



"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase				:				Any   		=		logging.get_logger(__name__)


if is_vision_available():
		import PIL

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							['pixel_values']



					def __init__( self							:						Optional[int],						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Dict[str, int] = None,						lowerCamelCase							:						PILImageResampling = PILImageResampling.BICUBIC,						lowerCamelCase							:						bool = True,						lowerCamelCase							:						Dict[str, int] = None,						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							:						bool = True,						**lowerCamelCase							:						int,						)->      None:
								super().__init__(**lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=size if size is not None else {'''shortest_edge''': 224}
								lowerCamelCase__	:	Dict				=get_size_dict(lowerCamelCase,						default_to_square=lowerCamelCase  )
								lowerCamelCase__	:	Dict				=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowerCamelCase__	:	List[Any]				=get_size_dict(lowerCamelCase,						default_to_square=lowerCamelCase,						param_name='''crop_size'''  )

								lowerCamelCase__	:	List[Any]				=do_resize
								lowerCamelCase__	:	List[str]				=size
								lowerCamelCase__	:	Optional[Any]				=resample
								lowerCamelCase__	:	Optional[int]				=do_center_crop
								lowerCamelCase__	:	str				=crop_size
								lowerCamelCase__	:	Optional[int]				=do_rescale
								lowerCamelCase__	:	List[str]				=rescale_factor
								lowerCamelCase__	:	List[str]				=do_normalize
								lowerCamelCase__	:	List[Any]				=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase__	:	Any				=image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase__	:	Optional[Any]				=do_convert_rgb



					def 	snake_case ( self							:						Tuple,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Dict[str, int],						lowerCamelCase							:						PILImageResampling = PILImageResampling.BICUBIC,						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						Tuple,						)->      np.ndarray:
								lowerCamelCase__	:	Union[str, Any]				=get_size_dict(lowerCamelCase,						default_to_square=lowerCamelCase  )
								if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
								lowerCamelCase__	:	Tuple				=get_resize_output_image_size(lowerCamelCase,						size=size['''shortest_edge'''],						default_to_square=lowerCamelCase  )
								return resize(lowerCamelCase,						size=lowerCamelCase,						resample=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Dict[str, int],						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						List[str],						)->      np.ndarray:
								lowerCamelCase__	:	List[Any]				=get_size_dict(lowerCamelCase  )
								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(lowerCamelCase,						size=(size['''height'''], size['''width''']),						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						int,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Union[int, float],						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						int,						)->      str:
								return rescale(lowerCamelCase,						scale=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						np.ndarray,						lowerCamelCase							:						Union[float, List[float]],						lowerCamelCase							:						Union[float, List[float]],						lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase							:						Tuple,						)->      np.ndarray:
								return normalize(lowerCamelCase,						mean=lowerCamelCase,						std=lowerCamelCase,						data_format=lowerCamelCase,						**lowerCamelCase  )




					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						ImageInput,						lowerCamelCase							:						bool = None,						lowerCamelCase							:						Dict[str, int] = None,						lowerCamelCase							:						PILImageResampling = None,						lowerCamelCase							:						bool = None,						lowerCamelCase							:						int = 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							:						bool = None,						lowerCamelCase							:						Optional[Union[str, TensorType]] = None,						lowerCamelCase							:						Optional[ChannelDimension] = ChannelDimension.FIRST,						**lowerCamelCase							:						Union[str, Any],						)->      PIL.Image.Image:
								lowerCamelCase__	:	Optional[int]				=do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__	:	Any				=size if size is not None else self.size
								lowerCamelCase__	:	List[Any]				=get_size_dict(lowerCamelCase,						param_name='''size''',						default_to_square=lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=resample if resample is not None else self.resample
								lowerCamelCase__	:	str				=do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase__	:	List[str]				=crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__	:	Union[str, Any]				=get_size_dict(lowerCamelCase,						param_name='''crop_size''',						default_to_square=lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__	:	List[str]				=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__	:	Dict				=do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__	:	Tuple				=image_mean if image_mean is not None else self.image_mean
								lowerCamelCase__	:	Optional[int]				=image_std if image_std is not None else self.image_std
								lowerCamelCase__	:	Optional[Any]				=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase__	:	str				=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:
											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.'''  )

								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.'''  )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
											lowerCamelCase__	:	Any				=[convert_to_rgb(lowerCamelCase  ) for image in images]

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

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

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

								if do_rescale:
											lowerCamelCase__	:	Union[str, Any]				=[self.rescale(image=lowerCamelCase,						scale=lowerCamelCase  ) for image in images]

								if do_normalize:
											lowerCamelCase__	:	Union[str, Any]				=[self.normalize(image=lowerCamelCase,						mean=lowerCamelCase,						std=lowerCamelCase  ) for image in images]

								lowerCamelCase__	:	Union[str, Any]				=[to_channel_dimension_format(lowerCamelCase,						lowerCamelCase  ) for image in images]

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









"""simple docstring"""






import os






def   snake_case__     (	):
			"""simple docstring"""
			with open(os.path.dirname(__lowerCamelCase			) + '''/p022_names.txt'''			) as file:
						lowerCamelCase__	:	Tuple				=str(file.readlines()[0]			)
						lowerCamelCase__	:	int				=names.replace('''"'''							,	''''''			).split(''','''			)

			names.sort()

			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	str				=0

			for i, name in enumerate(__lowerCamelCase			):
						for letter in name:
									name_score += ord(__lowerCamelCase			) - 64

						total_score += (i + 1) * name_score
						lowerCamelCase__	:	Dict				=0
			return total_score


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




