



'''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 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       StableDiffusionControlNetImgaImgPipeline
	lowerCamelCase__			 =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
	lowerCamelCase__			 =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCamelCase__			 =       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}			)
	lowerCamelCase__			 =       IMAGE_TO_IMAGE_IMAGE_PARAMS







	def      A	(						self			:     Dict		)			->		Dict:
								torch.manual_seed(0		)
								UpperCAmelCase :       List[str] 							=		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		)
								UpperCAmelCase :       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		)
								UpperCAmelCase :       str 							=		DDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			clip_sample=__snake_case		,			set_alpha_to_one=__snake_case		,			)
								torch.manual_seed(0		)
								UpperCAmelCase :       List[Any] 							=		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		)
								UpperCAmelCase :       Tuple 							=		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		,			)
								UpperCAmelCase :       Optional[int] 							=		CLIPTextModel(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase :       Tuple 							=		{
								    '''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]		,			__snake_case			:     str		,			__snake_case			:     List[Any]=0		)			->		Optional[Any]:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase :       List[Any] 							=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase :       Dict 							=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)

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







	def      A	(						self			:     Any		)			->		Tuple:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)

class 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






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







	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								torch.manual_seed(0		)
								UpperCAmelCase :       str 							=		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			:     List[str]		):
															if isinstance(__snake_case		,			torch.nn.Convad		):
																						torch.nn.init.normal(m.weight		)
																						m.bias.data.fill_(1.0		)

								UpperCAmelCase :       Optional[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)		,			)
								controlneta.controlnet_down_blocks.apply(__snake_case		)

								torch.manual_seed(0		)
								UpperCAmelCase :       List[str] 							=		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		)
								UpperCAmelCase :       List[Any] 							=		DDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			clip_sample=__snake_case		,			set_alpha_to_one=__snake_case		,			)
								torch.manual_seed(0		)
								UpperCAmelCase :       List[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		)
								UpperCAmelCase :       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		,			)
								UpperCAmelCase :       Any 							=		CLIPTextModel(__snake_case		)
								UpperCAmelCase :       int 							=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase :       Union[str, Any] 							=		MultiControlNetModel([controlneta, controlneta]		)

								UpperCAmelCase :       Union[str, Any] 							=		{
								    '''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]		,			__snake_case			:     Any		,			__snake_case			:     Optional[int]=0		)			->		Optional[Any]:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase :       int 							=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		2

								UpperCAmelCase :       Optional[int] 							=		[
								    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		)		,			),
								]

								UpperCAmelCase :       int 							=		floats_tensor(control_image[0].shape		,			rng=random.Random(__snake_case		)		).to(__snake_case		)
								UpperCAmelCase :       Dict 							=		image.cpu().permute(0		,			2		,			3		,			1		)[0]
								UpperCAmelCase :       Optional[Any] 							=		Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((64, 64)		)
								UpperCAmelCase :       Optional[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      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       List[str] 							=		self.get_dummy_components()
								UpperCAmelCase :       List[str] 							=		self.pipeline_class(**__snake_case		)
								pipe.to(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		10.0
								UpperCAmelCase :       Any 							=		4

								UpperCAmelCase :       Dict 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       int 							=		steps
								UpperCAmelCase :       List[str] 							=		scale
								UpperCAmelCase :       Union[str, Any] 							=		pipe(**__snake_case		)[0]

								UpperCAmelCase :       List[str] 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       int 							=		steps
								UpperCAmelCase :       List[Any] 							=		scale
								UpperCAmelCase :       str 							=		pipe(**__snake_case		,			control_guidance_start=0.1		,			control_guidance_end=0.2		)[0]

								UpperCAmelCase :       str 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		steps
								UpperCAmelCase :       Union[str, Any] 							=		scale
								UpperCAmelCase :       int 							=		pipe(**__snake_case		,			control_guidance_start=[0.1, 0.3]		,			control_guidance_end=[0.2, 0.7]		)[0]

								UpperCAmelCase :       Tuple 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		steps
								UpperCAmelCase :       Dict 							=		scale
								UpperCAmelCase :       Dict 							=		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      A	(						self			:     int		)			->		Dict:
								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		)			->		int:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







	def      A	(						self			:     Optional[Any]		)			->		str:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







	def      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								UpperCAmelCase :       List[Any] 							=		self.get_dummy_components()
								UpperCAmelCase :       List[str] 							=		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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Optional[int]		)			->		int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def      A	(						self			:     str		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

								UpperCAmelCase :       int 							=		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		)

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

								UpperCAmelCase :       Optional[int] 							=		pipe(
								    __snake_case		,			__snake_case		,			control_image=__snake_case		,			generator=__snake_case		,			output_type='''np'''		,			num_inference_steps=50		,			strength=0.6		,			)

								UpperCAmelCase :       Tuple 							=		output.images[0]

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

								UpperCAmelCase :       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










'''simple docstring'''






from manim import *

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		Rectangle(height=0.5		,			width=0.5		)
								UpperCAmelCase :       str 							=		Rectangle(height=0.46		,			width=0.46		).set_stroke(width=0		)

								UpperCAmelCase :       Tuple 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Optional[int] 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Dict 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Any 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(__snake_case		,			__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       Optional[Any] 							=		Text('''CPU'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		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		)

								UpperCAmelCase :       Optional[Any] 							=		[mem.copy() for i in range(4		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)
								UpperCAmelCase :       List[str] 							=		Text('''GPU'''		,			font_size=24		)
								UpperCAmelCase :       Dict 							=		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		)

								UpperCAmelCase :       int 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*__snake_case		).arrange(__snake_case		,			buff=0		)

								UpperCAmelCase :       List[str] 							=		Text('''Model'''		,			font_size=24		)
								UpperCAmelCase :       Tuple 							=		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		)

								UpperCAmelCase :       Any 							=		[]
								for i, rect in enumerate(__snake_case		):
															rect.set_stroke(__snake_case		)
															# target = fill.copy().set_fill(YELLOW, opacity=0.7)
															# target.move_to(rect)
															# self.add(target)

															UpperCAmelCase :       Dict 							=		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(cpu_targs[0]		,			direction=__snake_case		,			buff=0.0		)
															else:
																						cpu_target.next_to(cpu_targs[i - 1]		,			direction=__snake_case		,			buff=0.0		)
															self.add(__snake_case		)
															cpu_targs.append(__snake_case		)

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

								UpperCAmelCase :       Any 							=		Text('''Loaded Checkpoint'''		,			font_size=24		)
								UpperCAmelCase :       Union[str, Any] 							=		Group(__snake_case		,			__snake_case		).arrange(__snake_case		,			aligned_edge=__snake_case		,			buff=0.4		)
								checkpoint.move_to([3, 0.5, 0]		)

								UpperCAmelCase :       Optional[int] 							=		Square(side_length=2.2		)
								key.move_to([-5, 2, 0]		)

								UpperCAmelCase :       str 							=		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		)

								UpperCAmelCase :       Tuple 							=		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()		)

								UpperCAmelCase :       List[Any] 							=		MarkupText(
								    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""		,			font_size=24		,			)
								step_a.move_to([2, 2, 0]		)
								self.play(Write(__snake_case		)		,			Write(__snake_case		)		)

								self.play(Write(__snake_case		,			run_time=1		)		,			Create(__snake_case		,			run_time=1		)		)

								UpperCAmelCase :       Tuple 							=		[]
								UpperCAmelCase :       int 							=		[]
								for i, rect in enumerate(__snake_case		):
															UpperCAmelCase :       Any 							=		fill.copy().set_fill(__snake_case		,			opacity=0.7		)
															target.move_to(__snake_case		)
															first_animations.append(GrowFromCenter(__snake_case		,			run_time=1		)		)

															UpperCAmelCase :       List[str] 							=		target.copy()
															cpu_target.generate_target()
															if i < 5:
																						cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
															else:
																						cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
															second_animations.append(MoveToTarget(__snake_case		,			run_time=1.5		)		)

								self.play(*__snake_case		)
								self.play(*__snake_case		)
								self.wait()





