

"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			logging.get_logger(__name__)


class 		_A       (			__a					):
      __a								=	['pixel_values']





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = 1 / 255    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					None:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			size if size is not None else {"height": 224, "width": 224}
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						default_to_square=SCREAMING_SNAKE_CASE__    ,						param_name="crop_size"		)

         lowerCamelCase__						=			do_resize
         lowerCamelCase__						=			do_rescale
         lowerCamelCase__						=			do_normalize
         lowerCamelCase__						=			do_center_crop
         lowerCamelCase__						=			crop_size
         lowerCamelCase__						=			size
         lowerCamelCase__						=			resample
         lowerCamelCase__						=			rescale_factor
         lowerCamelCase__						=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         lowerCamelCase__						=			image_std if image_std is not None else IMAGENET_DEFAULT_STD





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)

         if "shortest_edge" in size:
            lowerCamelCase__						=			get_resize_output_image_size(SCREAMING_SNAKE_CASE__    ,						size=size["shortest_edge"]    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
            # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
            lowerCamelCase__						=			(size["height"], size["width"])
         else:
            raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. 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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         if "height" not in size or "width" not in size:
            raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
         return center_crop(SCREAMING_SNAKE_CASE__    ,						size=(size["height"], size["width"])    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__		)   ->					np.ndarray:
         return rescale(SCREAMING_SNAKE_CASE__    ,						scale=SCREAMING_SNAKE_CASE__    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__    ,						mean=SCREAMING_SNAKE_CASE__    ,						std=SCREAMING_SNAKE_CASE__    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					BatchFeature:
         lowerCamelCase__						=			do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__						=			do_rescale if do_rescale is not None else self.do_rescale
         lowerCamelCase__						=			do_normalize if do_normalize is not None else self.do_normalize
         lowerCamelCase__						=			do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCamelCase__						=			crop_size if crop_size is not None else self.crop_size
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__    ,						param_name="crop_size"    ,						default_to_square=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			resample if resample is not None else self.resample
         lowerCamelCase__						=			rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCamelCase__						=			image_mean if image_mean is not None else self.image_mean
         lowerCamelCase__						=			image_std if image_std is not None else self.image_std

         lowerCamelCase__						=			size if size is not None else self.size
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)

         if not is_batched(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			[images]

         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."		)

         if do_resize and size is None:
            raise ValueError("Size 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."		)

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

         if do_resize:
            lowerCamelCase__						=			[self.resize(image=SCREAMING_SNAKE_CASE__    ,						size=SCREAMING_SNAKE_CASE__    ,						resample=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_center_crop:
            lowerCamelCase__						=			[self.center_crop(image=SCREAMING_SNAKE_CASE__    ,						size=SCREAMING_SNAKE_CASE__		) for image in images]

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

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

         lowerCamelCase__						=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						=			{"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__    ,						tensor_type=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      str:




   """simple docstring"""
   return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




