


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class 					lowerCAmelCase_    (       unittest.TestCase):




						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    tempfile.mkdtemp()

									a__ :List[str]						  =    BlipImageProcessor()
									a__ :List[Any]						  =    BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"						)

									a__ :Dict						  =    BlipProcessor(__A   ,     __A						)

									processor.save_pretrained(self.tmpdirname						)




						def  _snake_case					(	self						:   Tuple   ,     **__A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									return AutoProcessor.from_pretrained(self.tmpdirname   ,     **__A						).tokenizer




						def  _snake_case					(	self						:   List[str]   ,     **__A						:   str						)      ->str:






									"""simple docstring"""






									return AutoProcessor.from_pretrained(self.tmpdirname   ,     **__A						).image_processor




						def  _snake_case					(	self						:   Union[str, Any]						)      ->Dict:






									"""simple docstring"""






									shutil.rmtree(self.tmpdirname						)




						def  _snake_case					(	self						:   Dict						)      ->str:






									"""simple docstring"""






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

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

									return image_inputs




						def  _snake_case					(	self						:   List[Any]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :List[str]						  =    BlipProcessor(tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									a__ :Union[str, Any]						  =    self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"						)
									a__ :str						  =    self.get_image_processor(do_normalize=__A   ,     padding_value=1.0						)

									a__ :Dict						  =    BlipProcessor.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   ,     __A						)

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




						def  _snake_case					(	self						:   List[Any]						)      ->Union[str, Any]:






									"""simple docstring"""






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

									a__ :List[Any]						  =    BlipProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :str						  =    self.prepare_image_inputs()

									a__ :List[Any]						  =    image_processor(__A   ,     return_tensors="np"						)
									a__ :List[Any]						  =    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  _snake_case					(	self						:   Dict						)      ->Dict:






									"""simple docstring"""






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

									a__ :Optional[int]						  =    BlipProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :Optional[Any]						  =    "lower newer"

									a__ :Tuple						  =    processor(text=__A						)

									a__ :str						  =    tokenizer(__A   ,     return_token_type_ids=__A						)

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




						def  _snake_case					(	self						:   List[str]						)      ->Dict:






									"""simple docstring"""






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

									a__ :str						  =    BlipProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :Dict						  =    "lower newer"
									a__ :List[str]						  =    self.prepare_image_inputs()

									a__ :int						  =    processor(text=__A   ,     images=__A						)

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

									# test if it raises when no input is passed
									with pytest.raises(__A						):
												processor()




						def  _snake_case					(	self						:   Tuple						)      ->str:






									"""simple docstring"""






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

									a__ :Tuple						  =    BlipProcessor(tokenizer=__A   ,     image_processor=__A						)

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

									a__ :Dict						  =    processor.batch_decode(__A						)
									a__ :Dict						  =    tokenizer.batch_decode(__A						)

									self.assertListEqual(__A   ,     __A						)






						def  _snake_case					(	self						:   List[str]						)      ->Tuple:






									"""simple docstring"""






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

									a__ :int						  =    BlipProcessor(tokenizer=__A   ,     image_processor=__A						)

									a__ :Any						  =    "lower newer"
									a__ :Tuple						  =    self.prepare_image_inputs()

									a__ :Tuple						  =    processor(text=__A   ,     images=__A						)

									# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
									self.assertListEqual(list(inputs.keys()						)   ,     ["pixel_values", "input_ids", "attention_mask"]						)






def 				lowerCamelCase__	(      a	:   int = 100				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    n * (n + 1) * (2 * n + 1) / 6
			a__ :Tuple						  =    (n * (n + 1) / 2) ** 2
			return int(square_of_sum - sum_of_squares				)


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


