





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowercase__						:      Optional[int]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					int						    =   ['pixel_values']





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

            '''simple docstring'''




            super().__init__(**lowerCAmelCase__					)
            _UpperCamelCase       =	size if size is not None else {'''shortest_edge''': 384}
            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)

            _UpperCamelCase       =	do_resize
            _UpperCamelCase       =	size
            # Default value set here for backwards compatibility where the value in config is None
            _UpperCamelCase       =	crop_pct if crop_pct is not None else 224 / 256
            _UpperCamelCase       =	resample
            _UpperCamelCase       =	do_rescale
            _UpperCamelCase       =	rescale_factor
            _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 snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Dict[str, int]	,			lowerCAmelCase__       :		float	,			lowerCAmelCase__       :		PILImageResampling = PILImageResampling.BICUBIC	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					np.ndarray:

            '''simple docstring'''




            _UpperCamelCase       =	get_size_dict(lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)
            if "shortest_edge" not in size:
                 raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
            _UpperCamelCase       =	size['''shortest_edge''']

            if shortest_edge < 384:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 _UpperCamelCase       =	int(shortest_edge / crop_pct					)
                 _UpperCamelCase       =	get_resize_output_image_size(lowerCAmelCase__	,			size=lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)
                 _UpperCamelCase       =	resize(image=lowerCAmelCase__	,			size=lowerCAmelCase__	,			resample=lowerCAmelCase__	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=lowerCAmelCase__	,			size=(shortest_edge, shortest_edge)	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)
            else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     lowerCAmelCase__	,			size=(shortest_edge, shortest_edge)	,			resample=lowerCAmelCase__	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Union[int, float]	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		int	,			)   ->					Optional[Any]:

            '''simple docstring'''




            return rescale(lowerCAmelCase__	,			scale=lowerCAmelCase__	,			data_format=lowerCAmelCase__	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Union[float, List[float]]	,			lowerCAmelCase__       :		Union[float, List[float]]	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		Tuple	,			)   ->					np.ndarray:

            '''simple docstring'''




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







       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		ImageInput	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		Dict[str, int] = None	,			lowerCAmelCase__       :		float = None	,			lowerCAmelCase__       :		PILImageResampling = None	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		float = 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__       :		Optional[Any]	,			)   ->					PIL.Image.Image:

            '''simple docstring'''




            _UpperCamelCase       =	do_resize if do_resize is not None else self.do_resize
            _UpperCamelCase       =	crop_pct if crop_pct is not None else self.crop_pct
            _UpperCamelCase       =	resample if resample is not None else self.resample
            _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       =	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(lowerCAmelCase__	,			default_to_square=lowerCAmelCase__					)

            _UpperCamelCase       =	make_list_of_images(lowerCAmelCase__					)

            if not valid_images(lowerCAmelCase__					):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                 raise ValueError('''crop_pct must be specified if size < 384.'''					)

            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.'''					)

            # All transformations expect numpy arrays.
            _UpperCamelCase       =	[to_numpy_array(lowerCAmelCase__					) for image in images]

            if do_resize:
                 _UpperCamelCase       =	[self.resize(image=lowerCAmelCase__	,			size=lowerCAmelCase__	,			crop_pct=lowerCAmelCase__	,			resample=lowerCAmelCase__					) for image in images]

            if do_rescale:
                 _UpperCamelCase       =	[self.rescale(image=lowerCAmelCase__	,			scale=lowerCAmelCase__					) for image in images]

            if do_normalize:
                 _UpperCamelCase       =	[self.normalize(image=lowerCAmelCase__	,			mean=lowerCAmelCase__	,			std=lowerCAmelCase__					) for image in images]

            _UpperCamelCase       =	[to_channel_dimension_format(lowerCAmelCase__	,			lowerCAmelCase__					) for image in images]

            _UpperCamelCase       =	{'''pixel_values''': images}
            return BatchFeature(data=lowerCAmelCase__	,			tensor_type=lowerCAmelCase__					)










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



