






"""simple docstring"""
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     UpperCamelCase_ (__A       ,		__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       StableDiffusionControlNetImgaImgPipeline
	__magic_name__			   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
	__magic_name__			   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__magic_name__			   =       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}  )
	__magic_name__			   =       IMAGE_TO_IMAGE_IMAGE_PARAMS





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				List[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		)
				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)			,			)
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Optional[Any]      =      DDIMScheduler(
				    beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			beta_schedule="scaled_linear"			,			clip_sample=lowerCAmelCase_			,			set_alpha_to_one=lowerCAmelCase_			,			)
				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_  :				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=1_000			,			)
				UpperCAmelCase_  :				Tuple      =      CLIPTextModel(lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				UpperCAmelCase_  :				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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Tuple=0		)		->    Dict:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				Any      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Any      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      2
				UpperCAmelCase_  :				int      =      randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,			generator=lowerCAmelCase_			,			device=torch.device(lowerCAmelCase_		)			,			)
				UpperCAmelCase_  :				Optional[Any]      =      floats_tensor(control_image.shape			,			rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      image.cpu().permute(0			,			2			,			3			,			1		)[0]
				UpperCAmelCase_  :				Tuple      =      Image.fromarray(np.uinta(lowerCAmelCase_		)		).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   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[Any]:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[int]:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Union[str, Any]:
				self._test_inference_batch_single_identical(expected_max_diff=2e-3		)







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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				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		)

				def init_weights(lowerCAmelCase_      :				str		):
							if isinstance(lowerCAmelCase_			,			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(lowerCAmelCase_		)

				torch.manual_seed(0		)
				UpperCAmelCase_  :				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(lowerCAmelCase_		)

				torch.manual_seed(0		)
				UpperCAmelCase_  :				Union[str, Any]      =      DDIMScheduler(
				    beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			beta_schedule="scaled_linear"			,			clip_sample=lowerCAmelCase_			,			set_alpha_to_one=lowerCAmelCase_			,			)
				torch.manual_seed(0		)
				UpperCAmelCase_  :				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_  :				Optional[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=1_000			,			)
				UpperCAmelCase_  :				Dict      =      CLIPTextModel(lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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

				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   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[Any]=0		)		->    Optional[int]:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				int      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Dict      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      2

				UpperCAmelCase_  :				List[Any]      =      [
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,			generator=lowerCAmelCase_			,			device=torch.device(lowerCAmelCase_		)			,			),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,			generator=lowerCAmelCase_			,			device=torch.device(lowerCAmelCase_		)			,			),
				]

				UpperCAmelCase_  :				Optional[Any]      =      floats_tensor(control_image[0].shape			,			rng=random.Random(lowerCAmelCase_		)		).to(lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      image.cpu().permute(0			,			2			,			3			,			1		)[0]
				UpperCAmelCase_  :				List[str]      =      Image.fromarray(np.uinta(lowerCAmelCase_		)		).convert("RGB"		).resize((64, 64)		)
				UpperCAmelCase_  :				Tuple      =      {
				    "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   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[str]      =      self.get_dummy_components()
				UpperCAmelCase_  :				Optional[Any]      =      self.pipeline_class(**lowerCAmelCase_		)
				pipe.to(lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      10.0
				UpperCAmelCase_  :				Any      =      4

				UpperCAmelCase_  :				str      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      steps
				UpperCAmelCase_  :				Dict      =      scale
				UpperCAmelCase_  :				Any      =      pipe(**lowerCAmelCase_		)[0]

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

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

				UpperCAmelCase_  :				int      =      self.get_dummy_inputs(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      steps
				UpperCAmelCase_  :				Any      =      scale
				UpperCAmelCase_  :				Any      =      pipe(**lowerCAmelCase_			,			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   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				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   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[Any]:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				self._test_inference_batch_single_identical(expected_max_diff=2e-3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				UpperCAmelCase_  :				int      =      self.get_dummy_components()
				UpperCAmelCase_  :				Optional[int]      =      self.pipeline_class(**lowerCAmelCase_		)
				pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
				with tempfile.TemporaryDirectory() as tmpdir:
							try:
										# save_pretrained is not implemented for Multi-ControlNet
										pipe.save_pretrained(lowerCAmelCase_		)
							except NotImplementedError:
										pass







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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[str]      =      ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"		)

				UpperCAmelCase_  :				List[str]      =      StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			safety_checker=lowerCAmelCase_			,			controlnet=lowerCAmelCase_		)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      pipe(
				    lowerCAmelCase_			,			lowerCAmelCase_			,			control_image=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			output_type="np"			,			num_inference_steps=50			,			strength=0.6			,			)

				UpperCAmelCase_  :				Optional[Any]      =      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"""
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowerCamelCase_       			=   argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowerCamelCase_			,	lowerCamelCase_       			=   parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'pip install -r transformers/examples/{example_dir}/requirements.txt'])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



