



'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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():
					from PIL import Image

					from transformers import ConditionalDetrImageProcessor



class _A     (			unittest.TestCase			):





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=7       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=30       ,				__UpperCAmelCase=400       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=[0.5, 0.5, 0.5]       ,				__UpperCAmelCase=[0.5, 0.5, 0.5]       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=1 / 255       ,				__UpperCAmelCase=True       ,				)				->   List[str]:

					'''simple docstring'''
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					__UpperCAmelCase      :					Union[str, Any]   =   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
					__UpperCAmelCase      :					Optional[int]   =   parent
					__UpperCAmelCase      :					int   =   batch_size
					__UpperCAmelCase      :					str   =   num_channels
					__UpperCAmelCase      :					int   =   min_resolution
					__UpperCAmelCase      :					str   =   max_resolution
					__UpperCAmelCase      :					Optional[int]   =   do_resize
					__UpperCAmelCase      :					Tuple   =   size
					__UpperCAmelCase      :					Optional[Any]   =   do_normalize
					__UpperCAmelCase      :					int   =   image_mean
					__UpperCAmelCase      :					Tuple   =   image_std
					__UpperCAmelCase      :					str   =   do_rescale
					__UpperCAmelCase      :					Union[str, Any]   =   rescale_factor
					__UpperCAmelCase      :					List[Any]   =   do_pad





		def 							__A				(   self )				->   int:

					'''simple docstring'''
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=False )				->   Optional[int]:

					'''simple docstring'''
					if not batched:
								__UpperCAmelCase      :					Union[str, Any]   =   image_inputs[0]
								if isinstance(_a       ,				Image.Image ):
											__UpperCAmelCase      :					Any   =   image.size
								else:
											__UpperCAmelCase      :					Union[str, Any]   =   image.shape[1], image.shape[2]
								if w < h:
											__UpperCAmelCase      :					int   =   int(self.size["""shortest_edge"""] * h / w )
											__UpperCAmelCase      :					List[str]   =   self.size['shortest_edge']
								elif w > h:
											__UpperCAmelCase      :					Optional[Any]   =   self.size['shortest_edge']
											__UpperCAmelCase      :					Optional[int]   =   int(self.size["""shortest_edge"""] * w / h )
								else:
											__UpperCAmelCase      :					Dict   =   self.size['shortest_edge']
											__UpperCAmelCase      :					Union[str, Any]   =   self.size['shortest_edge']

					else:
								__UpperCAmelCase      :					Dict   =   []
								for image in image_inputs:
											__UpperCAmelCase      :					Optional[int]   =   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								__UpperCAmelCase      :					Dict   =   max(_a       ,				key=lambda __UpperCAmelCase : item[0] )[0]
								__UpperCAmelCase      :					Any   =   max(_a       ,				key=lambda __UpperCAmelCase : item[1] )[1]

					return expected_height, expected_width



@require_torch
@require_vision
class _A     (			SCREAMING_SNAKE_CASE_		, unittest.TestCase			):
		_SCREAMING_SNAKE_CASE    :			Optional[int]  					=	ConditionalDetrImageProcessor if is_vision_available() else None





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   ConditionalDetrImageProcessingTester(self )





		@property
		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   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       ,				"""size""" ) )





		def 							__A				(   self )				->   Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 18, """longest_edge""": 1_333} )
					self.assertEqual(image_processor.do_pad       ,				_a )

					__UpperCAmelCase      :					Dict   =   self.image_processing_class.from_dict(
					    self.image_processor_dict       ,				size=42       ,				max_size=84       ,				pad_and_return_pixel_mask=_a )
					self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42, """longest_edge""": 84} )
					self.assertEqual(image_processor.do_pad       ,				_a )





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

					'''simple docstring'''
					pass





		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase      :					Dict   =   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					__UpperCAmelCase      :					Optional[Any]   =   prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a )
					for image in image_inputs:
								self.assertIsInstance(_a       ,				Image.Image )

					# Test not batched input
					__UpperCAmelCase      :					Union[str, Any]   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase      :					int   =   self.image_processor_tester.get_expected_values(_a )

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

					# Test batched
					__UpperCAmelCase      :					str   =   self.image_processor_tester.get_expected_values(_a       ,				batched=_a )

					__UpperCAmelCase      :					Any   =   image_processing(_a       ,				return_tensors="""pt""" ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )       ,				)





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase      :					str   =   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					__UpperCAmelCase      :					Any   =   prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				numpify=_a )
					for image in image_inputs:
								self.assertIsInstance(_a       ,				np.ndarray )

					# Test not batched input
					__UpperCAmelCase      :					Union[str, Any]   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase      :					List[str]   =   self.image_processor_tester.get_expected_values(_a )

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

					# Test batched
					__UpperCAmelCase      :					Optional[Any]   =   image_processing(_a       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase      :					str   =   self.image_processor_tester.get_expected_values(_a       ,				batched=_a )

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





		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					# Initialize image_processing
					__UpperCAmelCase      :					Tuple   =   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					__UpperCAmelCase      :					Any   =   prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				torchify=_a )
					for image in image_inputs:
								self.assertIsInstance(_a       ,				torch.Tensor )

					# Test not batched input
					__UpperCAmelCase      :					Optional[Any]   =   image_processing(image_inputs[0]       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase      :					Tuple   =   self.image_processor_tester.get_expected_values(_a )

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

					# Test batched
					__UpperCAmelCase      :					Dict   =   image_processing(_a       ,				return_tensors="""pt""" ).pixel_values

					__UpperCAmelCase      :					Optional[Any]   =   self.image_processor_tester.get_expected_values(_a       ,				batched=_a )

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





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

					'''simple docstring'''
					# prepare image and target
					__UpperCAmelCase      :					Optional[int]   =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
					with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,				"""r""" ) as f:
								__UpperCAmelCase      :					int   =   json.loads(f.read() )

					__UpperCAmelCase      :					Tuple   =   {'image_id': 39_769, 'annotations': target}

					# encode them
					__UpperCAmelCase      :					int   =   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""" )
					__UpperCAmelCase      :					Dict   =   image_processing(images=_a       ,				annotations=_a       ,				return_tensors="""pt""" )

					# verify pixel values
					__UpperCAmelCase      :					Dict   =   torch.Size([1, 3, 800, 1_066] )
					self.assertEqual(encoding["""pixel_values"""].shape       ,				_a )

					__UpperCAmelCase      :					List[str]   =   torch.tensor([0.2796, 0.3138, 0.3481] )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				_a       ,				atol=1E-4 ) )

					# verify area
					__UpperCAmelCase      :					str   =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				_a ) )
					# verify boxes
					__UpperCAmelCase      :					Optional[int]   =   torch.Size([6, 4] )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				_a )
					__UpperCAmelCase      :					List[Any]   =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				_a       ,				atol=1E-3 ) )
					# verify image_id
					__UpperCAmelCase      :					Dict   =   torch.tensor([39_769] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				_a ) )
					# verify is_crowd
					__UpperCAmelCase      :					Tuple   =   torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				_a ) )
					# verify class_labels
					__UpperCAmelCase      :					Union[str, Any]   =   torch.tensor([75, 75, 63, 65, 17, 17] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				_a ) )
					# verify orig_size
					__UpperCAmelCase      :					str   =   torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				_a ) )
					# verify size
					__UpperCAmelCase      :					int   =   torch.tensor([800, 1_066] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				_a ) )





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

					'''simple docstring'''
					# prepare image, target and masks_path
					__UpperCAmelCase      :					str   =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
					with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,				"""r""" ) as f:
								__UpperCAmelCase      :					int   =   json.loads(f.read() )

					__UpperCAmelCase      :					int   =   {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

					__UpperCAmelCase      :					str   =   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

					# encode them
					__UpperCAmelCase      :					List[str]   =   ConditionalDetrImageProcessor(format="""coco_panoptic""" )
					__UpperCAmelCase      :					Dict   =   image_processing(images=_a       ,				annotations=_a       ,				masks_path=_a       ,				return_tensors="""pt""" )

					# verify pixel values
					__UpperCAmelCase      :					Any   =   torch.Size([1, 3, 800, 1_066] )
					self.assertEqual(encoding["""pixel_values"""].shape       ,				_a )

					__UpperCAmelCase      :					Dict   =   torch.tensor([0.2796, 0.3138, 0.3481] )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				_a       ,				atol=1E-4 ) )

					# verify area
					__UpperCAmelCase      :					int   =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				_a ) )
					# verify boxes
					__UpperCAmelCase      :					List[str]   =   torch.Size([6, 4] )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				_a )
					__UpperCAmelCase      :					List[Any]   =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				_a       ,				atol=1E-3 ) )
					# verify image_id
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([39_769] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				_a ) )
					# verify is_crowd
					__UpperCAmelCase      :					Dict   =   torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				_a ) )
					# verify class_labels
					__UpperCAmelCase      :					int   =   torch.tensor([17, 17, 63, 75, 75, 93] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				_a ) )
					# verify masks
					__UpperCAmelCase      :					Any   =   822_873
					self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,				_a )
					# verify orig_size
					__UpperCAmelCase      :					Optional[Any]   =   torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				_a ) )
					# verify size
					__UpperCAmelCase      :					List[str]   =   torch.tensor([800, 1_066] )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				_a ) )









'''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






