

# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       StableDiffusionControlNetInpaintPipeline
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE      							=       frozenset({"""control_image"""}      )  # skip `image` and `mask` for now, only test for control_image
 __SCREAMING_SNAKE_CASE      							=       TEXT_TO_IMAGE_IMAGE_PARAMS




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  torch.manual_seed(0				)
  UpperCAmelCase__           =       UNetaDConditionModel(
      block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       ControlNetModel(
      block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       DDIMScheduler(
      beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=__a , set_alpha_to_one=__a , )
  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 , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       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=1000 , )
  UpperCAmelCase__           =       CLIPTextModel(__a				)
  UpperCAmelCase__           =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

  UpperCAmelCase__           =       {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components




 def 						UpperCamelCase__							(self , __a , __a=0				)				->      List[Any]:






  """simple docstring"""





  if str(__a				).startswith('mps'				):
   UpperCAmelCase__           =       torch.manual_seed(__a				)
  else:
   UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(__a				)

  UpperCAmelCase__           =       2
  UpperCAmelCase__           =       randn_tensor(
      (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__a , device=torch.device(__a				) , )
  UpperCAmelCase__           =       floats_tensor((1, 3, 32, 32) , rng=random.Random(__a				)				).to(__a				)
  UpperCAmelCase__           =       init_image.cpu().permute(0 , 2 , 3 , 1				)[0]

  UpperCAmelCase__           =       Image.fromarray(np.uinta(__a				)				).convert('RGB'				).resize((64, 64)				)
  UpperCAmelCase__           =       Image.fromarray(np.uinta(init_image + 4				)				).convert('RGB'				).resize((64, 64)				)

  UpperCAmelCase__           =       {
      'prompt': 'A painting of a squirrel eating a burger',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
      'image': image,
      'mask_image': mask_image,
      'control_image': control_image,
  }

  return inputs




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)




 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  self._test_inference_batch_single_identical(expected_max_diff=2E-3				)







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       StableDiffusionControlNetInpaintPipeline
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE      							=       frozenset([]      )




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  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 , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       ControlNetModel(
      block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       DDIMScheduler(
      beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=__a , set_alpha_to_one=__a , )
  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 , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       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=1000 , )
  UpperCAmelCase__           =       CLIPTextModel(__a				)
  UpperCAmelCase__           =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

  UpperCAmelCase__           =       {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components







class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       StableDiffusionControlNetInpaintPipeline
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE      							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  torch.manual_seed(0				)
  UpperCAmelCase__           =       UNetaDConditionModel(
      block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
  torch.manual_seed(0				)

  def init_weights(__a				):
   if isinstance(__a , torch.nn.Convad				):
    torch.nn.init.normal(m.weight				)
    m.bias.data.fill_(1.0				)

  UpperCAmelCase__           =       ControlNetModel(
      block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
  controlneta.controlnet_down_blocks.apply(__a				)

  torch.manual_seed(0				)
  UpperCAmelCase__           =       ControlNetModel(
      block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
  controlneta.controlnet_down_blocks.apply(__a				)

  torch.manual_seed(0				)
  UpperCAmelCase__           =       DDIMScheduler(
      beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=__a , set_alpha_to_one=__a , )
  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 , )
  torch.manual_seed(0				)
  UpperCAmelCase__           =       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=1000 , )
  UpperCAmelCase__           =       CLIPTextModel(__a				)
  UpperCAmelCase__           =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

  UpperCAmelCase__           =       MultiControlNetModel([controlneta, controlneta]				)

  UpperCAmelCase__           =       {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components




 def 						UpperCamelCase__							(self , __a , __a=0				)				->      Dict:






  """simple docstring"""





  if str(__a				).startswith('mps'				):
   UpperCAmelCase__           =       torch.manual_seed(__a				)
  else:
   UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(__a				)

  UpperCAmelCase__           =       2

  UpperCAmelCase__           =       [
      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__a , device=torch.device(__a				) , ),
      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__a , device=torch.device(__a				) , ),
  ]
  UpperCAmelCase__           =       floats_tensor((1, 3, 32, 32) , rng=random.Random(__a				)				).to(__a				)
  UpperCAmelCase__           =       init_image.cpu().permute(0 , 2 , 3 , 1				)[0]

  UpperCAmelCase__           =       Image.fromarray(np.uinta(__a				)				).convert('RGB'				).resize((64, 64)				)
  UpperCAmelCase__           =       Image.fromarray(np.uinta(init_image + 4				)				).convert('RGB'				).resize((64, 64)				)

  UpperCAmelCase__           =       {
      'prompt': 'A painting of a squirrel eating a burger',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
      'image': image,
      'mask_image': mask_image,
      'control_image': control_image,
  }

  return inputs




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_dummy_components()
  UpperCAmelCase__           =       self.pipeline_class(**__a				)
  pipe.to(__a				)

  UpperCAmelCase__           =       10.0
  UpperCAmelCase__           =       4

  UpperCAmelCase__           =       self.get_dummy_inputs(__a				)
  UpperCAmelCase__           =       steps
  UpperCAmelCase__           =       scale
  UpperCAmelCase__           =       pipe(**__a				)[0]

  UpperCAmelCase__           =       self.get_dummy_inputs(__a				)
  UpperCAmelCase__           =       steps
  UpperCAmelCase__           =       scale
  UpperCAmelCase__           =       pipe(**__a , control_guidance_start=0.1 , control_guidance_end=0.2				)[0]

  UpperCAmelCase__           =       self.get_dummy_inputs(__a				)
  UpperCAmelCase__           =       steps
  UpperCAmelCase__           =       scale
  UpperCAmelCase__           =       pipe(**__a , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7]				)[0]

  UpperCAmelCase__           =       self.get_dummy_inputs(__a				)
  UpperCAmelCase__           =       steps
  UpperCAmelCase__           =       scale
  UpperCAmelCase__           =       pipe(**__a , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8]				)[0]

  # make sure that all outputs are different
  assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
  assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
  assert np.sum(np.abs(output_a - output_a				)				) > 1E-3




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)




 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  self._test_inference_batch_single_identical(expected_max_diff=2E-3				)




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_dummy_components()
  UpperCAmelCase__           =       self.pipeline_class(**__a				)
  pipe.to(__a				)
  pipe.set_progress_bar_config(disable=__a				)
  with tempfile.TemporaryDirectory() as tmpdir:
   try:
    # save_pretrained is not implemented for Multi-ControlNet
    pipe.save_pretrained(__a				)
   except NotImplementedError:
    pass







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



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'				)

  UpperCAmelCase__           =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
      'runwayml/stable-diffusion-inpainting' , safety_checker=__a , controlnet=__a				)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       torch.Generator(device='cpu'				).manual_seed(0				)
  UpperCAmelCase__           =       load_image(
      'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'				).resize((512, 512)				)

  UpperCAmelCase__           =       load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
      '/stable_diffusion_inpaint/input_bench_mask.png'				).resize((512, 512)				)

  UpperCAmelCase__           =       'pitch black hole'

  UpperCAmelCase__           =       load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				).resize((512, 512)				)

  UpperCAmelCase__           =       pipe(
      __a , image=__a , mask_image=__a , control_image=__a , generator=__a , output_type='np' , num_inference_steps=3 , )

  UpperCAmelCase__           =       output.images[0]

  assert image.shape == (512, 512, 3)

  UpperCAmelCase__           =       load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'				)

  assert np.abs(expected_image - image				).max() < 9E-2




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'				)

  UpperCAmelCase__           =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
      'runwayml/stable-diffusion-v1-5' , safety_checker=__a , controlnet=__a				)
  UpperCAmelCase__           =       DDIMScheduler.from_config(pipe.scheduler.config				)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       torch.Generator(device='cpu'				).manual_seed(33				)

  UpperCAmelCase__           =       load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'				)
  UpperCAmelCase__           =       init_image.resize((512, 512)				)

  UpperCAmelCase__           =       load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'				)
  UpperCAmelCase__           =       mask_image.resize((512, 512)				)

  UpperCAmelCase__           =       'a handsome man with ray-ban sunglasses'

  def make_inpaint_condition(__a , __a				):
   UpperCAmelCase__           =       np.array(image.convert('RGB'				)				).astype(np.floataa				) / 2_55.0
   UpperCAmelCase__           =       np.array(image_mask.convert('L'				)				).astype(np.floataa				) / 2_55.0

   assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
   UpperCAmelCase__           =       -1.0  # set as masked pixel
   UpperCAmelCase__           =       np.expand_dims(__a , 0				).transpose(0 , 3 , 1 , 2				)
   UpperCAmelCase__           =       torch.from_numpy(__a				)
   return image

  UpperCAmelCase__           =       make_inpaint_condition(__a , __a				)

  UpperCAmelCase__           =       pipe(
      __a , image=__a , mask_image=__a , control_image=__a , guidance_scale=9.0 , eta=1.0 , generator=__a , num_inference_steps=20 , output_type='np' , )
  UpperCAmelCase__           =       output.images[0]

  assert image.shape == (512, 512, 3)

  UpperCAmelCase__           =       load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'				)

  assert np.abs(expected_image - image				).max() < 9E-2






import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 import PIL

 from transformers import FlavaImageProcessor
 from transformers.image_utils import PILImageResampling
 from transformers.models.flava.image_processing_flava import (
     FLAVA_CODEBOOK_MEAN,
     FLAVA_CODEBOOK_STD,
     FLAVA_IMAGE_MEAN,
     FLAVA_IMAGE_STD,
 )
else:
 _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    None



class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a=7 , __a=3 , __a=30 , __a=400 , __a=True , __a=None , __a=True , __a=None , __a=None , __a=True , __a=1 / 255 , __a=True , __a=FLAVA_IMAGE_MEAN , __a=FLAVA_IMAGE_STD , __a=14 , __a=75 , __a=None , __a=16 , __a=0.3 , __a=None , __a=True , __a=None , __a=None , __a=True , __a=None , __a=True , __a=True , __a=FLAVA_CODEBOOK_MEAN , __a=FLAVA_CODEBOOK_STD , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       size if size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
  UpperCAmelCase__           =       codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

  UpperCAmelCase__           =       parent
  UpperCAmelCase__           =       batch_size
  UpperCAmelCase__           =       num_channels
  UpperCAmelCase__           =       do_resize
  UpperCAmelCase__           =       do_rescale
  UpperCAmelCase__           =       rescale_factor
  UpperCAmelCase__           =       min_resolution
  UpperCAmelCase__           =       max_resolution
  UpperCAmelCase__           =       size
  UpperCAmelCase__           =       resample if resample is not None else PILImageResampling.BICUBIC
  UpperCAmelCase__           =       do_normalize
  UpperCAmelCase__           =       image_mean
  UpperCAmelCase__           =       image_std
  UpperCAmelCase__           =       do_center_crop
  UpperCAmelCase__           =       crop_size

  UpperCAmelCase__           =       input_size_patches
  UpperCAmelCase__           =       total_mask_patches
  UpperCAmelCase__           =       mask_group_max_patches
  UpperCAmelCase__           =       mask_group_min_patches
  UpperCAmelCase__           =       mask_group_min_aspect_ratio
  UpperCAmelCase__           =       mask_group_max_aspect_ratio

  UpperCAmelCase__           =       codebook_do_resize
  UpperCAmelCase__           =       codebook_size
  UpperCAmelCase__           =       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
  UpperCAmelCase__           =       codebook_do_center_crop
  UpperCAmelCase__           =       codebook_crop_size
  UpperCAmelCase__           =       codebook_do_map_pixels
  UpperCAmelCase__           =       codebook_do_normalize
  UpperCAmelCase__           =       codebook_image_mean
  UpperCAmelCase__           =       codebook_image_std




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
      "resample": self.resample,
      "do_rescale": self.do_rescale,
      "rescale_factor": self.rescale_factor,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
      "input_size_patches": self.input_size_patches,
      "total_mask_patches": self.total_mask_patches,
      "mask_group_max_patches": self.mask_group_max_patches,
      "mask_group_min_patches": self.mask_group_min_patches,
      "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
      "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
      "codebook_do_resize": self.codebook_do_resize,
      "codebook_size": self.codebook_size,
      "codebook_resample": self.codebook_resample,
      "codebook_do_center_crop": self.codebook_do_center_crop,
      "codebook_crop_size": self.codebook_crop_size,
      "codebook_do_map_pixels": self.codebook_do_map_pixels,
      "codebook_do_normalize": self.codebook_do_normalize,
      "codebook_image_mean": self.codebook_image_mean,
      "codebook_image_std": self.codebook_image_std,
  }




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (self.size["height"], self.size["width"])




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (
      (self.input_size_patches, self.input_size_patches)
      if not isinstance(self.input_size_patches , __a				)
      else self.input_size_patches
  )




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 					lowercase		( _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       FlavaImageProcessor if is_vision_available() else None
 __SCREAMING_SNAKE_CASE      							=       None




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       FlavaImageProcessingTester(self				)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  self.assertTrue(hasattr(__a , 'image_mean'				)				)
  self.assertTrue(hasattr(__a , 'image_std'				)				)
  self.assertTrue(hasattr(__a , 'do_normalize'				)				)
  self.assertTrue(hasattr(__a , 'do_resize'				)				)
  self.assertTrue(hasattr(__a , 'resample'				)				)
  self.assertTrue(hasattr(__a , 'crop_size'				)				)
  self.assertTrue(hasattr(__a , 'do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'do_rescale'				)				)
  self.assertTrue(hasattr(__a , 'rescale_factor'				)				)
  self.assertTrue(hasattr(__a , 'masking_generator'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_resize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_resample'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'codebook_crop_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_map_pixels'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_normalize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_mean'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_std'				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class.from_dict(self.image_processor_dict				)
  self.assertEqual(image_processor.size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.crop_size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.codebook_size , {'height': 112, 'width': 112}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 112, 'width': 112}				)

  UpperCAmelCase__           =       self.image_processing_class.from_dict(
      self.image_processor_dict , size=42 , crop_size=84 , codebook_size=33 , codebook_crop_size=66				)
  self.assertEqual(image_processor.size , {'height': 42, 'width': 42}				)
  self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84}				)
  self.assertEqual(image_processor.codebook_size , {'height': 33, 'width': 33}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 66, 'width': 66}				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PIL images
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				)

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

  self.assertEqual(
      encoded_images.pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self , __a , __a				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random tensors
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , **__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , __a				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  # Test masking
  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  self._test_call_framework(np.ndarray , prepare_kwargs={'numpify': True}				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  self._test_call_framework(torch.Tensor , prepare_kwargs={'torchify': True}				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  random.seed(1234				)
  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_image_mask=__a , return_tensors='pt'				)
  self.assertEqual(encoded_images.bool_masked_pos.sum().item() , 75				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PIL images
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )



