






'''simple docstring'''

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor







class 						SCREAMING_SNAKE_CASE						(   unittest.TestCase	):




       def __init__(       self   :			Any						,     A__   :			str						,     A__   :			Union[str, Any]=7						,     A__   :			str=3						,     A__   :			str=18						,     A__   :			Optional[Any]=30						,     A__   :			Optional[int]=400						,     A__   :			int=True						,     A__   :			Any=None						,     A__   :			Tuple=True						,     A__   :			Any=[0.5, 0.5, 0.5]						,     A__   :			str=[0.5, 0.5, 0.5]						,     ):


            """simple docstring"""







            __lowerCamelCase    :							str					 =					size if size is not None else {'''height''': 18, '''width''': 18}
            __lowerCamelCase    :							Optional[int]					 =					parent
            __lowerCamelCase    :							Optional[Any]					 =					batch_size
            __lowerCamelCase    :							List[str]					 =					num_channels
            __lowerCamelCase    :							List[Any]					 =					image_size
            __lowerCamelCase    :							List[Any]					 =					min_resolution
            __lowerCamelCase    :							List[str]					 =					max_resolution
            __lowerCamelCase    :							Tuple					 =					do_resize
            __lowerCamelCase    :							Any					 =					size
            __lowerCamelCase    :							Optional[Any]					 =					do_normalize
            __lowerCamelCase    :							Union[str, Any]					 =					image_mean
            __lowerCamelCase    :							str					 =					image_std




       def        a_       (       self   :			int       ):


            """simple docstring"""







            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
            }







@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE						(   lowerCamelCase__     , unittest.TestCase	):
       snake_case__     :  str 		=      DPTImageProcessor if is_vision_available() else None




       def        a_       (       self   :			Optional[int]       ):


            """simple docstring"""







            __lowerCamelCase    :							List[Any]					 =					DPTImageProcessingTester(self       )




       @property
       def        a_       (       self   :			Dict       ):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def        a_       (       self   :			List[str]       ):


            """simple docstring"""







            __lowerCamelCase    :							str					 =					self.image_processing_class(**self.image_processor_dict       )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,     """image_mean"""       )       )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,     """image_std"""       )       )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,     """do_normalize"""       )       )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,     """do_resize"""       )       )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,     """size"""       )       )




       def        a_       (       self   :			Union[str, Any]       ):


            """simple docstring"""







            __lowerCamelCase    :							str					 =					self.image_processing_class.from_dict(self.image_processor_dict       )
            self.assertEqual(image_processor.size						,     {"""height""": 18, """width""": 18}       )

            __lowerCamelCase    :							Dict					 =					self.image_processing_class.from_dict(self.image_processor_dict						,     size=42       )
            self.assertEqual(image_processor.size						,     {"""height""": 42, """width""": 42}       )




       def        a_       (       self   :			int       ):


            """simple docstring"""







            __lowerCamelCase    :							Dict					 =					self.image_processing_class(**self.image_processor_dict       )
            # create random PIL images
            __lowerCamelCase    :							List[Any]					 =					prepare_image_inputs(self.image_processor_tester						,     equal_resolution=SCREAMING_SNAKE_CASE__       )
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE__						,     Image.Image       )

            # Test not batched input
            __lowerCamelCase    :							Optional[Any]					 =					image_processing(image_inputs[0]						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )

            # Test batched
            __lowerCamelCase    :							Dict					 =					image_processing(SCREAMING_SNAKE_CASE__						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )




       def        a_       (       self   :			Union[str, Any]       ):


            """simple docstring"""







            __lowerCamelCase    :							int					 =					self.image_processing_class(**self.image_processor_dict       )
            # create random numpy tensors
            __lowerCamelCase    :							Tuple					 =					prepare_image_inputs(self.image_processor_tester						,     equal_resolution=SCREAMING_SNAKE_CASE__						,     numpify=SCREAMING_SNAKE_CASE__       )
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE__						,     np.ndarray       )

            # Test not batched input
            __lowerCamelCase    :							Optional[int]					 =					image_processing(image_inputs[0]						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )

            # Test batched
            __lowerCamelCase    :							Tuple					 =					image_processing(SCREAMING_SNAKE_CASE__						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )




       def        a_       (       self   :			str       ):


            """simple docstring"""







            __lowerCamelCase    :							List[Any]					 =					self.image_processing_class(**self.image_processor_dict       )
            # create random PyTorch tensors
            __lowerCamelCase    :							List[str]					 =					prepare_image_inputs(self.image_processor_tester						,     equal_resolution=SCREAMING_SNAKE_CASE__						,     torchify=SCREAMING_SNAKE_CASE__       )
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE__						,     torch.Tensor       )

            # Test not batched input
            __lowerCamelCase    :							List[str]					 =					image_processing(image_inputs[0]						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )

            # Test batched
            __lowerCamelCase    :							Union[str, Any]					 =					image_processing(SCREAMING_SNAKE_CASE__						,     return_tensors="""pt"""       ).pixel_values
            self.assertEqual(
                encoded_images.shape						,     (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )						,     )







"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

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

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

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


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

