





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

__A     =    logging.get_logger(__name__)





def 					lowerCAmelCase_			(	__a		)					->		List[List[ImageInput]]:







  """simple docstring"""





  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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["pixel_values"]

 def __init__(self    :       List[str]	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       PILImageResampling = PILImageResampling.BILINEAR	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Union[int, float] = 1 / 255	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			**UpperCAmelCase_    :       Tuple	,			)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				str        =size if size is not None else {"shortest_edge": 256}
   lowerCamelCase__:				Union[str, Any]        =get_size_dict(UpperCAmelCase_	,			default_to_square=UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:				Any        =get_size_dict(UpperCAmelCase_	,			param_name="crop_size")

   lowerCamelCase__:				List[Any]        =do_resize
   lowerCamelCase__:				Tuple        =size
   lowerCamelCase__:				Any        =do_center_crop
   lowerCamelCase__:				Dict        =crop_size
   lowerCamelCase__:				List[Any]        =resample
   lowerCamelCase__:				Union[str, Any]        =do_rescale
   lowerCamelCase__:				Tuple        =rescale_factor
   lowerCamelCase__:				Tuple        =offset
   lowerCamelCase__:				Any        =do_normalize
   lowerCamelCase__:				int        =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase__:				List[str]        =image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       PILImageResampling = PILImageResampling.BILINEAR	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       str	,			)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =get_size_dict(UpperCAmelCase_	,			default_to_square=UpperCAmelCase_)
   if "shortest_edge" in size:
     lowerCamelCase__:				Tuple        =get_resize_output_image_size(UpperCAmelCase_	,			size["shortest_edge"]	,			default_to_square=UpperCAmelCase_)
   elif "height" in size and "width" in size:
     lowerCamelCase__:				Union[str, Any]        =(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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       Any	,			)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Union[int, float]	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       Optional[int]	,			)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =image.astype(np.floataa)
   if offset:
     lowerCamelCase__:				Any        =image - (scale / 2)
   return rescale(UpperCAmelCase_	,			scale=UpperCAmelCase_	,			data_format=UpperCAmelCase_	,			**UpperCAmelCase_)

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       ImageInput	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       PILImageResampling = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       float = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[ChannelDimension] = ChannelDimension.FIRST	,			)	->np.ndarray:



   '''simple docstring'''



   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__:				int        =to_numpy_array(UpperCAmelCase_)

   if do_resize:
     lowerCamelCase__:				Tuple        =self.resize(image=UpperCAmelCase_	,			size=UpperCAmelCase_	,			resample=UpperCAmelCase_)

   if do_center_crop:
     lowerCamelCase__:				Tuple        =self.center_crop(UpperCAmelCase_	,			size=UpperCAmelCase_)

   if do_rescale:
     lowerCamelCase__:				Optional[Any]        =self.rescale(image=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			offset=UpperCAmelCase_)

   if do_normalize:
     lowerCamelCase__:				str        =self.normalize(image=UpperCAmelCase_	,			mean=UpperCAmelCase_	,			std=UpperCAmelCase_)

   lowerCamelCase__:				List[str]        =to_channel_dimension_format(UpperCAmelCase_	,			UpperCAmelCase_)
   return image







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       ImageInput	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       PILImageResampling = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       float = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       bool = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[Union[str, TensorType]] = None	,			UpperCAmelCase_    :       ChannelDimension = ChannelDimension.FIRST	,			**UpperCAmelCase_    :       Dict	,			)	->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:				Any        =do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:				Any        =resample if resample is not None else self.resample
   lowerCamelCase__:				Optional[int]        =do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase__:				int        =do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:				List[str]        =rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:				Optional[Any]        =offset if offset is not None else self.offset
   lowerCamelCase__:				int        =do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:				Dict        =image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:				int        =image_std if image_std is not None else self.image_std

   lowerCamelCase__:				Union[str, Any]        =size if size is not None else self.size
   lowerCamelCase__:				str        =get_size_dict(UpperCAmelCase_	,			default_to_square=UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =crop_size if crop_size is not None else self.crop_size
   lowerCamelCase__:				List[str]        =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__:				List[Any]        =make_batched(UpperCAmelCase_)

   lowerCamelCase__:				Dict        =[
       [
           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__:				Tuple        ={"pixel_values": videos}
   return BatchFeature(data=UpperCAmelCase_	,			tensor_type=UpperCAmelCase_)













from ..utils import DummyObject, requires_backends



class        _SCREAMING_SNAKE_CASE						(     metaclass=__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["torch", "torchsde"]

 def __init__(self    :       Union[str, Any]	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Tuple)	->Any:



   '''simple docstring'''



   requires_backends(self	,			["torch", "torchsde"])

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Dict	,			*UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])







 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Tuple	,			*UpperCAmelCase_    :       Tuple	,			**UpperCAmelCase_    :       Any)	->Any:



   '''simple docstring'''



   requires_backends(cls	,			["torch", "torchsde"])

