
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(    snake_case__    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=0						):
       UpperCamelCase__				=    np.random.RandomState(UpperCAmelCase_						)
       UpperCamelCase__				=    {
           "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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       UpperCamelCase__				=    PNDMScheduler.from_config(pipe.scheduler.config	,      skip_prk_steps=UpperCAmelCase_						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       UpperCamelCase__				=    LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       UpperCamelCase__				=    EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       UpperCamelCase__				=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       UpperCamelCase__				=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						).images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 1_28, 1_28, 3)
       UpperCamelCase__				=    np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    3 * [inputs["prompt"]]

       # forward
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						)
       UpperCamelCase__				=    output.images[0, -3:, -3:, -1]

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    3 * [inputs.pop("""prompt"""						)]

       UpperCamelCase__				=    pipe.tokenizer(
           UpperCAmelCase_	,      padding="""max_length"""	,      max_length=pipe.tokenizer.model_max_length	,      truncation=UpperCAmelCase_	,      return_tensors="""np"""	,      )
       UpperCamelCase__				=    text_inputs["input_ids"]

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

       UpperCamelCase__				=    prompt_embeds

       # forward
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						)
       UpperCamelCase__				=    output.images[0, -3:, -3:, -1]

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,      provider="""CPUExecutionProvider"""						)
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    3 * ["this is a negative prompt"]
       UpperCamelCase__				=    negative_prompt
       UpperCamelCase__				=    3 * [inputs["prompt"]]

       # forward
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						)
       UpperCamelCase__				=    output.images[0, -3:, -3:, -1]

       UpperCamelCase__				=    self.get_dummy_inputs()
       UpperCamelCase__				=    3 * [inputs.pop("""prompt"""						)]

       UpperCamelCase__				=    []
       for p in [prompt, negative_prompt]:
            UpperCamelCase__				=    pipe.tokenizer(
                UpperCAmelCase_	,      padding="""max_length"""	,      max_length=pipe.tokenizer.model_max_length	,      truncation=UpperCAmelCase_	,      return_tensors="""np"""	,      )
            UpperCamelCase__				=    text_inputs["input_ids"]

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

       UpperCamelCase__				=    embeds

       # forward
       UpperCamelCase__				=    pipe(**UpperCAmelCase_						)
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )







  @property
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ort.SessionOptions()
       UpperCamelCase__				=    False
       return options







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(
           """CompVis/stable-diffusion-v1-4"""	,      revision="""onnx"""	,      safety_checker=UpperCAmelCase_	,      feature_extractor=UpperCAmelCase_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    "A painting of a squirrel eating a burger"
       np.random.seed(0						)
       UpperCamelCase__				=    sd_pipe([prompt]	,      guidance_scale=6.0	,      num_inference_steps=10	,      output_type="""np"""						)
       UpperCamelCase__				=    output.images

       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase__				=    np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    DDIMScheduler.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      subfolder="""scheduler"""	,      revision="""onnx"""						)
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      revision="""onnx"""	,      scheduler=UpperCAmelCase_	,      safety_checker=UpperCAmelCase_	,      feature_extractor=UpperCAmelCase_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    "open neural network exchange"
       UpperCamelCase__				=    np.random.RandomState(0						)
       UpperCamelCase__				=    sd_pipe([prompt]	,      guidance_scale=7.5	,      num_inference_steps=10	,      generator=UpperCAmelCase_	,      output_type="""np"""						)
       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase__				=    np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    LMSDiscreteScheduler.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      subfolder="""scheduler"""	,      revision="""onnx"""						)
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      revision="""onnx"""	,      scheduler=UpperCAmelCase_	,      safety_checker=UpperCAmelCase_	,      feature_extractor=UpperCAmelCase_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    "open neural network exchange"
       UpperCamelCase__				=    np.random.RandomState(0						)
       UpperCamelCase__				=    sd_pipe([prompt]	,      guidance_scale=7.5	,      num_inference_steps=10	,      generator=UpperCAmelCase_	,      output_type="""np"""						)
       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase__				=    np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    0

       def test_callback_fn(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) -> None:
            UpperCamelCase__				=    True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                 assert latents.shape == (1, 4, 64, 64)
                 UpperCamelCase__				=    latents[0, -3:, -3:, -1]
                 UpperCamelCase__				=    np.array(
                     [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]						)

                 assert np.abs(latents_slice.flatten() - expected_slice						).max() < 1E-3
            elif step == 5:
                 assert latents.shape == (1, 4, 64, 64)
                 UpperCamelCase__				=    latents[0, -3:, -3:, -1]
                 UpperCamelCase__				=    np.array(
                     [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]						)

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

       UpperCamelCase__				=    False

       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      revision="""onnx"""	,      safety_checker=UpperCAmelCase_	,      feature_extractor=UpperCAmelCase_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
       pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

       UpperCamelCase__				=    "Andromeda galaxy in a bottle"

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      revision="""onnx"""	,      safety_checker=UpperCAmelCase_	,      feature_extractor=UpperCAmelCase_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
       assert isinstance(UpperCAmelCase_	,      UpperCAmelCase_						)
       assert pipe.safety_checker is None

       UpperCamelCase__				=    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(UpperCAmelCase_						)
            UpperCamelCase__				=    OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase_						)

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       UpperCamelCase__				=    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						):
       UpperCamelCase__				=    tempfile.mkdtemp()

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

       UpperCamelCase__				=    InstructBlipProcessor(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       processor.save_pretrained(self.tmpdirname						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).tokenizer







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).image_processor







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).qformer_tokenizer







  def 	UpperCAmelCase_ (self						):
       shutil.rmtree(self.tmpdirname						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [np.random.randint(2_55	,      size=(3, 30, 4_00)	,      dtype=np.uinta						)]

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

       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
       processor.save_pretrained(self.tmpdirname						)

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

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

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

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

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)
       UpperCamelCase__				=    processor(images=SCREAMING_SNAKE_CASE_	,      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						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

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

       UpperCamelCase__				=    """lower newer"""

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_						)

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

       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						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

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

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

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

       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(SCREAMING_SNAKE_CASE_						):
            processor()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

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

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

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

       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

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

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

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

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





