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

