


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


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

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   ["""pixel_values"""]

       def __init__(      self						,    a__ = True						,    a__ = None						,    a__ = PIL.Image.NEAREST						,    a__ = False						,    a__ = None						,    a__ = 1 / 2_55						,    a__ = False						,    a__ = True						,    a__ = True						,    a__ = None						,    a__ = None						,    a__ = True						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__       )
        snake_case :			Any							=   size if size is not None else {'''height''': 3_46, '''width''': 3_46}
        snake_case :			Optional[int]							=   get_size_dict(a__       )
        snake_case :			List[Any]							=   crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
        snake_case :			Optional[Any]							=   get_size_dict(a__						,    param_name='''crop_size'''       )

        snake_case :			int							=   do_resize
        snake_case :			List[Any]							=   size
        snake_case :			int							=   resample
        snake_case :			str							=   do_center_crop
        snake_case :			List[str]							=   crop_size
        snake_case :			int							=   do_rescale
        snake_case :			Dict							=   rescale_factor
        snake_case :			int							=   rescale_offset
        snake_case :			int							=   do_normalize
        snake_case :			List[str]							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        snake_case :			Any							=   image_std if image_std is not None else IMAGENET_STANDARD_STD
        snake_case :			Optional[Any]							=   include_top

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__ = PIL.Image.NEAREST						,    a__ = None						,    **a__						,    ):






        '''simple docstring'''






        snake_case :			str							=   get_size_dict(a__       )
        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(
            a__						,    size=(size['''height'''], size['''width'''])						,    resample=a__						,    data_format=a__						,    **a__       )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__ = None						,    **a__						,    ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   get_size_dict(a__       )
        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(a__						,    size=(size['''height'''], size['''width'''])						,    data_format=a__						,    **a__       )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__ = True						,    a__ = None						,    **a__						,    ):






        '''simple docstring'''






        if offset:
         snake_case :			str							=   (image - 127.5) * scale
         if data_format is not None:
          snake_case :			List[str]							=   to_channel_dimension_format(a__						,    a__       )
         snake_case :			List[Any]							=   rescaled_image.astype(np.floataa       )
        else:
         snake_case :			Optional[int]							=   rescale(a__						,    scale=a__						,    data_format=a__						,    **a__       )
        return rescaled_image

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__ = None						,    **a__						,    ):






        '''simple docstring'''






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



       def 					UpperCAmelCase					(      self						,    a__						,    a__ = None						,    a__ = None						,    a__=None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = ChannelDimension.FIRST						,    **a__						,    ):






        '''simple docstring'''






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

        snake_case :			Union[str, Any]							=   size if size is not None else self.size
        snake_case :			Optional[Any]							=   get_size_dict(a__       )
        snake_case :			Any							=   crop_size if crop_size is not None else self.crop_size
        snake_case :			Optional[int]							=   get_size_dict(a__						,    param_name='''crop_size'''       )

        snake_case :			Dict							=   make_list_of_images(a__       )

        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.'''       )

        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.
        snake_case :			Tuple							=   [to_numpy_array(a__       ) for image in images]

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

        if do_center_crop:
         snake_case :			Union[str, Any]							=   [self.center_crop(image=a__						,    size=a__       ) for image in images]

        if do_rescale:
         snake_case :			Dict							=   [self.rescale(image=a__						,    scale=a__						,    offset=a__       ) for image in images]

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

        if include_top:
         snake_case :			List[str]							=   [self.normalize(image=a__						,    mean=[0, 0, 0]						,    std=a__       ) for image in images]

        snake_case :			int							=   [to_channel_dimension_format(a__						,    a__       ) for image in images]

        snake_case :			Union[str, Any]							=   {'''pixel_values''': images}
        return BatchFeature(data=a__						,    tensor_type=a__       )










import os






def  _UpperCAmelCase						( )		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   0
 snake_case :			int							=   0
 with open(os.path.dirname(__lowerCamelCase							) + '''/p022_names.txt'''							) as file:
  snake_case :			int							=   str(file.readlines()[0]							)
  snake_case :			Any							=   name.replace('''"'''			,  ''''''							).split(''','''							)

 name.sort()
 for i in range(len(__lowerCamelCase							)							):
  for j in name[i]:
   temp_sum += ord(__lowerCamelCase							) - ord('''A'''							) + 1
  total_sum += (i + 1) * temp_sum
  snake_case :			List[Any]							=   0
 return total_sum


if __name__ == "__main__":
  print(solution())






