





'''simple docstring'''






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		lowerCamelCase  (      UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase       ):
 snake_case_      =   StableDiffusionInstructPixaPixPipeline
 snake_case_      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
 snake_case_      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case_      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
 snake_case_      =   IMAGE_TO_IMAGE_IMAGE_PARAMS



 def   _lowerCamelCase    (   self	):
   torch.manual_seed(0	)
   lowerCAmelCase     :    Optional[int]						=   UNetaDConditionModel(
       block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=8		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
   lowerCAmelCase     :    Union[str, Any]						=   PNDMScheduler(skip_prk_steps=_a	)
   torch.manual_seed(0	)
   lowerCAmelCase     :    Optional[int]						=   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		,      )
   torch.manual_seed(0	)
   lowerCAmelCase     :    Any						=   CLIPTextConfig(
       bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_000		,      )
   lowerCAmelCase     :    Union[str, Any]						=   CLIPTextModel(_a	)
   lowerCAmelCase     :    Dict						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

   lowerCAmelCase     :    List[str]						=   {
       """unet""": unet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components



 def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
   lowerCAmelCase     :    Optional[int]						=   floats_tensor((1, 3, 32, 32)		,      rng=random.Random(_a	)	).to(_a	)
   lowerCAmelCase     :    Union[str, Any]						=   image.cpu().permute(0		,      2		,      3		,      1	)[0]
   lowerCAmelCase     :    Tuple						=   Image.fromarray(np.uinta(_a	)	).convert("RGB"	)
   if str(_a	).startswith("mps"	):
     lowerCAmelCase     :    str						=   torch.manual_seed(_a	)
   else:
     lowerCAmelCase     :    Optional[Any]						=   torch.Generator(device=_a	).manual_seed(_a	)
   lowerCAmelCase     :    Union[str, Any]						=   {
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 6.0,
       """image_guidance_scale""": 1,
       """output_type""": """numpy""",
   }
   return inputs



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Dict						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowerCAmelCase     :    Tuple						=   self.get_dummy_components()
   lowerCAmelCase     :    Dict						=   StableDiffusionInstructPixaPixPipeline(**_a	)
   lowerCAmelCase     :    Optional[Any]						=   sd_pipe.to(_a	)
   sd_pipe.set_progress_bar_config(disable=_a	)

   lowerCAmelCase     :    Tuple						=   self.get_dummy_inputs(_a	)
   lowerCAmelCase     :    Tuple						=   sd_pipe(**_a	).images
   lowerCAmelCase     :    Any						=   image[0, -3:, -3:, -1]
   assert image.shape == (1, 32, 32, 3)
   lowerCAmelCase     :    List[str]						=   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Tuple						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowerCAmelCase     :    Optional[Any]						=   self.get_dummy_components()
   lowerCAmelCase     :    Optional[Any]						=   StableDiffusionInstructPixaPixPipeline(**_a	)
   lowerCAmelCase     :    Union[str, Any]						=   sd_pipe.to(_a	)
   sd_pipe.set_progress_bar_config(disable=_a	)

   lowerCAmelCase     :    List[str]						=   self.get_dummy_inputs(_a	)
   lowerCAmelCase     :    Optional[int]						=   """french fries"""
   lowerCAmelCase     :    Optional[int]						=   sd_pipe(**_a		,      negative_prompt=_a	)
   lowerCAmelCase     :    Optional[Any]						=   output.images
   lowerCAmelCase     :    str						=   image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   lowerCAmelCase     :    str						=   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Optional[int]						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowerCAmelCase     :    int						=   self.get_dummy_components()
   lowerCAmelCase     :    Any						=   StableDiffusionInstructPixaPixPipeline(**_a	)
   lowerCAmelCase     :    Optional[Any]						=   sd_pipe.to(_a	)
   sd_pipe.set_progress_bar_config(disable=_a	)

   lowerCAmelCase     :    int						=   self.get_dummy_inputs(_a	)
   lowerCAmelCase     :    Dict						=   [inputs["""prompt"""]] * 2

   lowerCAmelCase     :    Optional[int]						=   np.array(inputs["image"]	).astype(np.floataa	) / 255.0
   lowerCAmelCase     :    Any						=   torch.from_numpy(_a	).unsqueeze(0	).to(_a	)
   lowerCAmelCase     :    Tuple						=   image / 2 + 0.5
   lowerCAmelCase     :    int						=   image.permute(0		,      3		,      1		,      2	)
   lowerCAmelCase     :    Optional[Any]						=   image.repeat(2		,      1		,      1		,      1	)

   lowerCAmelCase     :    Optional[Any]						=   sd_pipe(**_a	).images
   lowerCAmelCase     :    Tuple						=   image[-1, -3:, -3:, -1]

   assert image.shape == (2, 32, 32, 3)
   lowerCAmelCase     :    Optional[int]						=   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Dict						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowerCAmelCase     :    Optional[Any]						=   self.get_dummy_components()
   lowerCAmelCase     :    Union[str, Any]						=   EulerAncestralDiscreteScheduler(
       beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"	)
   lowerCAmelCase     :    Optional[int]						=   StableDiffusionInstructPixaPixPipeline(**_a	)
   lowerCAmelCase     :    Tuple						=   sd_pipe.to(_a	)
   sd_pipe.set_progress_bar_config(disable=_a	)

   lowerCAmelCase     :    Tuple						=   self.get_dummy_inputs(_a	)
   lowerCAmelCase     :    List[str]						=   sd_pipe(**_a	).images
   lowerCAmelCase     :    Dict						=   image[0, -3:, -3:, -1]

   lowerCAmelCase     :    Any						=   [round(_a		,      4	) for x in image_slice.flatten().tolist()]
   print(",".join([str(_a	) for x in slice]	)	)

   assert image.shape == (1, 32, 32, 3)
   lowerCAmelCase     :    Dict						=   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

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



 def   _lowerCamelCase    (   self	):
   super().test_inference_batch_single_identical(expected_max_diff=3e-3	)





 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    int						=   self.get_dummy_components()
   lowerCAmelCase     :    List[str]						=   StableDiffusionInstructPixaPixPipeline(**_a	)
   lowerCAmelCase     :    Optional[int]						=   VaeImageProcessor(do_resize=_a		,      do_normalize=_a	)
   lowerCAmelCase     :    Optional[Any]						=   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)

   lowerCAmelCase     :    str						=   pipe(**self.get_dummy_inputs_by_type(_a		,      input_image_type="pt"	)	)[0]

   lowerCAmelCase     :    Any						=   components["""vae"""]
   lowerCAmelCase     :    Any						=   self.get_dummy_inputs_by_type(_a		,      input_image_type="pt"	)

   for image_param in self.image_latents_params:
     if image_param in inputs.keys():
       lowerCAmelCase     :    List[str]						=   vae.encode(inputs[image_param]	).latent_dist.mode()

   lowerCAmelCase     :    Dict						=   pipe(**_a	)[0]

   lowerCAmelCase     :    Any						=   np.abs(out - out_latents_inputs	).max()
   self.assertLess(_a		,      1e-4		,      "passing latents as image input generate different result from passing image"	)






@slow
@require_torch_gpu
class 		lowerCamelCase  (      unittest.TestCase       ):



 def   _lowerCamelCase    (   self	):
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()



 def   _lowerCamelCase    (   self		,      a_=0	):
   lowerCAmelCase     :    Optional[int]						=   torch.manual_seed(_a	)
   lowerCAmelCase     :    Dict						=   load_image(
       "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
   lowerCAmelCase     :    int						=   {
       """prompt""": """turn him into a cyborg""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 3,
       """guidance_scale""": 7.5,
       """image_guidance_scale""": 1.0,
       """output_type""": """numpy""",
   }
   return inputs



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[Any]						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       "timbrooks/instruct-pix2pix"		,      safety_checker=_a	)
   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing()

   lowerCAmelCase     :    int						=   self.get_inputs()
   lowerCAmelCase     :    Any						=   pipe(**_a	).images
   lowerCAmelCase     :    List[str]						=   image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   lowerCAmelCase     :    str						=   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Optional[Any]						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       "timbrooks/instruct-pix2pix"		,      safety_checker=_a	)
   lowerCAmelCase     :    List[Any]						=   LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing()

   lowerCAmelCase     :    Union[str, Any]						=   self.get_inputs()
   lowerCAmelCase     :    int						=   pipe(**_a	).images
   lowerCAmelCase     :    str						=   image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   lowerCAmelCase     :    int						=   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Any						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       "timbrooks/instruct-pix2pix"		,      safety_checker=_a	)
   lowerCAmelCase     :    Tuple						=   DDIMScheduler.from_config(pipe.scheduler.config	)
   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing()

   lowerCAmelCase     :    Optional[Any]						=   self.get_inputs()
   lowerCAmelCase     :    Tuple						=   pipe(**_a	).images
   lowerCAmelCase     :    Any						=   image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   lowerCAmelCase     :    Any						=   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   0

   def callback_fn(a_		,      a_		,      a_	) -> None:
     lowerCAmelCase     :    List[Any]						=   True
     nonlocal number_of_steps
     number_of_steps += 1
     if step == 1:
       lowerCAmelCase     :    List[Any]						=   latents.detach().cpu().numpy()
       assert latents.shape == (1, 4, 64, 64)
       lowerCAmelCase     :    Dict						=   latents[0, -3:, -3:, -1]
       lowerCAmelCase     :    Union[str, Any]						=   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

       assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
     elif step == 2:
       lowerCAmelCase     :    List[Any]						=   latents.detach().cpu().numpy()
       assert latents.shape == (1, 4, 64, 64)
       lowerCAmelCase     :    Any						=   latents[0, -3:, -3:, -1]
       lowerCAmelCase     :    Optional[int]						=   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

       assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

   lowerCAmelCase     :    int						=   False

   lowerCAmelCase     :    Any						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       "timbrooks/instruct-pix2pix"		,      safety_checker=_a		,      torch_dtype=torch.floataa	)
   lowerCAmelCase     :    Optional[Any]						=   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing()

   lowerCAmelCase     :    List[str]						=   self.get_inputs()
   pipe(**_a		,      callback=_a		,      callback_steps=1	)
   assert callback_fn.has_been_called
   assert number_of_steps == 3



 def   _lowerCamelCase    (   self	):
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCAmelCase     :    List[str]						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       "timbrooks/instruct-pix2pix"		,      safety_checker=_a		,      torch_dtype=torch.floataa	)
   lowerCAmelCase     :    str						=   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing(1	)
   pipe.enable_sequential_cpu_offload()

   lowerCAmelCase     :    Any						=   self.get_inputs()
   lowerCAmelCase     :    Tuple						=   pipe(**_a	)

   lowerCAmelCase     :    Any						=   torch.cuda.max_memory_allocated()
   # make sure that less than 2.2 GB is allocated
   assert mem_bytes < 2.2 * 10**9





 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Tuple						=   self.get_inputs()
   # resize to resolution that is divisible by 8 but not 16 or 32
   lowerCAmelCase     :    str						=   inputs["""image"""].resize((504, 504)	)

   lowerCAmelCase     :    List[Any]						=   """timbrooks/instruct-pix2pix"""
   lowerCAmelCase     :    List[str]						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
       _a		,      safety_checker=_a		,      )
   pipe.to(_a	)
   pipe.set_progress_bar_config(disable=_a	)
   pipe.enable_attention_slicing()

   lowerCAmelCase     :    str						=   pipe(**_a	)
   lowerCAmelCase     :    str						=   output.images[0]

   lowerCAmelCase     :    Any						=   image[255:258, 383:386, -1]

   assert image.shape == (504, 504, 3)
   lowerCAmelCase     :    List[str]						=   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

   assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3










'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   tempfile.mkdtemp()

			# fmt: off
			lowerCAmelCase     :    List[Any]						=   ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
			# fmt: on
			lowerCAmelCase     :    Dict						=   dict(zip(a_		,      range(len(a_	)	)	)	)
			lowerCAmelCase     :    List[str]						=   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
			lowerCAmelCase     :    List[Any]						=   {"unk_token": "<unk>"}

			lowerCAmelCase     :    int						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]	)
			lowerCAmelCase     :    List[str]						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]	)
			with open(self.vocab_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write(json.dumps(a_	) + "\n"	)
			with open(self.merges_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write("\n".join(a_	)	)

			lowerCAmelCase     :    Dict						=   {
			    "do_resize": True,
			    "size": 20,
			    "do_center_crop": True,
			    "crop_size": 18,
			    "do_normalize": True,
			    "image_mean": [0.48145466, 0.4578275, 0.40821073],
			    "image_std": [0.26862954, 0.26130258, 0.27577711],
			}
			lowerCAmelCase     :    int						=   os.path.join(self.tmpdirname		,      a_	)
			with open(self.image_processor_file		,      "w"		,      encoding="utf-8"	) as fp:
					json.dump(a_		,      a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return CLIPTokenizer.from_pretrained(self.tmpdirname		,      pad_token="!"		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      pad_token="!"		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return OwlViTImageProcessor.from_pretrained(self.tmpdirname		,      **a_	)



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   [np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta	)]

			lowerCAmelCase     :    Optional[int]						=   [Image.fromarray(np.moveaxis(a_		,      0		,      -1	)	) for x in image_inputs]

			return image_inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()
			lowerCAmelCase     :    Union[str, Any]						=   self.get_rust_tokenizer()
			lowerCAmelCase     :    Optional[int]						=   self.get_image_processor()

			lowerCAmelCase     :    Optional[int]						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)
			processor_slow.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    Optional[Any]						=   OwlViTProcessor.from_pretrained(self.tmpdirname		,      use_fast=a_	)

			lowerCAmelCase     :    int						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)
			processor_fast.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    List[Any]						=   OwlViTProcessor.from_pretrained(self.tmpdirname	)

			self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()	)
			self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()	)
			self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()	)
			self.assertIsInstance(processor_slow.tokenizer		,      a_	)
			self.assertIsInstance(processor_fast.tokenizer		,      a_	)

			self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()	)
			self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()	)
			self.assertIsInstance(processor_slow.image_processor		,      a_	)
			self.assertIsInstance(processor_fast.image_processor		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   OwlViTProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()	)
			processor.save_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    int						=   self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"	)
			lowerCAmelCase     :    Dict						=   self.get_image_processor(do_normalize=a_	)

			lowerCAmelCase     :    Optional[int]						=   OwlViTProcessor.from_pretrained(
			    self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=a_	)

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

			self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()	)
			self.assertIsInstance(processor.image_processor		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.get_image_processor()
			lowerCAmelCase     :    Optional[int]						=   self.get_tokenizer()

			lowerCAmelCase     :    str						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Optional[int]						=   self.prepare_image_inputs()

			lowerCAmelCase     :    List[str]						=   image_processor(a_		,      return_tensors="np"	)
			lowerCAmelCase     :    Any						=   processor(images=a_		,      return_tensors="np"	)

			for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()		,      input_processor[key].sum()		,      delta=1e-2	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.get_image_processor()
			lowerCAmelCase     :    List[str]						=   self.get_tokenizer()

			lowerCAmelCase     :    int						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Tuple						=   "lower newer"

			lowerCAmelCase     :    int						=   processor(text=a_		,      return_tensors="np"	)

			lowerCAmelCase     :    Tuple						=   tokenizer(a_		,      return_tensors="np"	)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key][0].tolist()		,      encoded_processor[key][0].tolist()	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.get_image_processor()
			lowerCAmelCase     :    int						=   self.get_tokenizer()

			lowerCAmelCase     :    List[str]						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Dict						=   "lower newer"
			lowerCAmelCase     :    Tuple						=   self.prepare_image_inputs()

			lowerCAmelCase     :    Any						=   processor(text=a_		,      images=a_	)

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

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    List[Any]						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    Dict						=   ["cat", "nasa badge"]
			lowerCAmelCase     :    Optional[int]						=   processor(text=a_	)

			lowerCAmelCase     :    Optional[int]						=   16
			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (2, seq_length)	)

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    Tuple						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    List[Any]						=   [["cat", "nasa badge"], ["person"]]
			lowerCAmelCase     :    int						=   processor(text=a_	)

			lowerCAmelCase     :    List[Any]						=   16
			lowerCAmelCase     :    Union[str, Any]						=   len(a_	)
			lowerCAmelCase     :    str						=   max([len(a_	) for texts in input_texts]	)

			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (batch_size * num_max_text_queries, seq_length)	)

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "google/owlvit-base-patch32"
			lowerCAmelCase     :    Union[str, Any]						=   OwlViTProcessor.from_pretrained(a_	)

			lowerCAmelCase     :    List[Any]						=   ["cat", "nasa badge"]
			lowerCAmelCase     :    Any						=   processor(text=a_	)

			lowerCAmelCase     :    Optional[Any]						=   16
			lowerCAmelCase     :    Optional[Any]						=   inputs["input_ids"]
			lowerCAmelCase     :    str						=   [
			    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			]

			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "attention_mask"]	)
			self.assertEqual(inputs["input_ids"].shape		,      (2, seq_length)	)
			self.assertListEqual(list(input_ids[0]	)		,      predicted_ids[0]	)
			self.assertListEqual(list(input_ids[1]	)		,      predicted_ids[1]	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_image_processor()
			lowerCAmelCase     :    Optional[Any]						=   self.get_tokenizer()

			lowerCAmelCase     :    Dict						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    str						=   self.prepare_image_inputs()
			lowerCAmelCase     :    Tuple						=   self.prepare_image_inputs()

			lowerCAmelCase     :    List[Any]						=   processor(images=a_		,      query_images=a_	)

			self.assertListEqual(list(inputs.keys()	)		,      ["query_pixel_values", "pixel_values"]	)

			# test if it raises when no input is passed
			with pytest.raises(a_	):
					processor()





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.get_image_processor()
			lowerCAmelCase     :    int						=   self.get_tokenizer()

			lowerCAmelCase     :    str						=   OwlViTProcessor(tokenizer=a_		,      image_processor=a_	)

			lowerCAmelCase     :    Union[str, Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCAmelCase     :    List[Any]						=   processor.batch_decode(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.batch_decode(a_	)

			self.assertListEqual(a_		,      a_	)



