


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  tempfile.mkdtemp()

						# fmt: off
						__a      :	List[str]    =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						__a      :	Optional[int]    =  os.path.join(self.tmpdirname						,    VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file						,    'w'						,    encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)

						__a      :	Optional[Any]    =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						__a      :	Any    =  os.path.join(self.tmpdirname						,    __a							)
						with open(self.image_processor_file						,    'w'						,    encoding='utf-8'							) as fp:
											json.dump(__a						,    __a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return BertTokenizer.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ViTImageProcessor.from_pretrained(self.tmpdirname						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  [np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta							)]

						__a      :	str    =  [Image.fromarray(np.moveaxis(__a						,    0						,    -1							)							) for x in image_inputs]

						return image_inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_tokenizer()
						__a      :	int    =  self.get_image_processor()

						__a      :	Tuple    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	List[Any]    =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    (BertTokenizer, BertTokenizerFast)							)

						self.assertEqual(processor.image_processor.to_json_string()						,    image_processor.to_json_string()							)
						self.assertIsInstance(processor.image_processor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	Optional[int]    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	Any    =  self.get_image_processor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	int    =  VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    (BertTokenizer, BertTokenizerFast)							)

						self.assertEqual(processor.image_processor.to_json_string()						,    image_processor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.image_processor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_image_processor()
						__a      :	Dict    =  self.get_tokenizer()

						__a      :	Dict    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						__a      :	Union[str, Any]    =  self.prepare_image_inputs()

						__a      :	Optional[int]    =  image_processor(__a						,    return_tensors='np'							)
						__a      :	Tuple    =  processor(images=__a						,    return_tensors='np'							)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_image_processor()
						__a      :	Tuple    =  self.get_tokenizer()

						__a      :	Optional[Any]    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						__a      :	List[str]    =  'lower newer'

						__a      :	Any    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.get_image_processor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	Any    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						__a      :	Tuple    =  'lower newer'
						__a      :	Optional[Any]    =  self.prepare_image_inputs()

						__a      :	Optional[Any]    =  processor(text=__a						,    images=__a							)

						self.assertListEqual(list(inputs.keys()							)						,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']							)

						# test if it raises when no input is passed
						with self.assertRaises(__a							):
											processor()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_image_processor()
						__a      :	List[Any]    =  self.get_tokenizer()

						__a      :	Optional[Any]    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						__a      :	int    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Union[str, Any]    =  processor.batch_decode(__a							)
						__a      :	Any    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.get_image_processor()
						__a      :	List[Any]    =  self.get_tokenizer()

						__a      :	Dict    =  VisionTextDualEncoderProcessor(tokenizer=__a						,    image_processor=__a							)

						__a      :	int    =  'lower newer'
						__a      :	Any    =  self.prepare_image_inputs()

						__a      :	Optional[Any]    =  processor(text=__a						,    images=__a							)

						self.assertListEqual(list(inputs.keys()							)						,    processor.model_input_names							)







'''simple docstring'''



import os






def   lowerCamelCase							():
					with open(os.path.dirname(_SCREAMING_SNAKE_CASE       ) + '/p022_names.txt'       ) as file:
										__a      :	List[Any]    =  str(file.readlines()[0]       )
										__a      :	str    =  names.replace('"'						,				''       ).split(','       )

					names.sort()

					__a      :	Union[str, Any]    =  0
					__a      :	Tuple    =  0

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

										total_score += (i + 1) * name_score
										__a      :	Any    =  0
					return total_score


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



