



'''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 		lowerCamelCase__      (						A	,   A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



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



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		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					)
						_lowercase					:       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)     ,  )
						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		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					)
						_lowercase					:       Tuple       =		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					)
						_lowercase					:       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     ,  )
						_lowercase					:       Any       =		CLIPTextModel(UpperCamelCase_					)
						_lowercase					:       int       =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						_lowercase					:       List[Any]       =		{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	List[Any]=0					)  ->       Dict:



						'''simple docstring'''





						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       Any       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       List[str]       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

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



						'''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 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Dict:



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Any:



						'''simple docstring'''





						self._test_inference_batch_single_identical(expected_max_diff=2E-3					)






class 		lowerCamelCase__      (						A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					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 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Any:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Dict       =		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_				:	Optional[int]					):
							if isinstance(UpperCamelCase_     ,  torch.nn.Convad					):
								torch.nn.init.normal(m.weight					)
								m.bias.data.fill_(1.0					)

						_lowercase					:       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(UpperCamelCase_					)

						torch.manual_seed(0					)
						_lowercase					:       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(UpperCamelCase_					)

						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		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					)
						_lowercase					:       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					)
						_lowercase					:       Optional[int]       =		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     ,  )
						_lowercase					:       Optional[int]       =		CLIPTextModel(UpperCamelCase_					)
						_lowercase					:       Tuple       =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						_lowercase					:       Tuple       =		MultiControlNetModel([controlneta, controlneta]					)

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




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Tuple=0					)  ->       int:



						'''simple docstring'''





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

						_lowercase					:       List[Any]       =		2

						_lowercase					:       Optional[Any]       =		[
						    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_					)     ,  ),
						]

						_lowercase					:       int       =		floats_tensor(control_image[0].shape     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		image.cpu().permute(0     ,  2     ,  3     ,  1					)[0]
						_lowercase					:       Optional[int]       =		Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('RGB'					).resize((64, 64)					)
						_lowercase					:       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 						__UpperCAmelCase     (  self				:	Tuple					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.get_dummy_components()
						_lowercase					:       List[str]       =		self.pipeline_class(**UpperCamelCase_					)
						pipe.to(UpperCamelCase_					)

						_lowercase					:       Tuple       =		10.0
						_lowercase					:       List[Any]       =		4

						_lowercase					:       Any       =		self.get_dummy_inputs(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		steps
						_lowercase					:       Optional[int]       =		scale
						_lowercase					:       Optional[int]       =		pipe(**UpperCamelCase_					)[0]

						_lowercase					:       Dict       =		self.get_dummy_inputs(UpperCamelCase_					)
						_lowercase					:       Tuple       =		steps
						_lowercase					:       Any       =		scale
						_lowercase					:       List[Any]       =		pipe(**UpperCamelCase_     ,  control_guidance_start=0.1     ,  control_guidance_end=0.2					)[0]

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

						_lowercase					:       Optional[Any]       =		self.get_dummy_inputs(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		steps
						_lowercase					:       List[Any]       =		scale
						_lowercase					:       Optional[Any]       =		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 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       List[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 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       List[str]:



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						self._test_inference_batch_single_identical(expected_max_diff=2E-3					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       int       =		self.get_dummy_components()
						_lowercase					:       Any       =		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 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'					)

						_lowercase					:       List[str]       =		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_					)

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

						_lowercase					:       str       =		pipe(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  control_image=UpperCamelCase_     ,  generator=UpperCamelCase_     ,  output_type='np'     ,  num_inference_steps=50     ,  strength=0.6     ,  )

						_lowercase					:       Optional[int]       =		output.images[0]

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

						_lowercase					:       Tuple       =		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 __future__ import annotations

import requests







def 						__UpperCamelCase				(							_lowercase					) ->			dict:
	_lowercase					:       Optional[int]       =		f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
	return requests.get(_lowercase					).json()







def 						__UpperCamelCase				(							_lowercase = 10					) ->			list[dict]:
	_lowercase					:       Union[str, Any]       =		'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
	_lowercase					:       Optional[Any]       =		requests.get(_lowercase					).json()[:max_stories]
	return [get_hackernews_story(_lowercase					) for story_id in story_ids]







def 						__UpperCamelCase				(							_lowercase = 10					) ->			str:
	_lowercase					:       Tuple       =		hackernews_top_stories(_lowercase					)
	return "\n".join('* [{title}]({url})'.format(**_lowercase					) for story in stories					)


if __name__ == "__main__":
		print(hackernews_top_stories_as_markdown())
