
'''simple docstring'''







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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast


@require_vision
class 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	tempfile.mkdtemp()

								_snake_case     		=	BlipImageProcessor()
								_snake_case     		=	GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'						)

								_snake_case     		=	BlipaProcessor(UpperCamelCase__					,     UpperCamelCase__						)

								processor.save_pretrained(self.tmpdirname						)


							def 				lowerCamelCase       ( self					,     **lowerCAmelCase_						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self					,     **lowerCAmelCase_						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								shutil.rmtree(self.tmpdirname						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	[np.random.randint(2_55					,     size=(3, 30, 4_00)					,     dtype=np.uinta						)]

								_snake_case     		=	[Image.fromarray(np.moveaxis(UpperCamelCase__					,     0					,     -1						)						) for x in image_inputs]

								return image_inputs


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BlipaProcessor(tokenizer=self.get_tokenizer()					,     image_processor=self.get_image_processor()						)
								processor.save_pretrained(self.tmpdirname						)

								_snake_case     		=	self.get_tokenizer(bos_token='(BOS)'					,     eos_token='(EOS)'						)
								_snake_case     		=	self.get_image_processor(do_normalize=UpperCamelCase__					,     padding_value=1.0						)

								_snake_case     		=	BlipaProcessor.from_pretrained(
								    self.tmpdirname					,     bos_token='(BOS)'					,     eos_token='(EOS)'					,     do_normalize=UpperCamelCase__					,     padding_value=1.0						)

								self.assertEqual(processor.tokenizer.get_vocab()					,     tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.tokenizer					,     UpperCamelCase__						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_image_processor()
								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	BlipaProcessor(tokenizer=UpperCamelCase__					,     image_processor=UpperCamelCase__						)

								_snake_case     		=	self.prepare_image_inputs()

								_snake_case     		=	image_processor(UpperCamelCase__					,     return_tensors='np'						)
								_snake_case     		=	processor(images=UpperCamelCase__					,     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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_image_processor()
								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	BlipaProcessor(tokenizer=UpperCamelCase__					,     image_processor=UpperCamelCase__						)

								_snake_case     		=	'''lower newer'''

								_snake_case     		=	processor(text=UpperCamelCase__						)

								_snake_case     		=	tokenizer(UpperCamelCase__					,     return_token_type_ids=UpperCamelCase__						)

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_image_processor()
								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	BlipaProcessor(tokenizer=UpperCamelCase__					,     image_processor=UpperCamelCase__						)

								_snake_case     		=	'''lower newer'''
								_snake_case     		=	self.prepare_image_inputs()

								_snake_case     		=	processor(text=UpperCamelCase__					,     images=UpperCamelCase__						)

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

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_image_processor()
								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	BlipaProcessor(tokenizer=UpperCamelCase__					,     image_processor=UpperCamelCase__						)

								_snake_case     		=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								_snake_case     		=	processor.batch_decode(UpperCamelCase__						)
								_snake_case     		=	tokenizer.batch_decode(UpperCamelCase__						)

								self.assertListEqual(UpperCamelCase__					,     UpperCamelCase__						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_image_processor()
								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	BlipaProcessor(tokenizer=UpperCamelCase__					,     image_processor=UpperCamelCase__						)

								_snake_case     		=	'''lower newer'''
								_snake_case     		=	self.prepare_image_inputs()

								_snake_case     		=	processor(text=UpperCamelCase__					,     images=UpperCamelCase__						)

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



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

