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

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()





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


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    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						)
												SCREAMING_SNAKE_CASE							:    int							=							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						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							DDIMScheduler(
												    beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='''scaled_linear'''    ,		clip_sample=UpperCamelCase__    ,		set_alpha_to_one=UpperCamelCase__    ,		)
												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    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						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							CLIPTextModel(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							{
												    '''unet''': unet,
												    '''controlnet''': controlnet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				int=0						):





												'''simple docstring'''





												if str(UpperCamelCase__						).startswith('''mps'''						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.manual_seed(UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    int							=							torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							2
												SCREAMING_SNAKE_CASE							:    Any							=							randn_tensor(
												    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,		generator=UpperCamelCase__    ,		device=torch.device(UpperCamelCase__						)    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							floats_tensor(control_image.shape    ,		rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							image.cpu().permute(0    ,		2    ,		3    ,		1						)[0]
												SCREAMING_SNAKE_CASE							:    Any							=							Image.fromarray(np.uinta(UpperCamelCase__						)						).convert('''RGB'''						).resize((64, 64)						)
												SCREAMING_SNAKE_CASE							:    str							=							{
												    '''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        __A       (       self	:				Dict						):





												'''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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)



						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												self._test_inference_batch_single_identical(expected_max_diff=2E-3						)






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


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							UNetaDConditionModel(
												    block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,		cross_attention_dim=32    ,		)
												torch.manual_seed(0						)

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

												SCREAMING_SNAKE_CASE							:    Tuple							=							ControlNetModel(
												    block_out_channels=(32, 64)    ,		layers_per_block=2    ,		in_channels=4    ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,		cross_attention_dim=32    ,		conditioning_embedding_out_channels=(16, 32)    ,		)
												controlneta.controlnet_down_blocks.apply(UpperCamelCase__						)

												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							ControlNetModel(
												    block_out_channels=(32, 64)    ,		layers_per_block=2    ,		in_channels=4    ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,		cross_attention_dim=32    ,		conditioning_embedding_out_channels=(16, 32)    ,		)
												controlneta.controlnet_down_blocks.apply(UpperCamelCase__						)

												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							DDIMScheduler(
												    beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='''scaled_linear'''    ,		clip_sample=UpperCamelCase__    ,		set_alpha_to_one=UpperCamelCase__    ,		)
												torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    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						)
												SCREAMING_SNAKE_CASE							:    Any							=							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    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							CLIPTextModel(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							MultiControlNetModel([controlneta, controlneta]						)

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


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





												'''simple docstring'''





												if str(UpperCamelCase__						).startswith('''mps'''						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							torch.manual_seed(UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Dict							=							torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							2

												SCREAMING_SNAKE_CASE							:    int							=							[
												    randn_tensor(
												        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,		generator=UpperCamelCase__    ,		device=torch.device(UpperCamelCase__						)    ,		),
												    randn_tensor(
												        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,		generator=UpperCamelCase__    ,		device=torch.device(UpperCamelCase__						)    ,		),
												]

												SCREAMING_SNAKE_CASE							:    Any							=							floats_tensor(control_image[0].shape    ,		rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							image.cpu().permute(0    ,		2    ,		3    ,		1						)[0]
												SCREAMING_SNAKE_CASE							:    List[str]							=							Image.fromarray(np.uinta(UpperCamelCase__						)						).convert('''RGB'''						).resize((64, 64)						)
												SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.pipeline_class(**UpperCamelCase__						)
												pipe.to(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							10.0
												SCREAMING_SNAKE_CASE							:    Dict							=							4

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							steps
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scale
												SCREAMING_SNAKE_CASE							:    Dict							=							pipe(**UpperCamelCase__						)[0]

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

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

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_dummy_inputs(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							steps
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							scale
												SCREAMING_SNAKE_CASE							:    str							=							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        __A       (       self	:				str						):





												'''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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												self._test_inference_batch_single_identical(expected_max_diff=2E-3						)



						def        __A       (       self	:				Any						):





												'''simple docstring'''





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






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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





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



						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							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__						)

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

												SCREAMING_SNAKE_CASE							:    Any							=							pipe(
												    UpperCamelCase__    ,		UpperCamelCase__    ,		control_image=UpperCamelCase__    ,		generator=UpperCamelCase__    ,		output_type='''np'''    ,		num_inference_steps=50    ,		strength=0.6    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							output.images[0]

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

												SCREAMING_SNAKE_CASE							:    Any							=							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






from math import ceil
def       A	(    _lowercase = 1_001						):
						SCREAMING_SNAKE_CASE							:    Any							=							1

						for i in range(1		,  int(ceil(n / 2.0						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							2 * i + 1
												SCREAMING_SNAKE_CASE							:    int							=							2 * i
												SCREAMING_SNAKE_CASE							:    List[str]							=							total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															__UpperCamelCase		:       Dict									=     int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')





