



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,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)

@require_vision
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[Any]:
									A							:					Tuple              =						tempfile.mkdtemp()

									A							:					Tuple              =						BlipImageProcessor()
									A							:					Optional[Any]              =						GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
									A							:					Tuple              =						BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert" )

									A							:					Dict              =						InstructBlipProcessor(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

									processor.save_pretrained(self.tmpdirname )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	**__lowerCamelCase      :			str )      ->				int:
									return AutoProcessor.from_pretrained(self.tmpdirname	,	**__lowerCamelCase ).tokenizer







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	**__lowerCamelCase      :			List[Any] )      ->				Optional[int]:
									return AutoProcessor.from_pretrained(self.tmpdirname	,	**__lowerCamelCase ).image_processor







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	**__lowerCamelCase      :			Union[str, Any] )      ->				Dict:
									return AutoProcessor.from_pretrained(self.tmpdirname	,	**__lowerCamelCase ).qformer_tokenizer







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									shutil.rmtree(self.tmpdirname )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				str:
									A							:					List[Any]              =						[np.random.randint(2_55	,	size=(3, 30, 4_00)	,	dtype=np.uinta )]

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

									return image_inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									A							:					List[str]              =						InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()	,	image_processor=self.get_image_processor()	,	qformer_tokenizer=self.get_qformer_tokenizer()	,	)
									processor.save_pretrained(self.tmpdirname )

									A							:					Dict              =						self.get_tokenizer(bos_token="(BOS)"	,	eos_token="(EOS)" )
									A							:					List[Any]              =						self.get_image_processor(do_normalize=__lowerCamelCase	,	padding_value=1.0 )

									A							:					Optional[int]              =						InstructBlipProcessor.from_pretrained(
									    self.tmpdirname	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	do_normalize=__lowerCamelCase	,	padding_value=1.0 )

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

									self.assertEqual(processor.image_processor.to_json_string()	,	image_processor_add_kwargs.to_json_string() )
									self.assertIsInstance(processor.image_processor	,	__lowerCamelCase )
									self.assertIsInstance(processor.qformer_tokenizer	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				str:
									A							:					Any              =						self.get_image_processor()
									A							:					str              =						self.get_tokenizer()
									A							:					List[str]              =						self.get_qformer_tokenizer()

									A							:					List[str]              =						InstructBlipProcessor(
									    tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase	,	qformer_tokenizer=__lowerCamelCase )

									A							:					str              =						self.prepare_image_inputs()

									A							:					Optional[int]              =						image_processor(__lowerCamelCase	,	return_tensors="np" )
									A							:					Union[str, Any]              =						processor(images=__lowerCamelCase	,	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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Tuple:
									A							:					List[str]              =						self.get_image_processor()
									A							:					Tuple              =						self.get_tokenizer()
									A							:					str              =						self.get_qformer_tokenizer()

									A							:					List[str]              =						InstructBlipProcessor(
									    tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase	,	qformer_tokenizer=__lowerCamelCase )

									A							:					Union[str, Any]              =						"lower newer"

									A							:					List[str]              =						processor(text=__lowerCamelCase )

									A							:					Union[str, Any]              =						tokenizer(__lowerCamelCase	,	return_token_type_ids=__lowerCamelCase )
									A							:					List[str]              =						qformer_tokenizer(__lowerCamelCase	,	return_token_type_ids=__lowerCamelCase )

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

									for key in encoded_tokens_qformer.keys():
												self.assertListEqual(encoded_tokens_qformer[key]	,	encoded_processor["qformer_" + key] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Tuple:
									A							:					List[str]              =						self.get_image_processor()
									A							:					List[str]              =						self.get_tokenizer()
									A							:					Optional[Any]              =						self.get_qformer_tokenizer()

									A							:					Optional[int]              =						InstructBlipProcessor(
									    tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase	,	qformer_tokenizer=__lowerCamelCase )

									A							:					Optional[Any]              =						"lower newer"
									A							:					Dict              =						self.prepare_image_inputs()

									A							:					Dict              =						processor(text=__lowerCamelCase	,	images=__lowerCamelCase )

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

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									A							:					Any              =						self.get_image_processor()
									A							:					int              =						self.get_tokenizer()
									A							:					Any              =						self.get_qformer_tokenizer()

									A							:					Optional[int]              =						InstructBlipProcessor(
									    tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase	,	qformer_tokenizer=__lowerCamelCase )

									A							:					str              =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A							:					str              =						processor.batch_decode(__lowerCamelCase )
									A							:					Tuple              =						tokenizer.batch_decode(__lowerCamelCase )

									self.assertListEqual(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Tuple:
									A							:					Union[str, Any]              =						self.get_image_processor()
									A							:					int              =						self.get_tokenizer()
									A							:					Any              =						self.get_qformer_tokenizer()

									A							:					str              =						InstructBlipProcessor(
									    tokenizer=__lowerCamelCase	,	image_processor=__lowerCamelCase	,	qformer_tokenizer=__lowerCamelCase )

									A							:					Optional[int]              =						"lower newer"
									A							:					int              =						self.prepare_image_inputs()

									A							:					Tuple              =						processor(text=__lowerCamelCase	,	images=__lowerCamelCase )

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




from math import sqrt




def 	UpperCAmelCase   (						_lowerCamelCase = 100_0000    ):
			A							:					int              =						0
			A							:					int              =						0
			A							:					int

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2 ,    2 * max_cuboid_size + 1    ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
												num_cuboids += (
												    min(_lowerCamelCase ,    sum_shortest_sides // 2    )
												    - max(1 ,    sum_shortest_sides - max_cuboid_size    )
												    + 1
												)

			return max_cuboid_size


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