






# 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			(		lowercase_     ,      lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Dict      =  StableDiffusionControlNetImgaImgPipeline
				_lowercase				:   Union[str, Any]      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
				_lowercase				:   Tuple      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase				:   Dict      =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}      )
				_lowercase				:   Optional[int]      =  IMAGE_TO_IMAGE_IMAGE_PARAMS



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       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)
											snake_case__	  =       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)
											snake_case__	  =       DDIMScheduler(
											    beta_start=0.0_00_85			,		beta_end=0.0_12			,		beta_schedule="""scaled_linear"""			,		clip_sample=UpperCamelCase__			,		set_alpha_to_one=UpperCamelCase__			,		)
											torch.manual_seed(0)
											snake_case__	  =       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)
											snake_case__	  =       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			,		)
											snake_case__	  =       CLIPTextModel(UpperCamelCase__)
											snake_case__	  =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

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



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Any			,		UpperCamelCase__    :    Dict=0):






											'''simple docstring'''







											if str(UpperCamelCase__).startswith("""mps"""):
																		snake_case__	  =       torch.manual_seed(UpperCamelCase__)
											else:
																		snake_case__	  =       torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)

											snake_case__	  =       2
											snake_case__	  =       randn_tensor(
											    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)			,		generator=UpperCamelCase__			,		device=torch.device(UpperCamelCase__)			,		)
											snake_case__	  =       floats_tensor(control_image.shape			,		rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
											snake_case__	  =       image.cpu().permute(0			,		2			,		3			,		1)[0]
											snake_case__	  =       Image.fromarray(np.uinta(UpperCamelCase__)).convert("""RGB""").resize((6_4, 6_4))
											snake_case__	  =       {
											    """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 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											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 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											self._test_inference_batch_single_identical(expected_max_diff=2E-3)


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   int      =  StableDiffusionControlNetImgaImgPipeline
				_lowercase				:   int      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
				_lowercase				:   Optional[int]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase				:   Optional[Any]      =  frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       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(UpperCamelCase__    :    List[Any]):
																		if isinstance(UpperCamelCase__			,		torch.nn.Convad):
																									torch.nn.init.normal(m.weight)
																									m.bias.data.fill_(1.0)

											snake_case__	  =       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(UpperCamelCase__)

											torch.manual_seed(0)
											snake_case__	  =       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(UpperCamelCase__)

											torch.manual_seed(0)
											snake_case__	  =       DDIMScheduler(
											    beta_start=0.0_00_85			,		beta_end=0.0_12			,		beta_schedule="""scaled_linear"""			,		clip_sample=UpperCamelCase__			,		set_alpha_to_one=UpperCamelCase__			,		)
											torch.manual_seed(0)
											snake_case__	  =       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)
											snake_case__	  =       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			,		)
											snake_case__	  =       CLIPTextModel(UpperCamelCase__)
											snake_case__	  =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

											snake_case__	  =       MultiControlNetModel([controlneta, controlneta])

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



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    Union[str, Any]=0):






											'''simple docstring'''







											if str(UpperCamelCase__).startswith("""mps"""):
																		snake_case__	  =       torch.manual_seed(UpperCamelCase__)
											else:
																		snake_case__	  =       torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)

											snake_case__	  =       2

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

											snake_case__	  =       floats_tensor(control_image[0].shape			,		rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
											snake_case__	  =       image.cpu().permute(0			,		2			,		3			,		1)[0]
											snake_case__	  =       Image.fromarray(np.uinta(UpperCamelCase__)).convert("""RGB""").resize((6_4, 6_4))
											snake_case__	  =       {
											    """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 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       self.get_dummy_components()
											snake_case__	  =       self.pipeline_class(**UpperCamelCase__)
											pipe.to(UpperCamelCase__)

											snake_case__	  =       10.0
											snake_case__	  =       4

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)
											snake_case__	  =       steps
											snake_case__	  =       scale
											snake_case__	  =       pipe(**UpperCamelCase__)[0]

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

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

											snake_case__	  =       self.get_dummy_inputs(UpperCamelCase__)
											snake_case__	  =       steps
											snake_case__	  =       scale
											snake_case__	  =       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 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											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 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											self._test_inference_batch_single_identical(expected_max_diff=2E-3)



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       self.get_dummy_components()
											snake_case__	  =       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    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""")

											snake_case__	  =       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__)

											snake_case__	  =       torch.Generator(device="""cpu""").manual_seed(0)
											snake_case__	  =       """evil space-punk bird"""
											snake_case__	  =       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))
											snake_case__	  =       load_image(
											    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""").resize((5_1_2, 5_1_2))

											snake_case__	  =       pipe(
											    UpperCamelCase__			,		UpperCamelCase__			,		control_image=UpperCamelCase__			,		generator=UpperCamelCase__			,		output_type="""np"""			,		num_inference_steps=5_0			,		strength=0.6			,		)

											snake_case__	  =       output.images[0]

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

											snake_case__	  =       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








def _UpperCAmelCase   (	a						:	int = 400_0000 ):
							snake_case__	  =       [0, 1]
							snake_case__	  =       0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1] )
														if fib[i + 2] > n:
																					break
														i += 1
							snake_case__	  =       0
							for j in range(len(a ) - 1 ):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


if __name__ == "__main__":
	print(F'''{solution() = }''')
