






'''simple docstring'''







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class _lowercase	(				_lowercase				,      _lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       StableDiffusionControlNetImgaImgPipeline
							a				      =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
							a				      =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							a				      =       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}		)
							a				      =       IMAGE_TO_IMAGE_IMAGE_PARAMS




							def 		lowerCamelCase_				(							self:				Any     ):
														torch.manual_seed(0     )
														lowerCamelCase__					:	List[str]         =       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__					:	List[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__					:	List[Any]         =       DDIMScheduler(
														    beta_start=0.00_085			,							beta_end=0.012			,							beta_schedule="""scaled_linear"""			,							clip_sample=UpperCamelCase__			,							set_alpha_to_one=UpperCamelCase__			,							)
														torch.manual_seed(0     )
														lowerCamelCase__					:	List[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__					:	Tuple         =       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=1_000			,							)
														lowerCamelCase__					:	str         =       CLIPTextModel(UpperCamelCase__     )
														lowerCamelCase__					:	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




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Tuple=0     ):
														if str(UpperCamelCase__     ).startswith("""mps"""     ):
																					lowerCamelCase__					:	Any         =       torch.manual_seed(UpperCamelCase__     )
														else:
																					lowerCamelCase__					:	int         =       torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       2
														lowerCamelCase__					:	Optional[Any]         =       randn_tensor(
														    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,							generator=UpperCamelCase__			,							device=torch.device(UpperCamelCase__     )			,							)
														lowerCamelCase__					:	Optional[Any]         =       floats_tensor(control_image.shape			,							rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	str         =       image.cpu().permute(0			,							2			,							3			,							1     )[0]
														lowerCamelCase__					:	List[str]         =       Image.fromarray(np.uinta(UpperCamelCase__     )     ).convert("""RGB"""     ).resize((64, 64)     )
														lowerCamelCase__					:	Tuple         =       {
														    """prompt""": """A painting of a squirrel eating a burger""",
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """guidance_scale""": 6.0,
														    """output_type""": """numpy""",
														    """image""": image,
														    """control_image""": control_image,
														}

														return inputs




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														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 		lowerCamelCase_				(							self:				Optional[int]     ):
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )




							def 		lowerCamelCase_				(							self:				Any     ):
														self._test_inference_batch_single_identical(expected_max_diff=2e-3     )



class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       StableDiffusionControlNetImgaImgPipeline
							a				      =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
							a				      =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							a				      =       frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




							def 		lowerCamelCase_				(							self:				str     ):
														torch.manual_seed(0     )
														lowerCamelCase__					:	List[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     )

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

														lowerCamelCase__					:	Optional[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(UpperCamelCase__     )

														torch.manual_seed(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(UpperCamelCase__     )

														torch.manual_seed(0     )
														lowerCamelCase__					:	Optional[Any]         =       DDIMScheduler(
														    beta_start=0.00_085			,							beta_end=0.012			,							beta_schedule="""scaled_linear"""			,							clip_sample=UpperCamelCase__			,							set_alpha_to_one=UpperCamelCase__			,							)
														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__					:	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=1_000			,							)
														lowerCamelCase__					:	Optional[int]         =       CLIPTextModel(UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

														lowerCamelCase__					:	Any         =       MultiControlNetModel([controlneta, controlneta]     )

														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 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[Any]=0     ):
														if str(UpperCamelCase__     ).startswith("""mps"""     ):
																					lowerCamelCase__					:	int         =       torch.manual_seed(UpperCamelCase__     )
														else:
																					lowerCamelCase__					:	Optional[Any]         =       torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       2

														lowerCamelCase__					:	List[str]         =       [
														    randn_tensor(
														        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,							generator=UpperCamelCase__			,							device=torch.device(UpperCamelCase__     )			,							),
														    randn_tensor(
														        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,							generator=UpperCamelCase__			,							device=torch.device(UpperCamelCase__     )			,							),
														]

														lowerCamelCase__					:	Tuple         =       floats_tensor(control_image[0].shape			,							rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       image.cpu().permute(0			,							2			,							3			,							1     )[0]
														lowerCamelCase__					:	Any         =       Image.fromarray(np.uinta(UpperCamelCase__     )     ).convert("""RGB"""     ).resize((64, 64)     )
														lowerCamelCase__					:	Optional[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,
														    """control_image""": control_image,
														}

														return inputs




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       self.get_dummy_components()
														lowerCamelCase__					:	List[str]         =       self.pipeline_class(**UpperCamelCase__     )
														pipe.to(UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       10.0
														lowerCamelCase__					:	Any         =       4

														lowerCamelCase__					:	Union[str, Any]         =       self.get_dummy_inputs(UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       steps
														lowerCamelCase__					:	Optional[int]         =       scale
														lowerCamelCase__					:	Union[str, Any]         =       pipe(**UpperCamelCase__     )[0]

														lowerCamelCase__					:	Any         =       self.get_dummy_inputs(UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       steps
														lowerCamelCase__					:	Union[str, Any]         =       scale
														lowerCamelCase__					:	Tuple         =       pipe(**UpperCamelCase__			,							control_guidance_start=0.1			,							control_guidance_end=0.2     )[0]

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

														lowerCamelCase__					:	Optional[Any]         =       self.get_dummy_inputs(UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       steps
														lowerCamelCase__					:	Optional[Any]         =       scale
														lowerCamelCase__					:	Optional[Any]         =       pipe(**UpperCamelCase__			,							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 		lowerCamelCase_				(							self:				str     ):
														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 		lowerCamelCase_				(							self:				int     ):
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )




							def 		lowerCamelCase_				(							self:				int     ):
														self._test_inference_batch_single_identical(expected_max_diff=2e-3     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_dummy_components()
														lowerCamelCase__					:	str         =       self.pipeline_class(**UpperCamelCase__     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )
														with tempfile.TemporaryDirectory() as tmpdir:
																					try:
																												# save_pretrained is not implemented for Multi-ControlNet
																												pipe.save_pretrained(UpperCamelCase__     )
																					except NotImplementedError:
																												pass



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




							def 		lowerCamelCase_				(							self:				List[str]     ):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""     )

														lowerCamelCase__					:	Tuple         =       StableDiffusionControlNetImgaImgPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""			,							safety_checker=UpperCamelCase__			,							controlnet=UpperCamelCase__     )
														pipe.enable_model_cpu_offload()
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       torch.Generator(device="""cpu"""     ).manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       """evil space-punk bird"""
														lowerCamelCase__					:	List[Any]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""     ).resize((512, 512)     )
														lowerCamelCase__					:	Union[str, Any]         =       load_image(
														    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""     ).resize((512, 512)     )

														lowerCamelCase__					:	Optional[int]         =       pipe(
														    UpperCamelCase__			,							UpperCamelCase__			,							control_image=UpperCamelCase__			,							generator=UpperCamelCase__			,							output_type="""np"""			,							num_inference_steps=50			,							strength=0.6			,							)

														lowerCamelCase__					:	Optional[int]         =       output.images[0]

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

														lowerCamelCase__					:	Optional[int]         =       load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""     )

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




'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






