

# 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 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						StableDiffusionControlNetImgaImgPipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			SCREAMING_SNAKE_CASE							   =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
			SCREAMING_SNAKE_CASE							   =						IMAGE_TO_IMAGE_IMAGE_PARAMS





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =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					)
						__a        =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					)
						__a        =DDIMScheduler(
						    beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='scaled_linear'					,  clip_sample=__snake_case					,  set_alpha_to_one=__snake_case					,  )
						torch.manual_seed(0					)
						__a        =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					)
						__a        =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					,  )
						__a        =CLIPTextModel(__snake_case					)
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						__a        ={
						    '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					,  __snake_case					,  __snake_case=0					)   -> Tuple:




						'''simple docstring'''
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)

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




						'''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 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						StableDiffusionControlNetImgaImgPipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			SCREAMING_SNAKE_CASE							   =						frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =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(__snake_case					):
									if isinstance(__snake_case					,  torch.nn.Convad					):
												torch.nn.init.normal(m.weight					)
												m.bias.data.fill_(1.0					)

						__a        =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(__snake_case					)

						torch.manual_seed(0					)
						__a        =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(__snake_case					)

						torch.manual_seed(0					)
						__a        =DDIMScheduler(
						    beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='scaled_linear'					,  clip_sample=__snake_case					,  set_alpha_to_one=__snake_case					,  )
						torch.manual_seed(0					)
						__a        =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					)
						__a        =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					,  )
						__a        =CLIPTextModel(__snake_case					)
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						__a        =MultiControlNetModel([controlneta, controlneta]					)

						__a        ={
						    '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					,  __snake_case					,  __snake_case=0					)   -> Optional[int]:




						'''simple docstring'''
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)

						__a        =2

						__a        =[
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,  generator=__snake_case					,  device=torch.device(__snake_case					)					,  ),
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,  generator=__snake_case					,  device=torch.device(__snake_case					)					,  ),
						]

						__a        =floats_tensor(control_image[0].shape					,  rng=random.Random(__snake_case					)					).to(__snake_case					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					)[0]
						__a        =Image.fromarray(np.uinta(__snake_case					)					).convert('RGB'					).resize((64, 64)					)
						__a        ={
						    '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					)   -> Optional[int]:




						'''simple docstring'''
						__a        =self.get_dummy_components()
						__a        =self.pipeline_class(**__snake_case					)
						pipe.to(__snake_case					)

						__a        =10.0
						__a        =4

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

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

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

						__a        =self.get_dummy_inputs(__snake_case					)
						__a        =steps
						__a        =scale
						__a        =pipe(**__snake_case					,  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					)   -> 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					)   -> Dict:




						'''simple docstring'''
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





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




						'''simple docstring'''
						__a        =self.get_dummy_components()
						__a        =self.pipeline_class(**__snake_case					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)
						with tempfile.TemporaryDirectory() as tmpdir:
									try:
												# save_pretrained is not implemented for Multi-ControlNet
												pipe.save_pretrained(__snake_case					)
									except NotImplementedError:
												pass






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





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




						'''simple docstring'''
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'					)

						__a        =StableDiffusionControlNetImgaImgPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5'					,  safety_checker=__snake_case					,  controlnet=__snake_case					)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__snake_case					)

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

						__a        =pipe(
						    __snake_case					,  __snake_case					,  control_image=__snake_case					,  generator=__snake_case					,  output_type='np'					,  num_inference_steps=50					,  strength=0.6					,  )

						__a        =output.images[0]

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

						__a        =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 manim import *






class 	__magic_name__    (			lowerCAmelCase_      ):





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =Rectangle(height=0.5					,  width=0.5					)
						__a        =Rectangle(height=0.25					,  width=0.25					)
						__a        =Rectangle(height=0.46					,  width=0.46					).set_stroke(width=0					)

						__a        =[mem.copy() for i in range(6					)]
						__a        =[mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('CPU'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						cpu.move_to([-2.5, -0.5, 0]					)
						self.add(__snake_case					)

						__a        =[mem.copy() for i in range(4					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('GPU'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						gpu.move_to([-1, -1, 0]					)
						self.add(__snake_case					)

						__a        =[mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)

						__a        =Text('Model'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						model.move_to([3, -1.0, 0]					)
						self.add(__snake_case					)

						__a        =[]
						__a        =[]
						__a        =[]

						for i, rect in enumerate(__snake_case					):
									rect.set_stroke(__snake_case					)

									__a        =Rectangle(height=0.46 / 4					,  width=0.46 / 3					).set_stroke(width=0.0					).set_fill(__snake_case					,  opacity=0.7					)

									if i == 0:
												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)					,  buff=0.02					,  direction=__snake_case					)
												cpu_target.set_x(cpu_target.get_x() + 0.1					)
									elif i == 3:
												cpu_target.next_to(model_cpu_arr[0]					,  direction=__snake_case					,  buff=0.0					)
									else:
												cpu_target.next_to(model_cpu_arr[i - 1]					,  direction=__snake_case					,  buff=0.0					)
									self.add(__snake_case					)
									model_cpu_arr.append(__snake_case					)

						self.add(*__snake_case					,  *__snake_case					,  *__snake_case					)

						__a        =[mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)

						__a        =Text('Loaded Checkpoint'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						checkpoint.move_to([3, 0.5, 0]					)

						self.add(__snake_case					)

						__a        =[]
						__a        =[]

						for i, rect in enumerate(__snake_case					):
									__a        =fill.copy().set_fill(__snake_case					,  opacity=0.7					)
									target.move_to(__snake_case					)
									ckpt_arr.append(__snake_case					)

									__a        =target.copy()
									if i < 5:
												cpu_target.move_to(cpu_left_col_base[i + 1]					)
									else:
												cpu_target.move_to(cpu_right_col_base[i - 5]					)
									ckpt_cpu_arr.append(__snake_case					)
						self.add(*__snake_case					,  *__snake_case					)

						__a        =Square(side_length=2.2					)
						key.move_to([-5, 2, 0]					)

						__a        =MarkupText(
						    f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'					,  font_size=18					,  )

						key_text.move_to([-5, 2.4, 0]					)

						self.add(__snake_case					,  __snake_case					)

						__a        =MarkupText(
						    f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'					,  font_size=18					,  )

						blue_text.next_to(__snake_case					,  DOWN * 2.4					,  aligned_edge=key_text.get_left()					)
						self.add(__snake_case					)

						__a        =MarkupText(
						    f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'					,  font_size=24					,  )
						step_a.move_to([2, 2, 0]					)

						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('Disk'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						disk.move_to([-4.0, -1.25, 0]					)
						self.play(Write(__snake_case					,  run_time=3					)					,  Write(__snake_case					,  run_time=1					)					,  Create(__snake_case					,  run_time=1					)					)

						__a        =[]
						for i, rect in enumerate(__snake_case					):
									__a        =rect.copy()
									target.generate_target()
									target.target.move_to(disk_left_col_base[i]					).scale(0.5					)
									animations.append(MoveToTarget(__snake_case					,  run_time=1.5					)					)
						self.play(*__snake_case					)

						self.play(FadeOut(__snake_case					)					)

						__a        =MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'					,  font_size=24					)
						step_a.move_to([2, 2, 0]					)

						self.play(Write(__snake_case					,  run_time=3					)					)

						self.play(
						    FadeOut(__snake_case					,  __snake_case					,  *__snake_case					,  *__snake_case					)					,  )

						self.wait()






