'''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 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	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       snake_case__						(      self				:							Any	)							->	List[str]:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,		cross_attention_dim=3_2     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			ControlNetModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		in_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		cross_attention_dim=3_2     ,		conditioning_embedding_out_channels=(1_6, 3_2)     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			DDIMScheduler(
								    beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=__a     ,		set_alpha_to_one=__a     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			AutoencoderKL(
								    block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPTextConfig(
								    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
								__UpperCAmelCase							=			CLIPTextModel(__a	)
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							Tuple=0	)							->	List[str]:
								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)

								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			randn_tensor(
								    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,		generator=__a     ,		device=torch.device(__a	)     ,		)
								__UpperCAmelCase							=			floats_tensor(control_image.shape     ,		rng=random.Random(__a	)	).to(__a	)
								__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
								__UpperCAmelCase							=			Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
								__UpperCAmelCase							=			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''control_image''': control_image,
								}

								return inputs





							def       snake_case__						(      self				:							List[Any]	)							->	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       snake_case__						(      self				:							Dict	)							->	Optional[int]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)






							def       snake_case__						(      self				:							str	)							->	Dict:
								self._test_inference_batch_single_identical(expected_max_diff=2e-3	)






class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	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       snake_case__						(      self				:							Tuple	)							->	Union[str, Any]:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,		cross_attention_dim=3_2     ,		)
								torch.manual_seed(0	)

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

								__UpperCAmelCase							=			ControlNetModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		in_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		cross_attention_dim=3_2     ,		conditioning_embedding_out_channels=(1_6, 3_2)     ,		)
								controlneta.controlnet_down_blocks.apply(__a	)

								torch.manual_seed(0	)
								__UpperCAmelCase							=			ControlNetModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		in_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		cross_attention_dim=3_2     ,		conditioning_embedding_out_channels=(1_6, 3_2)     ,		)
								controlneta.controlnet_down_blocks.apply(__a	)

								torch.manual_seed(0	)
								__UpperCAmelCase							=			DDIMScheduler(
								    beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=__a     ,		set_alpha_to_one=__a     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			AutoencoderKL(
								    block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPTextConfig(
								    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
								__UpperCAmelCase							=			CLIPTextModel(__a	)
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								__UpperCAmelCase							=			MultiControlNetModel([controlneta, controlneta]	)

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





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

								__UpperCAmelCase							=			2

								__UpperCAmelCase							=			[
								    randn_tensor(
								        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,		generator=__a     ,		device=torch.device(__a	)     ,		),
								    randn_tensor(
								        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,		generator=__a     ,		device=torch.device(__a	)     ,		),
								]

								__UpperCAmelCase							=			floats_tensor(control_image[0].shape     ,		rng=random.Random(__a	)	).to(__a	)
								__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
								__UpperCAmelCase							=			Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
								__UpperCAmelCase							=			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''control_image''': control_image,
								}

								return inputs





							def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								pipe.to(__a	)

								__UpperCAmelCase							=			1_0.0
								__UpperCAmelCase							=			4

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

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

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

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

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





							def       snake_case__						(      self				:							Optional[Any]	)							->	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       snake_case__						(      self				:							Dict	)							->	List[str]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)





							def       snake_case__						(      self				:							int	)							->	Tuple:
								self._test_inference_batch_single_identical(expected_max_diff=2e-3	)






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






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





							def       snake_case__						(      self				:							Tuple	)							->	int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''	)

								__UpperCAmelCase							=			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	)

								__UpperCAmelCase							=			torch.Generator(device='''cpu'''	).manual_seed(0	)
								__UpperCAmelCase							=			'''evil space-punk bird'''
								__UpperCAmelCase							=			load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''	).resize((5_1_2, 5_1_2)	)
								__UpperCAmelCase							=			load_image(
								    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''	).resize((5_1_2, 5_1_2)	)

								__UpperCAmelCase							=			pipe(
								    __a     ,		__a     ,		control_image=__a     ,		generator=__a     ,		output_type='''np'''     ,		num_inference_steps=5_0     ,		strength=0.6     ,		)

								__UpperCAmelCase							=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								__UpperCAmelCase							=			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'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
