


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 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					tempfile.mkdtemp()

										# fmt: off
										SCREAMING_SNAKE_CASE_		:     Dict					=					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
										# fmt: on
										SCREAMING_SNAKE_CASE_		:     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]       )       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
										    'do_resize': True,
										    'size': {'height': 1_8, 'width': 1_8},
										    'do_normalize': True,
										    'image_mean': [0.5, 0.5, 0.5],
										    'image_std': [0.5, 0.5, 0.5],
										}
										SCREAMING_SNAKE_CASE_		:     str					=					os.path.join(self.tmpdirname			,						lowerCAmelCase__       )
										with open(self.image_processor_file			,						'w'			,						encoding='utf-8'       ) as fp:
													json.dump(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										shutil.rmtree(self.tmpdirname       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[np.random.randint(2_5_5			,						size=(3, 3_0, 4_0_0)			,						dtype=np.uinta       )]

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

										return image_inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.get_tokenizer()
										SCREAMING_SNAKE_CASE_		:     str					=					self.get_image_processor()

										SCREAMING_SNAKE_CASE_		:     str					=					VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										processor.save_pretrained(self.tmpdirname       )
										SCREAMING_SNAKE_CASE_		:     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			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()       )
										processor.save_pretrained(self.tmpdirname       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_image_processor(do_normalize=lowerCAmelCase__			,						padding_value=1.0       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					VisionTextDualEncoderProcessor.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			,						(BertTokenizer, BertTokenizerFast)       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

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

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Any					=					image_processor(lowerCAmelCase__			,						return_tensors='np'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

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

										SCREAMING_SNAKE_CASE_		:     str					=					'lower newer'

										SCREAMING_SNAKE_CASE_		:     Tuple					=					processor(text=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer(lowerCAmelCase__       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     str					=					VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     Any					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     List[str]					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

										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(lowerCAmelCase__       ):
													processor()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     str					=					VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					processor.batch_decode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer.batch_decode(lowerCAmelCase__       )

										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     str					=					VisionTextDualEncoderProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Tuple					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

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









def 						a__			(				A__ = 5_0_0_0_0_0_0_0				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					set()
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					int((limit - 2_4) ** (1 / 2)				)

			SCREAMING_SNAKE_CASE_		:     Dict					=					set(range(3,      prime_square_limit + 1,      2				)				)
			primes.add(2				)
			for p in range(3,      prime_square_limit + 1,      2				):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p,      prime_square_limit + 1,      A__				)				)				)

			for primea in primes:
						SCREAMING_SNAKE_CASE_		:     int					=					primea * primea
						for primea in primes:
									SCREAMING_SNAKE_CASE_		:     Dict					=					primea * primea * primea
									if square + cube >= limit - 1_6:
												break
									for primea in primes:
												SCREAMING_SNAKE_CASE_		:     Optional[int]					=					primea * primea * primea * primea
												SCREAMING_SNAKE_CASE_		:     str					=					square + cube + tetr
												if total >= limit:
															break
												ret.add(A__				)

			return len(A__				)


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





