






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       lowercase__	(					A_:						Tuple		)	->				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 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Dict				= ['pixel_values']



    def __init__(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = PILImageResampling.BILINEAR ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Union[int, float] = 1 / 255 ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     **__SCREAMING_SNAKE_CASE      :     Optional[int] ,     )      ->							None:
           super().__init__(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=size if size is not None else {"""shortest_edge""": 256}
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     param_name="""crop_size"""      )

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



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



    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     List[str] ,     )      ->							np.ndarray:
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE      )
           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(__SCREAMING_SNAKE_CASE ,     size=(size["""height"""], size["""width"""]) ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



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



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



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     ImageInput ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     float = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[ChannelDimension] = ChannelDimension.FIRST ,     )      ->							np.ndarray:
           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.
           __UpperCAmelCase     				=to_numpy_array(__SCREAMING_SNAKE_CASE      )

           if do_resize:
                  __UpperCAmelCase     				=self.resize(image=__SCREAMING_SNAKE_CASE ,     size=__SCREAMING_SNAKE_CASE ,     resample=__SCREAMING_SNAKE_CASE      )

           if do_center_crop:
                  __UpperCAmelCase     				=self.center_crop(__SCREAMING_SNAKE_CASE ,     size=__SCREAMING_SNAKE_CASE      )

           if do_rescale:
                  __UpperCAmelCase     				=self.rescale(image=__SCREAMING_SNAKE_CASE ,     scale=__SCREAMING_SNAKE_CASE ,     offset=__SCREAMING_SNAKE_CASE      )

           if do_normalize:
                  __UpperCAmelCase     				=self.normalize(image=__SCREAMING_SNAKE_CASE ,     mean=__SCREAMING_SNAKE_CASE ,     std=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           return image



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

           __UpperCAmelCase     				=size if size is not None else self.size
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=crop_size if crop_size is not None else self.crop_size
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     param_name="""crop_size"""      )

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

           __UpperCAmelCase     				=make_batched(__SCREAMING_SNAKE_CASE      )

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

           __UpperCAmelCase     				={"""pixel_values""": videos}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE ,     tensor_type=__SCREAMING_SNAKE_CASE      )










'''simple docstring'''

from ....utils import logging


a    :							Optional[int]										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Any=None			,							lowercase_       :   List[Any]=2048   ):
									snake_case_		     =							config.__dict__
									snake_case_		     =							modal_hidden_size
									if num_labels:
														snake_case_		     =							num_labels

