



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 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= tempfile.mkdtemp()

											# fmt: off
											a__	:							Any      						= ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
											# fmt: on
											a__	:							List[Any]      						= 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__	:							Tuple      						= {
											    '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__	:							Union[str, Any]      						= os.path.join(self.tmpdirname ,					SCREAMING_SNAKE_CASE_				)
											with open(self.image_processor_file ,					"w" ,					encoding="utf-8"				) as fp:
															json.dump(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



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



							def 		_A	(							self ,					**__UpperCAmelCase				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



											shutil.rmtree(self.tmpdirname				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= [np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta				)]

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

											return image_inputs



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.get_tokenizer()
											a__	:							Optional[int]      						= self.get_image_processor()

											a__	:							Union[str, Any]      						= VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,					image_processor=SCREAMING_SNAKE_CASE_				)

											processor.save_pretrained(self.tmpdirname				)
											a__	:							Tuple      						= 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 ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



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

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

											a__	:							List[str]      						= VisionTextDualEncoderProcessor.from_pretrained(
											    self.tmpdirname ,					bos_token="(BOS)" ,					eos_token="(EOS)" ,					do_normalize=SCREAMING_SNAKE_CASE_ ,					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 ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.get_image_processor()
											a__	:							List[str]      						= self.get_tokenizer()

											a__	:							int      						= VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,					image_processor=SCREAMING_SNAKE_CASE_				)

											a__	:							Any      						= self.prepare_image_inputs()

											a__	:							Optional[int]      						= image_processor(SCREAMING_SNAKE_CASE_ ,					return_tensors="np"				)
											a__	:							Union[str, Any]      						= processor(images=SCREAMING_SNAKE_CASE_ ,					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 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.get_image_processor()
											a__	:							str      						= self.get_tokenizer()

											a__	:							Any      						= VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,					image_processor=SCREAMING_SNAKE_CASE_				)

											a__	:							List[Any]      						= 'lower newer'

											a__	:							Optional[int]      						= processor(text=SCREAMING_SNAKE_CASE_				)

											a__	:							List[Any]      						= tokenizer(SCREAMING_SNAKE_CASE_				)

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



							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							int      						= VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,					image_processor=SCREAMING_SNAKE_CASE_				)

											a__	:							int      						= 'lower newer'
											a__	:							List[str]      						= self.prepare_image_inputs()

											a__	:							Any      						= processor(text=SCREAMING_SNAKE_CASE_ ,					images=SCREAMING_SNAKE_CASE_				)

											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(SCREAMING_SNAKE_CASE_				):
															processor()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.get_image_processor()
											a__	:							int      						= self.get_tokenizer()

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

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

											a__	:							int      						= processor.batch_decode(SCREAMING_SNAKE_CASE_				)
											a__	:							Tuple      						= tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				)

											self.assertListEqual(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_				)




							def 		_A	(							self				):







											"""simple docstring"""



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

											a__	:							int      						= VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,					image_processor=SCREAMING_SNAKE_CASE_				)

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

											a__	:							Any      						= processor(text=SCREAMING_SNAKE_CASE_ ,					images=SCREAMING_SNAKE_CASE_				)

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






'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
      print(f'''{solution() = }''')






