





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 :   str  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					)    ->    List[List[ImageInput]]:







			"""simple docstring"""
			if isinstance(UpperCAmelCase      ,  (list, tuple)					) and isinstance(videos[0]      ,  (list, tuple)					) and is_valid_image(videos[0][0]					):
						return videos

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

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

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




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
							_UpperCAmelCase				:  Optional[int]			   =   ["pixel_values"]







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

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







							def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     np.ndarray		, A					:     Dict[str, int]		, A					:     PILImageResampling = PILImageResampling.BILINEAR		, A					:     Optional[Union[str, ChannelDimension]] = None		, **A					:     str		, )       ->np.ndarray:
										lowerCamelCase__       :						Any        =							get_size_dict(A		, default_to_square=A      )
										if "shortest_edge" in size:
													lowerCamelCase__       :						Dict        =							get_resize_output_image_size(A		, size['''shortest_edge''']		, default_to_square=A      )
										elif "height" in size and "width" in size:
													lowerCamelCase__       :						List[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(A		, size=A		, resample=A		, data_format=A		, **A      )







							def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     np.ndarray		, A					:     Dict[str, int]		, A					:     Optional[Union[str, ChannelDimension]] = None		, **A					:     Dict		, )       ->np.ndarray:
										lowerCamelCase__       :						Optional[Any]        =							get_size_dict(A      )
										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(A		, size=(size['''height'''], size['''width'''])		, data_format=A		, **A      )







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







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







							def 						__lowerCamelCase   (				self					:     Any		, A					:     ImageInput		, A					:     bool = None		, A					:     Dict[str, int] = None		, A					:     PILImageResampling = None		, A					:     bool = None		, A					:     Dict[str, int] = None		, A					:     bool = None		, A					:     float = None		, A					:     bool = None		, A					:     bool = None		, A					:     Optional[Union[float, List[float]]] = None		, A					:     Optional[Union[float, List[float]]] = None		, A					:     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.
										lowerCamelCase__       :						Optional[int]        =							to_numpy_array(A      )

										if do_resize:
													lowerCamelCase__       :						Union[str, Any]        =							self.resize(image=A		, size=A		, resample=A      )

										if do_center_crop:
													lowerCamelCase__       :						int        =							self.center_crop(A		, size=A      )

										if do_rescale:
													lowerCamelCase__       :						Tuple        =							self.rescale(image=A		, scale=A		, offset=A      )

										if do_normalize:
													lowerCamelCase__       :						Dict        =							self.normalize(image=A		, mean=A		, std=A      )

										lowerCamelCase__       :						Dict        =							to_channel_dimension_format(A		, A      )
										return image







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

										lowerCamelCase__       :						int        =							size if size is not None else self.size
										lowerCamelCase__       :						Union[str, Any]        =							get_size_dict(A		, default_to_square=A      )
										lowerCamelCase__       :						List[Any]        =							crop_size if crop_size is not None else self.crop_size
										lowerCamelCase__       :						int        =							get_size_dict(A		, param_name='''crop_size'''      )

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

										lowerCamelCase__       :						Union[str, Any]        =							make_batched(A      )

										lowerCamelCase__       :						Optional[Any]        =							[
										    [
										        self._preprocess_image(
										            image=A		, do_resize=A		, size=A		, resample=A		, do_center_crop=A		, crop_size=A		, do_rescale=A		, rescale_factor=A		, offset=A		, do_normalize=A		, image_mean=A		, image_std=A		, data_format=A		, )
										        for img in video
										    ]
										    for video in videos
										]

										lowerCamelCase__       :						List[str]        =							{'''pixel_values''': videos}
										return BatchFeature(data=A		, tensor_type=A      )










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""

   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise TypeError('''only integers accepted as input'''					)
   else:
      lowerCamelCase__       :						Any        =							str(abs(UpperCAmelCase					)					)
      lowerCamelCase__       :						Union[str, Any]        =							[list(UpperCAmelCase					) for char in range(len(UpperCAmelCase					)					)]
      for index in range(len(UpperCAmelCase					)					):
         num_transpositions[index].pop(UpperCAmelCase					)
      return max(
          int(''''''.join(list(UpperCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
       __import__('doctest').testmod()



