



'''simple docstring'''
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import UperNetForSemanticSegmentation
					from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class _A     :





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=13       ,				__UpperCAmelCase=32       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=4       ,				__UpperCAmelCase=[10, 20, 30, 40]       ,				__UpperCAmelCase=[2, 2, 3, 2]       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=37       ,				__UpperCAmelCase="gelu"       ,				__UpperCAmelCase=10       ,				__UpperCAmelCase=0.02       ,				__UpperCAmelCase=["stage2", "stage3", "stage4"]       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=None       ,				)				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   parent
					__UpperCAmelCase      :					int   =   batch_size
					__UpperCAmelCase      :					str   =   image_size
					__UpperCAmelCase      :					Any   =   num_channels
					__UpperCAmelCase      :					Union[str, Any]   =   num_stages
					__UpperCAmelCase      :					Union[str, Any]   =   hidden_sizes
					__UpperCAmelCase      :					Optional[Any]   =   depths
					__UpperCAmelCase      :					Dict   =   is_training
					__UpperCAmelCase      :					Optional[int]   =   use_labels
					__UpperCAmelCase      :					List[str]   =   intermediate_size
					__UpperCAmelCase      :					List[str]   =   hidden_act
					__UpperCAmelCase      :					Optional[Any]   =   type_sequence_label_size
					__UpperCAmelCase      :					Tuple   =   initializer_range
					__UpperCAmelCase      :					List[str]   =   out_features
					__UpperCAmelCase      :					Dict   =   num_labels
					__UpperCAmelCase      :					Optional[int]   =   scope
					__UpperCAmelCase      :					Union[str, Any]   =   num_stages





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					List[Any]   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					__UpperCAmelCase      :					List[str]   =   None
					if self.use_labels:
								__UpperCAmelCase      :					List[Any]   =   ids_tensor([self.batch_size]       ,				self.type_sequence_label_size )

					__UpperCAmelCase      :					str   =   self.get_config()

					return config, pixel_values, labels





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

					'''simple docstring'''
					return ConvNextConfig(
					    num_channels=self.num_channels       ,				num_stages=self.num_stages       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				is_training=self.is_training       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				out_features=self.out_features       ,				)





		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					return UperNetConfig(
					    backbone_config=self.get_backbone_config()       ,				hidden_size=512       ,				pool_scales=[1, 2, 3, 6]       ,				use_auxiliary_head=_a       ,				auxiliary_loss_weight=0.4       ,				auxiliary_in_channels=40       ,				auxiliary_channels=256       ,				auxiliary_num_convs=1       ,				auxiliary_concat_input=_a       ,				loss_ignore_index=255       ,				num_labels=self.num_labels       ,				)





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   UperNetForSemanticSegmentation(config=_a )
					model.to(_a )
					model.eval()
					__UpperCAmelCase      :					List[Any]   =   model(_a )
					self.parent.assertEqual(
					    result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size, self.image_size) )





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

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   self.prepare_config_and_inputs()
					(
					    __UpperCAmelCase
					)      :					List[Any]   =   config_and_inputs
					__UpperCAmelCase      :					Optional[Any]   =   {'pixel_values': pixel_values}
					return config, inputs_dict



@require_torch
class _A     (			SCREAMING_SNAKE_CASE_		, SCREAMING_SNAKE_CASE_		, unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			Union[str, Any]  					=	(UperNetForSemanticSegmentation,) if is_torch_available() else ()
		_SCREAMING_SNAKE_CASE    :			Union[str, Any]  					=	{"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
		_SCREAMING_SNAKE_CASE    :			Dict  					=	False
		_SCREAMING_SNAKE_CASE    :			str  					=	False
		_SCREAMING_SNAKE_CASE    :			List[str]  					=	False
		_SCREAMING_SNAKE_CASE    :			List[str]  					=	False
		_SCREAMING_SNAKE_CASE    :			List[str]  					=	False
		_SCREAMING_SNAKE_CASE    :			Tuple  					=	False





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   UperNetModelTester(self )
					__UpperCAmelCase      :					str   =   ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37 )





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

					'''simple docstring'''
					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()





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

					'''simple docstring'''
					return





		def 							__A				(   self )				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase      :					Union[str, Any]   =   model_class(_a )
								__UpperCAmelCase      :					Optional[Any]   =   inspect.signature(model.forward )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__UpperCAmelCase      :					Optional[int]   =   [*signature.parameters.keys()]

								__UpperCAmelCase      :					List[Any]   =   ['pixel_values']
								self.assertListEqual(arg_names[:1]       ,				_a )





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*_a )





		@unittest.skip(reason="""UperNet does not use inputs_embeds""" )
		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					pass





		@unittest.skip(reason="""UperNet does not support input and output embeddings""" )
		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					pass





		@unittest.skip(reason="""UperNet does not have a base model""" )
		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					pass





		@unittest.skip(reason="""UperNet does not have a base model""" )
		def 							__A				(   self )				->   int:

					'''simple docstring'''
					pass





		@require_torch_multi_gpu
		@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`""" )
		def 							__A				(   self )				->   Optional[int]:

					'''simple docstring'''
					pass





		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					pass





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					def check_hidden_states_output(__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase ):
								__UpperCAmelCase      :					Dict   =   model_class(_a )
								model.to(_a )
								model.eval()

								with torch.no_grad():
											__UpperCAmelCase      :					Optional[Any]   =   model(**self._prepare_for_class(_a       ,				_a ) )

								__UpperCAmelCase      :					Dict   =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								__UpperCAmelCase      :					List[str]   =   self.model_tester.num_stages
								self.assertEqual(len(_a )       ,				expected_num_stages + 1 )

								# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:] )       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

					__UpperCAmelCase      :					str   =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase      :					int   =   True
								check_hidden_states_output(_a       ,				_a       ,				_a )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								__UpperCAmelCase      :					Any   =   True

								check_hidden_states_output(_a       ,				_a       ,				_a )





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

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   self.model_tester.prepare_config_and_inputs_for_common()

					__UpperCAmelCase      :					Tuple   =   _config_zero_init(_a )
					__UpperCAmelCase      :					List[Any]   =   _config_zero_init(configs_no_init.backbone_config )
					for model_class in self.all_model_classes:
								__UpperCAmelCase      :					str   =   model_class(config=_a )
								for name, param in model.named_parameters():
											if param.requires_grad:
														self.assertIn(
														    ((param.data.mean() * 1E9).round() / 1E9).item()       ,				[0.0, 1.0]       ,				msg=f'Parameter {name} of model {model_class} seems not properly initialized'       ,				)





		@unittest.skip(reason="""UperNet does not have tied weights""" )
		def 							__A				(   self )				->   Optional[Any]:

					'''simple docstring'''
					pass





		@slow
		def 							__A				(   self )				->   int:

					'''simple docstring'''
					for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__UpperCAmelCase      :					List[str]   =   UperNetForSemanticSegmentation.from_pretrained(_a )
								self.assertIsNotNone(_a )


def      lowercase_		(      ):

			"""simple docstring"""





			__UpperCAmelCase      :					Optional[int]   =   hf_hub_download(
			    repo_id="""hf-internal-testing/fixtures_ade20k"""	,							repo_type="""dataset"""	,							filename="""ADE_val_00000001.jpg"""       )
			__UpperCAmelCase      :					List[Any]   =   Image.open(_lowerCAmelCase       ).convert("""RGB"""       )
			return image






@require_torch
@require_vision
@slow
class _A     (			unittest.TestCase			):





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

					'''simple docstring'''
					__UpperCAmelCase      :					List[Any]   =   AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""" )
					__UpperCAmelCase      :					Any   =   UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""" ).to(_a )

					__UpperCAmelCase      :					Tuple   =   prepare_img()
					__UpperCAmelCase      :					int   =   processor(images=_a       ,				return_tensors="""pt""" ).to(_a )

					with torch.no_grad():
								__UpperCAmelCase      :					List[str]   =   model(**_a )

					__UpperCAmelCase      :					Union[str, Any]   =   torch.Size((1, model.config.num_labels, 512, 512) )
					self.assertEqual(outputs.logits.shape       ,				_a )

					__UpperCAmelCase      :					Any   =   torch.tensor(
					    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(_a )
					self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]       ,				_a       ,				atol=1E-4 ) )





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

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""" )
					__UpperCAmelCase      :					str   =   UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""" ).to(_a )

					__UpperCAmelCase      :					List[str]   =   prepare_img()
					__UpperCAmelCase      :					Tuple   =   processor(images=_a       ,				return_tensors="""pt""" ).to(_a )

					with torch.no_grad():
								__UpperCAmelCase      :					int   =   model(**_a )

					__UpperCAmelCase      :					Optional[int]   =   torch.Size((1, model.config.num_labels, 512, 512) )
					self.assertEqual(outputs.logits.shape       ,				_a )

					__UpperCAmelCase      :					Tuple   =   torch.tensor(
					    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(_a )
					self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]       ,				_a       ,				atol=1E-4 ) )









'''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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  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 						_lowercase     ( self      :	Union[str, Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       Tuple           = 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=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       Dict           = 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       :       Dict           = 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       :       Union[str, Any]           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Any           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Any			,	_a      :	List[str]=0   )					->					List[str]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Any           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Any           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Union[str, Any]           = 2
    __lowerCamelCase       :       Any           = randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,	generator=_a			,	device=torch.device(_a   )			,	)
    __lowerCamelCase       :       Optional[Any]           = floats_tensor(control_image.shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       List[Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       int           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       int           = {
        '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 						_lowercase     ( self      :	List[str]   )					->					Optional[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 						_lowercase     ( self      :	str   )					->					List[str]:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	Any   )					->					List[str]:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  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 						_lowercase     ( self      :	List[Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       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(_a      :	str   ):
      if isinstance(_a			,	torch.nn.Convad   ):
        torch.nn.init.normal(m.weight   )
        m.bias.data.fill_(1.0   )

    __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)			,	)
    controlneta.controlnet_down_blocks.apply(_a   )

    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)			,	)
    controlneta.controlnet_down_blocks.apply(_a   )

    torch.manual_seed(0   )
    __lowerCamelCase       :       List[Any]           = DDIMScheduler(
        beta_start=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    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       :       Optional[int]           = 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       :       Dict           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Optional[Any]           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

    __lowerCamelCase       :       List[str]           = 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 						_lowercase     ( self      :	Dict			,	_a      :	int			,	_a      :	Union[str, Any]=0   )					->					Union[str, Any]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Optional[Any]           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Union[str, Any]           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Any           = 2

    __lowerCamelCase       :       Any           = [
        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   )			,	),
    ]

    __lowerCamelCase       :       List[Any]           = floats_tensor(control_image[0].shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       Union[str, Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       Dict           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       Dict           = {
        '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 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = self.get_dummy_components()
    __lowerCamelCase       :       Any           = self.pipeline_class(**_a   )
    pipe.to(_a   )

    __lowerCamelCase       :       Optional[int]           = 10.0
    __lowerCamelCase       :       Tuple           = 4

    __lowerCamelCase       :       str           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       int           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       Optional[Any]           = pipe(**_a   )[0]

    __lowerCamelCase       :       Dict           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       List[str]           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       Dict           = pipe(**_a			,	control_guidance_start=0.1			,	control_guidance_end=0.2   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Union[str, Any]           = steps
    __lowerCamelCase       :       Any           = scale
    __lowerCamelCase       :       Optional[int]           = pipe(**_a			,	control_guidance_start=[0.1, 0.3]			,	control_guidance_end=[0.2, 0.7]   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Tuple           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       List[str]           = 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 						_lowercase     ( self      :	Optional[Any]   )					->					List[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 						_lowercase     ( self      :	Dict   )					->					Tuple:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	str   )					->					int:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    __lowerCamelCase       :       int           = self.get_dummy_components()
    __lowerCamelCase       :       List[Any]           = 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 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'   )

    __lowerCamelCase       :       List[str]           = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'			,	safety_checker=_a			,	controlnet=_a   )
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=_a   )

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

    __lowerCamelCase       :       Dict           = pipe(
        _a			,	_a			,	control_image=_a			,	generator=_a			,	output_type='np'			,	num_inference_steps=50			,	strength=0.6			,	)

    __lowerCamelCase       :       Optional[Any]           = output.images[0]

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

    __lowerCamelCase       :       List[str]           = 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






