"""simple docstring"""





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class      UpperCAmelCase_	(    unittest.TestCase):


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      @property
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Optional[int]       =	1
             lowercase__			:		int       =	3
             lowercase__			:		Optional[Any]       =	(3_2, 3_2)

             lowercase__			:		int       =	floats_tensor((batch_size, num_channels) + sizes		,			rng=random.Random(0							)							).to(a							)
             return image


      @property
      def 		_UpperCAmelCase       (   self							)       ->					int:
             torch.manual_seed(0							)
             lowercase__			:		str       =	UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4)		,			layers_per_block=2		,			sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,			cross_attention_dim=3_2		,			)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             torch.manual_seed(0							)
             lowercase__			:		int       =	AutoencoderKL(
                 block_out_channels=[3_2, 6_4]		,			in_channels=3		,			out_channels=3		,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,			latent_channels=4		,			)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             torch.manual_seed(0							)
             lowercase__			:		Optional[Any]       =	CLIPTextConfig(
                 bos_token_id=0		,			eos_token_id=2		,			hidden_size=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)
             return CLIPTextModel(a							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             def extract(*a		,			**a							):


                    class      UpperCAmelCase_	:


                          def __init__(   self							)       ->					List[str]:
                                 lowercase__			:		List[Any]       =	torch.ones([0]							)


                          def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict:
                                 self.pixel_values.to(a							)
                                 return self





                    return Out()

             return extract


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Optional[Any]       =	'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__			:		Optional[int]       =	self.dummy_cond_unet
             lowercase__			:		int       =	DDIMScheduler(
                 beta_start=0.00_085		,			beta_end=0.012		,			beta_schedule='scaled_linear'		,			clip_sample=a		,			set_alpha_to_one=a		,			)

             lowercase__			:		Any       =	self.dummy_vae
             lowercase__			:		Optional[Any]       =	self.dummy_text_encoder
             lowercase__			:		Union[str, Any]       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

             # make sure here that pndm scheduler skips prk
             lowercase__			:		List[Any]       =	StableDiffusionPipeline(
                 unet=a		,			scheduler=a		,			vae=a		,			text_encoder=a		,			tokenizer=a		,			safety_checker=a		,			feature_extractor=self.dummy_extractor		,			)
             lowercase__			:		str       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		int       =	'A painting of a squirrel eating a burger'

             lowercase__			:		str       =	torch.Generator(device=a							).manual_seed(0							)
             lowercase__			:		List[Any]       =	sd_pipe([prompt]		,			generator=a		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='np'							)
             lowercase__			:		List[Any]       =	output.images

             lowercase__			:		Dict       =	torch.Generator(device=a							).manual_seed(0							)
             lowercase__			:		str       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='np'		,			return_dict=a		,			)[0]

             lowercase__			:		List[str]       =	image[0, -3:, -3:, -1]
             lowercase__			:		Any       =	image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             lowercase__			:		Dict       =	np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		List[Any]       =	'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__			:		Dict       =	self.dummy_cond_unet
             lowercase__			:		Union[str, Any]       =	PNDMScheduler(skip_prk_steps=a							)
             lowercase__			:		int       =	self.dummy_vae
             lowercase__			:		List[Any]       =	self.dummy_text_encoder
             lowercase__			:		List[Any]       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

             # make sure here that pndm scheduler skips prk
             lowercase__			:		Tuple       =	StableDiffusionPipeline(
                 unet=a		,			scheduler=a		,			vae=a		,			text_encoder=a		,			tokenizer=a		,			safety_checker=a		,			feature_extractor=self.dummy_extractor		,			)
             lowercase__			:		Optional[int]       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		Dict       =	'A painting of a squirrel eating a burger'
             lowercase__			:		Optional[int]       =	torch.Generator(device=a							).manual_seed(0							)
             lowercase__			:		List[Any]       =	sd_pipe([prompt]		,			generator=a		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='np'							)

             lowercase__			:		str       =	output.images

             lowercase__			:		Optional[Any]       =	torch.Generator(device=a							).manual_seed(0							)
             lowercase__			:		Union[str, Any]       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=6.0		,			num_inference_steps=2		,			output_type='np'		,			return_dict=a		,			)[0]

             lowercase__			:		str       =	image[0, -3:, -3:, -1]
             lowercase__			:		Any       =	image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             lowercase__			:		str       =	np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		int       =	StableDiffusionPipeline.from_pretrained(
                 'hf-internal-testing/tiny-stable-diffusion-lms-pipe'		,			safety_checker=a							)
             assert isinstance(a		,			a							)
             assert isinstance(pipe.scheduler		,			a							)
             assert pipe.safety_checker is None

             lowercase__			:		Optional[int]       =	pipe('example prompt'		,			num_inference_steps=2							).images[0]
             assert image is not None

             # check that there's no error when saving a pipeline with one of the models being None
             with tempfile.TemporaryDirectory() as tmpdirname:
                    pipe.save_pretrained(a							)
                    lowercase__			:		Union[str, Any]       =	StableDiffusionPipeline.from_pretrained(a							)

             # sanity check that the pipeline still works
             assert pipe.safety_checker is None
             lowercase__			:		Union[str, Any]       =	pipe('example prompt'		,			num_inference_steps=2							).images[0]
             assert image is not None


      @unittest.skipIf(torch_device != 'cuda'		,			'This test requires a GPU'							)
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		Union[str, Any]       =	self.dummy_cond_unet
             lowercase__			:		Any       =	PNDMScheduler(skip_prk_steps=a							)
             lowercase__			:		Optional[Any]       =	self.dummy_vae
             lowercase__			:		int       =	self.dummy_text_encoder
             lowercase__			:		List[str]       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

             # put models in fp16
             lowercase__			:		Any       =	unet.half()
             lowercase__			:		List[Any]       =	vae.half()
             lowercase__			:		Optional[Any]       =	bert.half()

             # make sure here that pndm scheduler skips prk
             lowercase__			:		Any       =	StableDiffusionPipeline(
                 unet=a		,			scheduler=a		,			vae=a		,			text_encoder=a		,			tokenizer=a		,			safety_checker=a		,			feature_extractor=self.dummy_extractor		,			)
             lowercase__			:		Union[str, Any]       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		int       =	'A painting of a squirrel eating a burger'
             lowercase__			:		List[Any]       =	sd_pipe([prompt]		,			num_inference_steps=2		,			output_type='np'							).images

             assert image.shape == (1, 6_4, 6_4, 3)





@nightly
@require_torch_gpu
class      UpperCAmelCase_	(    unittest.TestCase):


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Union[str, Any]       =	StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		,			safety_checker=a							)
             lowercase__			:		List[str]       =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
             lowercase__			:		Any       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		str       =	(
                 'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
                 ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
                 ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
                 ' children from bahnhof zoo, detailed '
             )
             lowercase__			:		Any       =	4_0_0_3_6_6_0_3_4_6
             lowercase__			:		Union[str, Any]       =	7

             # without safety guidance (sld_guidance_scale = 0)
             lowercase__			:		Union[str, Any]       =	torch.manual_seed(a							)
             lowercase__			:		str       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=0		,			)

             lowercase__			:		Dict       =	output.images
             lowercase__			:		Tuple       =	image[0, -3:, -3:, -1]
             lowercase__			:		List[Any]       =	[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

             # without safety guidance (strong configuration)
             lowercase__			:		Any       =	torch.manual_seed(a							)
             lowercase__			:		Any       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=2_0_0_0		,			sld_warmup_steps=7		,			sld_threshold=0.025		,			sld_momentum_scale=0.5		,			sld_mom_beta=0.7		,			)

             lowercase__			:		Dict       =	output.images
             lowercase__			:		Dict       =	image[0, -3:, -3:, -1]
             lowercase__			:		Any       =	[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		,			safety_checker=a							)
             lowercase__			:		List[str]       =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
             lowercase__			:		int       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		int       =	'padme amidala taking a bath artwork, safe for work, no nudity'
             lowercase__			:		Union[str, Any]       =	2_7_3_4_9_7_1_7_5_5
             lowercase__			:		Any       =	7

             lowercase__			:		Union[str, Any]       =	torch.manual_seed(a							)
             lowercase__			:		Dict       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=0		,			)

             lowercase__			:		Tuple       =	output.images
             lowercase__			:		List[Any]       =	image[0, -3:, -3:, -1]
             lowercase__			:		Optional[int]       =	[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

             lowercase__			:		Tuple       =	torch.manual_seed(a							)
             lowercase__			:		Union[str, Any]       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=2_0_0_0		,			sld_warmup_steps=7		,			sld_threshold=0.025		,			sld_momentum_scale=0.5		,			sld_mom_beta=0.7		,			)

             lowercase__			:		str       =	output.images
             lowercase__			:		Tuple       =	image[0, -3:, -3:, -1]
             lowercase__			:		Tuple       =	[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		int       =	StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'							)
             lowercase__			:		Union[str, Any]       =	sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		int       =	(
                 'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
                 ' leyendecker'
             )
             lowercase__			:		Optional[int]       =	1_0_4_4_3_5_5_2_3_4
             lowercase__			:		List[Any]       =	1_2

             lowercase__			:		List[str]       =	torch.manual_seed(a							)
             lowercase__			:		int       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=0		,			)

             lowercase__			:		Tuple       =	output.images
             lowercase__			:		Any       =	image[0, -3:, -3:, -1]
             lowercase__			:		Tuple       =	np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]							)

             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-7

             lowercase__			:		List[Any]       =	torch.manual_seed(a							)
             lowercase__			:		str       =	sd_pipe(
                 [prompt]		,			generator=a		,			guidance_scale=a		,			num_inference_steps=5_0		,			output_type='np'		,			width=5_1_2		,			height=5_1_2		,			sld_guidance_scale=2_0_0_0		,			sld_warmup_steps=7		,			sld_threshold=0.025		,			sld_momentum_scale=0.5		,			sld_mom_beta=0.7		,			)

             lowercase__			:		Dict       =	output.images
             lowercase__			:		Dict       =	image[0, -3:, -3:, -1]
             lowercase__			:		Optional[Any]       =	np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]							)
             assert image.shape == (1, 5_1_2, 5_1_2, 3)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2


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 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

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

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



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



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

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

        return image_inputs



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

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

        _A        =  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      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

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

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  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							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

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

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  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      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

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

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  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      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

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

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

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

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

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

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

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

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




