





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 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				tempfile.mkdtemp()

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

										_snake_case									=				InstructBlipProcessor(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )

										processor.save_pretrained(self.tmpdirname  )




					def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							Optional[Any]:
										return AutoProcessor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_  ).tokenizer




					def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							List[Any]:
										return AutoProcessor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_  ).image_processor




					def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							Any:
										return AutoProcessor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_  ).qformer_tokenizer




					def 	lowerCAmelCase			(		self  )		->							str:
										shutil.rmtree(self.tmpdirname  )




					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				[np.random.randint(255				,						size=(3, 30, 400)				,						dtype=np.uinta  )]

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

										return image_inputs




					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				InstructBlipProcessor(
										    tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()				,						qformer_tokenizer=self.get_qformer_tokenizer()				,						)
										processor.save_pretrained(self.tmpdirname  )

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

										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				self.get_image_processor()
										_snake_case									=				self.get_tokenizer()
										_snake_case									=				self.get_qformer_tokenizer()

										_snake_case									=				InstructBlipProcessor(
										    tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_				,						qformer_tokenizer=lowerCAmelCase_  )

										_snake_case									=				self.prepare_image_inputs()

										_snake_case									=				image_processor(lowerCAmelCase_				,						return_tensors='np'  )
										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.get_image_processor()
										_snake_case									=				self.get_tokenizer()
										_snake_case									=				self.get_qformer_tokenizer()

										_snake_case									=				InstructBlipProcessor(
										    tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_				,						qformer_tokenizer=lowerCAmelCase_  )

										_snake_case									=				'lower newer'

										_snake_case									=				processor(text=lowerCAmelCase_  )

										_snake_case									=				tokenizer(lowerCAmelCase_				,						return_token_type_ids=lowerCAmelCase_  )
										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				self.get_image_processor()
										_snake_case									=				self.get_tokenizer()
										_snake_case									=				self.get_qformer_tokenizer()

										_snake_case									=				InstructBlipProcessor(
										    tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_				,						qformer_tokenizer=lowerCAmelCase_  )

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

										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				self.get_image_processor()
										_snake_case									=				self.get_tokenizer()
										_snake_case									=				self.get_qformer_tokenizer()

										_snake_case									=				InstructBlipProcessor(
										    tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_				,						qformer_tokenizer=lowerCAmelCase_  )

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

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

										self.assertListEqual(lowerCAmelCase_				,						lowerCAmelCase_  )






					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.get_image_processor()
										_snake_case									=				self.get_tokenizer()
										_snake_case									=				self.get_qformer_tokenizer()

										_snake_case									=				InstructBlipProcessor(
										    tokenizer=lowerCAmelCase_				,						image_processor=lowerCAmelCase_				,						qformer_tokenizer=lowerCAmelCase_  )

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

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

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










import random





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       List[Any]						)			-> tuple:



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				[], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase__						)
										elif element > pivot:
															greater.append(UpperCamelCase__						)
										else:
															equal.append(UpperCamelCase__						)
					return less, equal, greater





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       int						)			-> Dict:



					'''simple docstring'''




					if index >= len(UpperCamelCase__						) or index < 0:
										return None

					_snake_case									=				items[random.randint(0       ,    len(UpperCamelCase__						) - 1						)]
					_snake_case									=				0
					_snake_case							,     _snake_case							,     _snake_case									=				_partition(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase__       ,    UpperCamelCase__						)
					# must be in larger
					else:
										return quick_select(UpperCamelCase__       ,    index - (m + count)						)



