





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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[List[ImageInput]]:




    """simple docstring"""




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

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

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

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







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['pixel_values']




     def __init__(    self				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = PILImageResampling.BILINEAR				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = True				,     snake_case_ = 1 / 255				,     snake_case_ = True				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         super().__init__(**snake_case_							)
         _A     =							size if size is not None else {'shortest_edge': 256}
         _A     =							get_size_dict(snake_case_				,     default_to_square=snake_case_							)
         _A     =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
         _A     =							get_size_dict(snake_case_				,     param_name='crop_size'							)

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = PILImageResampling.BILINEAR				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							get_size_dict(snake_case_				,     default_to_square=snake_case_							)
         if "shortest_edge" in size:
             _A     =							get_resize_output_image_size(snake_case_				,     size['shortest_edge']				,     default_to_square=snake_case_							)
         elif "height" in size and "width" in size:
             _A     =							(size['height'], size['width'])
         else:
             raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"							)
         return resize(snake_case_				,     size=snake_case_				,     resample=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							get_size_dict(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(snake_case_				,     size=(size['height'], size['width'])				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = True				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							image.astype(np.floataa							)
         if offset:
             _A     =							image - (scale / 2)
         return rescale(snake_case_				,     scale=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         return normalize(snake_case_				,     mean=snake_case_				,     std=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = ChannelDimension.FIRST				,     ):
         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.
         _A     =							to_numpy_array(snake_case_							)

         if do_resize:
             _A     =							self.resize(image=snake_case_				,     size=snake_case_				,     resample=snake_case_							)

         if do_center_crop:
             _A     =							self.center_crop(snake_case_				,     size=snake_case_							)

         if do_rescale:
             _A     =							self.rescale(image=snake_case_				,     scale=snake_case_				,     offset=snake_case_							)

         if do_normalize:
             _A     =							self.normalize(image=snake_case_				,     mean=snake_case_				,     std=snake_case_							)

         _A     =							to_channel_dimension_format(snake_case_				,     snake_case_							)
         return image




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = ChannelDimension.FIRST				,     **snake_case_				,     ):
         _A     =							do_resize if do_resize is not None else self.do_resize
         _A     =							resample if resample is not None else self.resample
         _A     =							do_center_crop if do_center_crop is not None else self.do_center_crop
         _A     =							do_rescale if do_rescale is not None else self.do_rescale
         _A     =							rescale_factor if rescale_factor is not None else self.rescale_factor
         _A     =							offset if offset is not None else self.offset
         _A     =							do_normalize if do_normalize is not None else self.do_normalize
         _A     =							image_mean if image_mean is not None else self.image_mean
         _A     =							image_std if image_std is not None else self.image_std

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

         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.'							)

         _A     =							make_batched(snake_case_							)

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

         _A     =							{'pixel_values': videos}
         return BatchFeature(data=snake_case_				,     tensor_type=snake_case_							)












'''simple docstring'''




from math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



