

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






@require_vision
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				tempfile.mkdtemp()

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

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(_lowercase,     _lowercase,     _lowercase       )

											processor.save_pretrained(self.tmpdirname       )



				def   a__					(    self,     **_lowercase       )     ->							str:
											return AutoProcessor.from_pretrained(self.tmpdirname,     **_lowercase       ).tokenizer



				def   a__					(    self,     **_lowercase       )     ->							Optional[Any]:
											return AutoProcessor.from_pretrained(self.tmpdirname,     **_lowercase       ).image_processor



				def   a__					(    self,     **_lowercase       )     ->							int:
											return AutoProcessor.from_pretrained(self.tmpdirname,     **_lowercase       ).qformer_tokenizer



				def   a__					(    self       )     ->							Optional[Any]:
											shutil.rmtree(self.tmpdirname       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				[np.random.randint(255,     size=(3, 30, 400),     dtype=np.uinta       )]

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

											return image_inputs



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor(),     qformer_tokenizer=self.get_qformer_tokenizer(),     )
											processor.save_pretrained(self.tmpdirname       )

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

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor.from_pretrained(
											    self.tmpdirname,     bos_token='(BOS)',     eos_token='(EOS)',     do_normalize=_lowercase,     padding_value=1.0       )

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

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



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.get_image_processor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=_lowercase,     image_processor=_lowercase,     qformer_tokenizer=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_				=				image_processor(_lowercase,     return_tensors='np'       )
											SCREAMING_SNAKE_CASE_				=				processor(images=_lowercase,     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       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				self.get_image_processor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=_lowercase,     image_processor=_lowercase,     qformer_tokenizer=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'lower newer'

											SCREAMING_SNAKE_CASE_				=				processor(text=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				tokenizer(_lowercase,     return_token_type_ids=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				qformer_tokenizer(_lowercase,     return_token_type_ids=_lowercase       )

											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   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				self.get_image_processor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=_lowercase,     image_processor=_lowercase,     qformer_tokenizer=_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				processor(text=_lowercase,     images=_lowercase       )

											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(_lowercase       ):
																		processor()



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				self.get_image_processor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=_lowercase,     image_processor=_lowercase,     qformer_tokenizer=_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				processor.batch_decode(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				tokenizer.batch_decode(_lowercase       )

											self.assertListEqual(_lowercase,     _lowercase       )





				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.get_image_processor()
											SCREAMING_SNAKE_CASE_				=				self.get_tokenizer()
											SCREAMING_SNAKE_CASE_				=				self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE_				=				InstructBlipProcessor(
											    tokenizer=_lowercase,     image_processor=_lowercase,     qformer_tokenizer=_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				processor(text=_lowercase,     images=_lowercase       )

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








'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> str:
							if a < 0 or b < 0:
														raise ValueError('the value of both inputs must be positive'   )
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]  # remove the leading "0b"
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]
							SCREAMING_SNAKE_CASE_				=				max(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
							return "0b" + "".join(
							    str(int('1' in (char_a, char_b)   )   )
							    for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase__   )		,b_binary.zfill(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





