


# 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       _a							(       _lowercase  ,		_lowercase  ,		_lowercase  ,		unittest.TestCase):
	_a						:						List[Any]   					=							StableDiffusionControlNetInpaintPipeline
	_a						:						Optional[int]   					=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_a						:						Optional[Any]   					=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_a						:						Union[str, Any]   					=							frozenset({'''control_image'''})  # skip `image` and `mask` for now, only test for control_image
	_a						:						Union[str, Any]   					=							TEXT_TO_IMAGE_IMAGE_PARAMS




	def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> List[Any]:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      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	)
			lowerCAmelCase__     :    Dict					  =      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	)
			lowerCAmelCase__     :    Optional[Any]					  =      DDIMScheduler(
			    beta_start=0.0_0085	,      beta_end=0.012	,      beta_schedule='''scaled_linear'''	,      clip_sample=_SCREAMING_SNAKE_CASE	,      set_alpha_to_one=_SCREAMING_SNAKE_CASE	,      )
			torch.manual_seed(0	)
			lowerCAmelCase__     :    str					  =      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__     :    Union[str, Any]					  =      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=1000	,      )
			lowerCAmelCase__     :    Optional[Any]					  =      CLIPTextModel(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

			lowerCAmelCase__     :    List[Any]					  =      {
			    '''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		:	int	,      _SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	int=0	)-> Dict:
			if str(_SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
					lowerCAmelCase__     :    Any					  =      torch.manual_seed(_SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Dict					  =      2
			lowerCAmelCase__     :    Optional[int]					  =      randn_tensor(
			    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,      generator=_SCREAMING_SNAKE_CASE	,      device=torch.device(_SCREAMING_SNAKE_CASE	)	,      )
			lowerCAmelCase__     :    Union[str, Any]					  =      floats_tensor((1, 3, 32, 32)	,      rng=random.Random(_SCREAMING_SNAKE_CASE	)	).to(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      init_image.cpu().permute(0	,      2	,      3	,      1	)[0]

			lowerCAmelCase__     :    Dict					  =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE	)	).convert('''RGB'''	).resize((64, 64)	)
			lowerCAmelCase__     :    Optional[int]					  =      Image.fromarray(np.uinta(init_image + 4	)	).convert('''RGB'''	).resize((64, 64)	)

			lowerCAmelCase__     :    Union[str, Any]					  =      {
			    '''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		:	Optional[int]	)-> int:
			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		:	Optional[Any]	)-> List[str]:
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)




	def 							UpperCAmelCase__(      self		:	Tuple	)-> Any:
			self._test_inference_batch_single_identical(expected_max_diff=2E-3	)





class       _a							(       _lowercase):
	_a						:						Any   					=							StableDiffusionControlNetInpaintPipeline
	_a						:						Dict   					=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_a						:						int   					=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_a						:						List[str]   					=							frozenset([])




	def 							UpperCAmelCase__(      self		:	Any	)-> Optional[Any]:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[Any]					  =      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	)
			lowerCAmelCase__     :    Any					  =      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	)
			lowerCAmelCase__     :    Any					  =      DDIMScheduler(
			    beta_start=0.0_0085	,      beta_end=0.012	,      beta_schedule='''scaled_linear'''	,      clip_sample=_SCREAMING_SNAKE_CASE	,      set_alpha_to_one=_SCREAMING_SNAKE_CASE	,      )
			torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      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__     :    List[Any]					  =      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=1000	,      )
			lowerCAmelCase__     :    Optional[Any]					  =      CLIPTextModel(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[Any]					  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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





class       _a							(       _lowercase  ,		_lowercase  ,		unittest.TestCase):
	_a						:						Optional[Any]   					=							StableDiffusionControlNetInpaintPipeline
	_a						:						Any   					=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_a						:						Dict   					=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    str					  =      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(_SCREAMING_SNAKE_CASE		:	int	):
					if isinstance(_SCREAMING_SNAKE_CASE	,      torch.nn.Convad	):
							torch.nn.init.normal(m.weight	)
							m.bias.data.fill_(1.0	)

			lowerCAmelCase__     :    Tuple					  =      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(_SCREAMING_SNAKE_CASE	)

			torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      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(_SCREAMING_SNAKE_CASE	)

			torch.manual_seed(0	)
			lowerCAmelCase__     :    Dict					  =      DDIMScheduler(
			    beta_start=0.0_0085	,      beta_end=0.012	,      beta_schedule='''scaled_linear'''	,      clip_sample=_SCREAMING_SNAKE_CASE	,      set_alpha_to_one=_SCREAMING_SNAKE_CASE	,      )
			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__     :    List[str]					  =      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=1000	,      )
			lowerCAmelCase__     :    Optional[Any]					  =      CLIPTextModel(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

			lowerCAmelCase__     :    Union[str, Any]					  =      MultiControlNetModel([controlneta, controlneta]	)

			lowerCAmelCase__     :    str					  =      {
			    '''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		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=0	)-> List[str]:
			if str(_SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
					lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(_SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    List[str]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Tuple					  =      2

			lowerCAmelCase__     :    List[Any]					  =      [
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,      generator=_SCREAMING_SNAKE_CASE	,      device=torch.device(_SCREAMING_SNAKE_CASE	)	,      ),
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,      generator=_SCREAMING_SNAKE_CASE	,      device=torch.device(_SCREAMING_SNAKE_CASE	)	,      ),
			]
			lowerCAmelCase__     :    str					  =      floats_tensor((1, 3, 32, 32)	,      rng=random.Random(_SCREAMING_SNAKE_CASE	)	).to(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      init_image.cpu().permute(0	,      2	,      3	,      1	)[0]

			lowerCAmelCase__     :    Tuple					  =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE	)	).convert('''RGB'''	).resize((64, 64)	)
			lowerCAmelCase__     :    str					  =      Image.fromarray(np.uinta(init_image + 4	)	).convert('''RGB'''	).resize((64, 64)	)

			lowerCAmelCase__     :    List[Any]					  =      {
			    '''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		:	Union[str, Any]	)-> List[Any]:
			lowerCAmelCase__     :    Tuple					  =      self.get_dummy_components()
			lowerCAmelCase__     :    Dict					  =      self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
			pipe.to(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Any					  =      10.0
			lowerCAmelCase__     :    Optional[int]					  =      4

			lowerCAmelCase__     :    str					  =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Tuple					  =      steps
			lowerCAmelCase__     :    Dict					  =      scale
			lowerCAmelCase__     :    Union[str, Any]					  =      pipe(**_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    List[Any]					  =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[int]					  =      steps
			lowerCAmelCase__     :    str					  =      scale
			lowerCAmelCase__     :    Tuple					  =      pipe(**_SCREAMING_SNAKE_CASE	,      control_guidance_start=0.1	,      control_guidance_end=0.2	)[0]

			lowerCAmelCase__     :    Any					  =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Tuple					  =      steps
			lowerCAmelCase__     :    Optional[int]					  =      scale
			lowerCAmelCase__     :    List[str]					  =      pipe(**_SCREAMING_SNAKE_CASE	,      control_guidance_start=[0.1, 0.3]	,      control_guidance_end=[0.2, 0.7]	)[0]

			lowerCAmelCase__     :    List[str]					  =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      steps
			lowerCAmelCase__     :    Any					  =      scale
			lowerCAmelCase__     :    List[str]					  =      pipe(**_SCREAMING_SNAKE_CASE	,      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		:	int	)-> Optional[int]:
			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		:	Union[str, Any]	)-> List[Any]:
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)




	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> List[str]:
			self._test_inference_batch_single_identical(expected_max_diff=2E-3	)




	def 							UpperCAmelCase__(      self		:	int	)-> Any:
			lowerCAmelCase__     :    List[Any]					  =      self.get_dummy_components()
			lowerCAmelCase__     :    int					  =      self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
			pipe.to(_SCREAMING_SNAKE_CASE	)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)
			with tempfile.TemporaryDirectory() as tmpdir:
					try:
							# save_pretrained is not implemented for Multi-ControlNet
							pipe.save_pretrained(_SCREAMING_SNAKE_CASE	)
					except NotImplementedError:
							pass





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




	def 							UpperCAmelCase__(      self		:	int	)-> List[str]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 							UpperCAmelCase__(      self		:	List[Any]	)-> List[str]:
			lowerCAmelCase__     :    List[Any]					  =      ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''	)

			lowerCAmelCase__     :    Union[str, Any]					  =      StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    '''runwayml/stable-diffusion-inpainting'''	,      safety_checker=_SCREAMING_SNAKE_CASE	,      controlnet=_SCREAMING_SNAKE_CASE	)
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

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

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

			lowerCAmelCase__     :    Union[str, Any]					  =      '''pitch black hole'''

			lowerCAmelCase__     :    Union[str, Any]					  =      load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''	).resize((512, 512)	)

			lowerCAmelCase__     :    List[Any]					  =      pipe(
			    _SCREAMING_SNAKE_CASE	,      image=_SCREAMING_SNAKE_CASE	,      mask_image=_SCREAMING_SNAKE_CASE	,      control_image=_SCREAMING_SNAKE_CASE	,      generator=_SCREAMING_SNAKE_CASE	,      output_type='''np'''	,      num_inference_steps=3	,      )

			lowerCAmelCase__     :    Any					  =      output.images[0]

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

			lowerCAmelCase__     :    Optional[Any]					  =      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		:	Optional[int]	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''	)

			lowerCAmelCase__     :    List[Any]					  =      StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    '''runwayml/stable-diffusion-v1-5'''	,      safety_checker=_SCREAMING_SNAKE_CASE	,      controlnet=_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[Any]					  =      DDIMScheduler.from_config(pipe.scheduler.config	)
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Tuple					  =      torch.Generator(device='''cpu'''	).manual_seed(33	)

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

			lowerCAmelCase__     :    Union[str, Any]					  =      load_image(
			    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''	)
			lowerCAmelCase__     :    int					  =      mask_image.resize((512, 512)	)

			lowerCAmelCase__     :    List[str]					  =      '''a handsome man with ray-ban sunglasses'''

			def make_inpaint_condition(_SCREAMING_SNAKE_CASE		:	Any	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	):
					lowerCAmelCase__     :    Optional[int]					  =      np.array(image.convert('''RGB'''	)	).astype(np.floataa	) / 255.0
					lowerCAmelCase__     :    Dict					  =      np.array(image_mask.convert('''L'''	)	).astype(np.floataa	) / 255.0

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

			lowerCAmelCase__     :    Optional[int]					  =      make_inpaint_condition(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Union[str, Any]					  =      pipe(
			    _SCREAMING_SNAKE_CASE	,      image=_SCREAMING_SNAKE_CASE	,      mask_image=_SCREAMING_SNAKE_CASE	,      control_image=_SCREAMING_SNAKE_CASE	,      guidance_scale=9.0	,      eta=1.0	,      generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=20	,      output_type='''np'''	,      )
			lowerCAmelCase__     :    str					  =      output.images[0]

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

			lowerCAmelCase__     :    str					  =      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 getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
lowerCamelCase		=	False
lowerCamelCase		=	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCamelCase		=	datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class       _a							:




	def __init__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	Dict=sys.argv[1:]	)-> str:
			lowerCAmelCase__     :    int					  =      None
			lowerCAmelCase__     :    Optional[int]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      1
			lowerCAmelCase__  ,			lowerCAmelCase__     :    List[str]					  =      getopt.getopt(_SCREAMING_SNAKE_CASE	,      '''i:o:b:s'''	,      ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
			for opt, arg in opts:
					if opt in ("-i", "--inputdir"):
							lowerCAmelCase__     :    List[Any]					  =      arg
					elif opt in ("-o", "--outfile"):
							lowerCAmelCase__     :    Optional[Any]					  =      arg
					elif opt in ("-b", "--batch_size"):
							lowerCAmelCase__     :    Tuple					  =      int(_SCREAMING_SNAKE_CASE	)
					elif opt in ("-s", "--subset_list"):
							lowerCAmelCase__     :    str					  =      arg

			assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
			assert outputfile is not None and not os.path.isfile(_SCREAMING_SNAKE_CASE	), F'{outputfile}'
			if subset_list is not None:
					with open(os.path.realpath(_SCREAMING_SNAKE_CASE	)	) as f:
							lowerCAmelCase__     :    Union[str, Any]					  =      {self._vqa_file_split()[0] for x in tryload(_SCREAMING_SNAKE_CASE	)}
			else:
					lowerCAmelCase__     :    int					  =      None

			lowerCAmelCase__     :    List[Any]					  =      CONFIG
			if torch.cuda.is_available():
					lowerCAmelCase__     :    List[Any]					  =      '''cuda'''
			lowerCAmelCase__     :    Optional[int]					  =      os.path.realpath(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      os.path.realpath(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    str					  =      Preprocess(self.config	)
			lowerCAmelCase__     :    str					  =      GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,      config=self.config	)
			lowerCAmelCase__     :    Tuple					  =      batch_size if batch_size != 0 else 1
			lowerCAmelCase__     :    Dict					  =      DEFAULT_SCHEMA




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
			lowerCAmelCase__     :    List[Any]					  =      os.path.join(self.inputdir	,      _SCREAMING_SNAKE_CASE	)
			return (img_id, filepath)




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> Tuple:
			lowerCAmelCase__     :    Any					  =      []
			for i, file in enumerate(os.listdir(self.inputdir	)	):
					if self.subset_list is not None and i not in self.subset_list:
							continue
					batch.append(self._vqa_file_split(_SCREAMING_SNAKE_CASE	)	)
					if len(_SCREAMING_SNAKE_CASE	) == self.batch:
							lowerCAmelCase__     :    Optional[int]					  =      batch
							lowerCAmelCase__     :    Union[str, Any]					  =      []
							yield list(map(_SCREAMING_SNAKE_CASE	,      zip(*_SCREAMING_SNAKE_CASE	)	)	)

			for i in range(1	):
					yield list(map(_SCREAMING_SNAKE_CASE	,      zip(*_SCREAMING_SNAKE_CASE	)	)	)




	def __call__(      self		:	int	)-> Dict:
			# make writer
			if not TEST:
					lowerCAmelCase__     :    Union[str, Any]					  =      datasets.ArrowWriter(features=self.schema	,      path=self.outputfile	)
			# do file generator
			for i, (img_ids, filepaths) in enumerate(self.file_generator	):
					lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.preprocess(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    List[Any]					  =      self.model(
					    _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      scales_yx=_SCREAMING_SNAKE_CASE	,      padding='''max_detections'''	,      max_detections=self.config.MAX_DETECTIONS	,      pad_value=0	,      return_tensors='''np'''	,      location='''cpu'''	,      )
					lowerCAmelCase__     :    Any					  =      output_dict.pop('''normalized_boxes'''	)
					if not TEST:
							lowerCAmelCase__     :    Optional[int]					  =      np.array(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    str					  =      self.schema.encode_batch(_SCREAMING_SNAKE_CASE	)
							writer.write_batch(_SCREAMING_SNAKE_CASE	)
					if TEST:
							break
					# finalizer the writer
			if not TEST:
					lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[int]					  =      writer.finalize()
					print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		try:
				lowerCAmelCase__     :    Any					  =      json.load(_a )
				try:
						lowerCAmelCase__     :    Tuple					  =      list(data.keys() )
				except Exception:
						lowerCAmelCase__     :    List[str]					  =      [d['''img_id'''] for d in data]
		except Exception:
				try:
						lowerCAmelCase__     :    int					  =      eval(stream.read() )
				except Exception:
						lowerCAmelCase__     :    Tuple					  =      stream.read().split('''\n''' )
		return data


if __name__ == "__main__":
		lowerCamelCase		=	Extract(sys.argv[1:])
		extract()
		if not TEST:
				lowerCamelCase		=	datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)




