
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      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  @property
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    1
       UpperCamelCase__				=    3
       UpperCamelCase__				=    (32, 32)

       UpperCamelCase__				=    floats_tensor((batch_size, num_channels) + sizes	,      rng=random.Random(0						)						).to(_a						)
       return image







  @property
  def 	UpperCAmelCase_ (self						):
       torch.manual_seed(0						)
       UpperCamelCase__				=    UNetaDConditionModel(
           block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=4	,      out_channels=4	,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,      cross_attention_dim=32	,      )
       return model







  @property
  def 	UpperCAmelCase_ (self						):
       torch.manual_seed(0						)
       UpperCamelCase__				=    AutoencoderKL(
           block_out_channels=[32, 64]	,      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						):
       torch.manual_seed(0						)
       UpperCamelCase__				=    CLIPTextConfig(
           bos_token_id=0	,      eos_token_id=2	,      hidden_size=32	,      intermediate_size=37	,      layer_norm_eps=1E-05	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=10_00	,      )
       return CLIPTextModel(_a						)







  @property
  def 	UpperCAmelCase_ (self						):
       def extract(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):




            class      __A:

              """simple docstring"""







              def __init__(self						):
                   UpperCamelCase__				=    torch.ones([0]						)







              def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
                   self.pixel_values.to(_a						)
                   return self






            return Out()

       return extract







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__				=    self.dummy_cond_unet
       UpperCamelCase__				=    DDIMScheduler(
           beta_start=0.0_0085	,      beta_end=0.012	,      beta_schedule="""scaled_linear"""	,      clip_sample=_a	,      set_alpha_to_one=_a	,      )

       UpperCamelCase__				=    self.dummy_vae
       UpperCamelCase__				=    self.dummy_text_encoder
       UpperCamelCase__				=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__				=    StableDiffusionPipeline(
           unet=_a	,      scheduler=_a	,      vae=_a	,      text_encoder=_a	,      tokenizer=_a	,      safety_checker=_a	,      feature_extractor=self.dummy_extractor	,      )
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    '''A painting of a squirrel eating a burger'''

       UpperCamelCase__				=    torch.Generator(device=_a						).manual_seed(0						)
       UpperCamelCase__				=    sd_pipe([prompt]	,      generator=_a	,      guidance_scale=6.0	,      num_inference_steps=2	,      output_type="""np"""						)
       UpperCamelCase__				=    output.images

       UpperCamelCase__				=    torch.Generator(device=_a						).manual_seed(0						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=6.0	,      num_inference_steps=2	,      output_type="""np"""	,      return_dict=_a	,      )[0]

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

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__				=    np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]						)

       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						):
       UpperCamelCase__				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__				=    self.dummy_cond_unet
       UpperCamelCase__				=    PNDMScheduler(skip_prk_steps=_a						)
       UpperCamelCase__				=    self.dummy_vae
       UpperCamelCase__				=    self.dummy_text_encoder
       UpperCamelCase__				=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__				=    StableDiffusionPipeline(
           unet=_a	,      scheduler=_a	,      vae=_a	,      text_encoder=_a	,      tokenizer=_a	,      safety_checker=_a	,      feature_extractor=self.dummy_extractor	,      )
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    '''A painting of a squirrel eating a burger'''
       UpperCamelCase__				=    torch.Generator(device=_a						).manual_seed(0						)
       UpperCamelCase__				=    sd_pipe([prompt]	,      generator=_a	,      guidance_scale=6.0	,      num_inference_steps=2	,      output_type="""np"""						)

       UpperCamelCase__				=    output.images

       UpperCamelCase__				=    torch.Generator(device=_a						).manual_seed(0						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=6.0	,      num_inference_steps=2	,      output_type="""np"""	,      return_dict=_a	,      )[0]

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

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__				=    np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]						)

       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						):
       UpperCamelCase__				=    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

       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(_a						)
            UpperCamelCase__				=    StableDiffusionPipeline.from_pretrained(_a						)

       # 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







  @unittest.skipIf(torch_device != """cuda"""	,      """This test requires a GPU"""						)
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.dummy_cond_unet
       UpperCamelCase__				=    PNDMScheduler(skip_prk_steps=_a						)
       UpperCamelCase__				=    self.dummy_vae
       UpperCamelCase__				=    self.dummy_text_encoder
       UpperCamelCase__				=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

       # put models in fp16
       UpperCamelCase__				=    unet.half()
       UpperCamelCase__				=    vae.half()
       UpperCamelCase__				=    bert.half()

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__				=    StableDiffusionPipeline(
           unet=_a	,      scheduler=_a	,      vae=_a	,      text_encoder=_a	,      tokenizer=_a	,      safety_checker=_a	,      feature_extractor=self.dummy_extractor	,      )
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    '''A painting of a squirrel eating a burger'''
       UpperCamelCase__				=    sd_pipe([prompt]	,      num_inference_steps=2	,      output_type="""np"""						).images

       assert image.shape == (1, 64, 64, 3)






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

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,      safety_checker=_a						)
       UpperCamelCase__				=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    (
           '''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 '''
       )
       UpperCamelCase__				=    40_03_66_03_46
       UpperCamelCase__				=    7

       # without safety guidance (sld_guidance_scale = 0)
       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=0	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       # without safety guidance (strong configuration)
       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=20_00	,      sld_warmup_steps=7	,      sld_threshold=0.025	,      sld_momentum_scale=0.5	,      sld_mom_beta=0.7	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

       assert image.shape == (1, 5_12, 5_12, 3)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,      safety_checker=_a						)
       UpperCamelCase__				=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    '''padme amidala taking a bath artwork, safe for work, no nudity'''
       UpperCamelCase__				=    27_34_97_17_55
       UpperCamelCase__				=    7

       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=0	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

       assert image.shape == (1, 5_12, 5_12, 3)

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

       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=20_00	,      sld_warmup_steps=7	,      sld_threshold=0.025	,      sld_momentum_scale=0.5	,      sld_mom_beta=0.7	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

       assert image.shape == (1, 5_12, 5_12, 3)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""						)
       UpperCamelCase__				=    sd_pipe.to(_a						)
       sd_pipe.set_progress_bar_config(disable=_a						)

       UpperCamelCase__				=    (
           '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
           ''' leyendecker'''
       )
       UpperCamelCase__				=    10_44_35_52_34
       UpperCamelCase__				=    12

       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=0	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    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_12, 5_12, 3)

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

       UpperCamelCase__				=    torch.manual_seed(_a						)
       UpperCamelCase__				=    sd_pipe(
           [prompt]	,      generator=_a	,      guidance_scale=_a	,      num_inference_steps=50	,      output_type="""np"""	,      width=5_12	,      height=5_12	,      sld_guidance_scale=20_00	,      sld_warmup_steps=7	,      sld_threshold=0.025	,      sld_momentum_scale=0.5	,      sld_mom_beta=0.7	,      )

       UpperCamelCase__				=    output.images
       UpperCamelCase__				=    image[0, -3:, -3:, -1]
       UpperCamelCase__				=    np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]						)
       assert image.shape == (1, 5_12, 5_12, 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						):
       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"""]	,      )





