






import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
							import onnxruntime as ort




class 				__A     (			__lowerCAmelCase      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Tuple			 ='''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


				def       __lowercase							( self							,  lowerCamelCase__=0    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=np.random.RandomState(lowerCAmelCase_    )
									__UpperCamelCase      :					List[str]       		={
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Optional[int]       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Optional[int]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					Optional[Any]       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					Union[str, Any]       		=np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									__UpperCamelCase      :					Union[str, Any]       		=PNDMScheduler.from_config(pipe.scheduler.config							,  skip_prk_steps=lowerCAmelCase_    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Tuple       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Union[str, Any]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					List[Any]       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					Any       		=np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									__UpperCamelCase      :					Union[str, Any]       		=LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					List[Any]       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Optional[int]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					int       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					Tuple       		=np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									__UpperCamelCase      :					Union[str, Any]       		=EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					List[str]       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Union[str, Any]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					int       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					str       		=np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[int]       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									__UpperCamelCase      :					int       		=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					str       		=self.get_dummy_inputs()
									__UpperCamelCase      :					List[str]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					List[Any]       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					List[str]       		=np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									__UpperCamelCase      :					Union[str, Any]       		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Dict       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Optional[Any]       		=pipe(**lowerCAmelCase_    ).images
									__UpperCamelCase      :					Tuple       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__UpperCamelCase      :					Dict       		=np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Any       		=self.get_dummy_inputs()
									__UpperCamelCase      :					List[str]       		=3 * [inputs['prompt']]

									# forward
									__UpperCamelCase      :					Optional[Any]       		=pipe(**lowerCAmelCase_    )
									__UpperCamelCase      :					Optional[int]       		=output.images[0, -3:, -3:, -1]

									__UpperCamelCase      :					List[str]       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Tuple       		=3 * [inputs.pop('prompt'    )]

									__UpperCamelCase      :					Optional[Any]       		=pipe.tokenizer(
									    lowerCAmelCase_							,  padding='max_length'							,  max_length=pipe.tokenizer.model_max_length							,  truncation=lowerCAmelCase_							,  return_tensors='np'							,  )
									__UpperCamelCase      :					Tuple       		=text_inputs['input_ids']

									__UpperCamelCase      :					int       		=pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

									__UpperCamelCase      :					Dict       		=prompt_embeds

									# forward
									__UpperCamelCase      :					Tuple       		=pipe(**lowerCAmelCase_    )
									__UpperCamelCase      :					Union[str, Any]       		=output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='CPUExecutionProvider'    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					List[str]       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Tuple       		=3 * ['this is a negative prompt']
									__UpperCamelCase      :					str       		=negative_prompt
									__UpperCamelCase      :					Any       		=3 * [inputs['prompt']]

									# forward
									__UpperCamelCase      :					List[Any]       		=pipe(**lowerCAmelCase_    )
									__UpperCamelCase      :					List[Any]       		=output.images[0, -3:, -3:, -1]

									__UpperCamelCase      :					str       		=self.get_dummy_inputs()
									__UpperCamelCase      :					Optional[int]       		=3 * [inputs.pop('prompt'    )]

									__UpperCamelCase      :					Optional[Any]       		=[]
									for p in [prompt, negative_prompt]:
														__UpperCamelCase      :					Any       		=pipe.tokenizer(
														    lowerCAmelCase_							,  padding='max_length'							,  max_length=pipe.tokenizer.model_max_length							,  truncation=lowerCAmelCase_							,  return_tensors='np'							,  )
														__UpperCamelCase      :					Optional[int]       		=text_inputs['input_ids']

														embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

									__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=embeds

									# forward
									__UpperCamelCase      :					Tuple       		=pipe(**lowerCAmelCase_    )
									__UpperCamelCase      :					Optional[Any]       		=output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4




@nightly
@require_onnxruntime
@require_torch_gpu
class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=ort.SessionOptions()
									__UpperCamelCase      :					Optional[int]       		=False
									return options


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=OnnxStableDiffusionPipeline.from_pretrained(
									    'CompVis/stable-diffusion-v1-4'							,  revision='onnx'							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					int       		='A painting of a squirrel eating a burger'
									np.random.seed(0    )
									__UpperCamelCase      :					List[Any]       		=sd_pipe([prompt]							,  guidance_scale=6.0							,  num_inference_steps=10							,  output_type='np'    )
									__UpperCamelCase      :					Any       		=output.images

									__UpperCamelCase      :					List[str]       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									__UpperCamelCase      :					Dict       		=np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=DDIMScheduler.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  subfolder='scheduler'							,  revision='onnx'    )
									__UpperCamelCase      :					str       		=OnnxStableDiffusionPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  revision='onnx'							,  scheduler=lowerCAmelCase_							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Tuple       		='open neural network exchange'
									__UpperCamelCase      :					Union[str, Any]       		=np.random.RandomState(0    )
									__UpperCamelCase      :					Optional[int]       		=sd_pipe([prompt]							,  guidance_scale=7.5							,  num_inference_steps=10							,  generator=lowerCAmelCase_							,  output_type='np'    )
									__UpperCamelCase      :					str       		=output.images
									__UpperCamelCase      :					List[Any]       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									__UpperCamelCase      :					Optional[int]       		=np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=LMSDiscreteScheduler.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  subfolder='scheduler'							,  revision='onnx'    )
									__UpperCamelCase      :					List[str]       		=OnnxStableDiffusionPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  revision='onnx'							,  scheduler=lowerCAmelCase_							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					int       		='open neural network exchange'
									__UpperCamelCase      :					Union[str, Any]       		=np.random.RandomState(0    )
									__UpperCamelCase      :					Union[str, Any]       		=sd_pipe([prompt]							,  guidance_scale=7.5							,  num_inference_steps=10							,  generator=lowerCAmelCase_							,  output_type='np'    )
									__UpperCamelCase      :					Union[str, Any]       		=output.images
									__UpperCamelCase      :					int       		=image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									__UpperCamelCase      :					Tuple       		=np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=0

									def test_callback_fn(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ) -> None:
														__UpperCamelCase      :					str       		=True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 0:
																			assert latents.shape == (1, 4, 64, 64)
																			__UpperCamelCase      :					Any       		=latents[0, -3:, -3:, -1]
																			__UpperCamelCase      :					str       		=np.array(
																			    [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]    )

																			assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
														elif step == 5:
																			assert latents.shape == (1, 4, 64, 64)
																			__UpperCamelCase      :					int       		=latents[0, -3:, -3:, -1]
																			__UpperCamelCase      :					Dict       		=np.array(
																			    [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]    )

																			assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3

									__UpperCamelCase      :					str       		=False

									__UpperCamelCase      :					int       		=OnnxStableDiffusionPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  revision='onnx'							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									__UpperCamelCase      :					Union[str, Any]       		='Andromeda galaxy in a bottle'

									__UpperCamelCase      :					str       		=np.random.RandomState(0    )
									pipe(
									    prompt=lowerCAmelCase_							,  num_inference_steps=5							,  guidance_scale=7.5							,  generator=lowerCAmelCase_							,  callback=lowerCAmelCase_							,  callback_steps=1							,  )
									assert test_callback_fn.has_been_called
									assert number_of_steps == 6







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=OnnxStableDiffusionPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'							,  revision='onnx'							,  safety_checker=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									assert isinstance(lowerCAmelCase_							,  lowerCAmelCase_    )
									assert pipe.safety_checker is None

									__UpperCamelCase      :					Dict       		=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(lowerCAmelCase_    )
														__UpperCamelCase      :					List[str]       		=OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_    )

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


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"""]		,				)




