


'''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, 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


lowerCAmelCase    :     Dict					=						logging.get_logger(__name__)
class 	UpperCamelCase__					( lowercase_	):



     """simple docstring"""






     __magic_name__    			=    ['''pixel_values''']







     def __init__( self      ,	snake_case__ = True      ,	snake_case__ = None      ,	snake_case__ = PIL.Image.BICUBIC      ,	snake_case__ = True      ,	snake_case__ = None      ,	snake_case__ = 1 / 255      ,	snake_case__ = True      ,	snake_case__ = True      ,	snake_case__ = None      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       size if size is not None else {'height': 256, 'width': 256}
            _lowerCAmelCase    :							Optional[int]   			=       get_size_dict(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _lowerCAmelCase    :							Optional[Any]   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

            _lowerCAmelCase    :							str   			=       do_resize
            _lowerCAmelCase    :							str   			=       size
            _lowerCAmelCase    :							Any   			=       resample
            _lowerCAmelCase    :							str   			=       do_center_crop
            _lowerCAmelCase    :							str   			=       crop_size
            _lowerCAmelCase    :							List[Any]   			=       do_rescale
            _lowerCAmelCase    :							List[Any]   			=       rescale_factor
            _lowerCAmelCase    :							Tuple   			=       do_normalize
            _lowerCAmelCase    :							List[Any]   			=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _lowerCAmelCase    :							Any   			=       image_std if image_std is not None else IMAGENET_STANDARD_STD







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = PIL.Image.BICUBIC      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       get_size_dict(snake_case__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'   )
            return resize(
                snake_case__      ,	size=(size['height'], size['width'])      ,	resample=snake_case__      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       get_size_dict(snake_case__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'   )
            return center_crop(snake_case__      ,	size=(size['height'], size['width'])      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

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







     def a	( 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      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase    :							Dict   			=       resample if resample is not None else self.resample
            _lowerCAmelCase    :							List[str]   			=       do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase    :							Optional[int]   			=       do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase    :							str   			=       rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase    :							str   			=       do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase    :							Dict   			=       image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase    :							Tuple   			=       image_std if image_std is not None else self.image_std

            _lowerCAmelCase    :							List[str]   			=       size if size is not None else self.size
            _lowerCAmelCase    :							Union[str, Any]   			=       get_size_dict(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase    :							Dict   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

            _lowerCAmelCase    :							Optional[int]   			=       make_list_of_images(snake_case__   )

            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.'   )

            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.'   )

            # All transformations expect numpy arrays.
            _lowerCAmelCase    :							str   			=       [to_numpy_array(snake_case__   ) for image in images]

            if do_resize:
                   _lowerCAmelCase    :							str   			=       [self.resize(image=snake_case__      ,	size=snake_case__      ,	resample=snake_case__   ) for image in images]

            if do_center_crop:
                   _lowerCAmelCase    :							str   			=       [self.center_crop(image=snake_case__      ,	size=snake_case__   ) for image in images]

            if do_rescale:
                   _lowerCAmelCase    :							Optional[Any]   			=       [self.rescale(image=snake_case__      ,	scale=snake_case__   ) for image in images]

            if do_normalize:
                   _lowerCAmelCase    :							int   			=       [self.normalize(image=snake_case__      ,	mean=snake_case__      ,	std=snake_case__   ) for image in images]

            _lowerCAmelCase    :							Optional[Any]   			=       [to_channel_dimension_format(snake_case__      ,	snake_case__   ) for image in images]

            _lowerCAmelCase    :							Dict   			=       {'pixel_values': images}
            return BatchFeature(data=snake_case__      ,	tensor_type=snake_case__   )





'''simple docstring'''







from collections import Counter
from timeit import timeit






def 			lowercase				(_A = ""   ,						):







       """simple docstring"""







       return sum(c % 2 for c in Counter(input_str.replace(' '   ,						''					).lower()					).values()					) < 2






def 			lowercase				(_A = ""					):







       """simple docstring"""







       if len(_A					) == 0:
              return True
       _lowerCAmelCase    :							Union[str, Any]   			=       input_str.replace(' '   ,						''					).lower()
       # character_freq_dict: Stores the frequency of every character in the input string
       _lowerCAmelCase    :							dict[str, int]   			=       {}

       for character in lower_case_input_str:
              _lowerCAmelCase    :							Union[str, Any]   			=       character_freq_dict.get(_A   ,						0					) + 1
       _lowerCAmelCase    :							List[Any]   			=       0

       for character_count in character_freq_dict.values():
              if character_count % 2:
                     odd_char += 1
       if odd_char > 1:
              return False
       return True






def 			lowercase				(_A = ""					):







       """simple docstring"""







       print('\nFor string = '   ,						_A   ,						':'					)
       print(
           '> can_string_be_rearranged_as_palindrome_counter()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome_counter(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)
       print(
           '> can_string_be_rearranged_as_palindrome()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						input(
           """Enter string to determine if it can be rearranged as a palindrome or not: """
       ).strip()
       benchmark(check_str)
       lowerCAmelCase    :     Optional[Any]					=						can_string_be_rearranged_as_palindrome_counter(check_str)
       print(F'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')

