


'''simple docstring'''





import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


UpperCAmelCase__      =							'''1.5.0'''

UpperCAmelCase__      =							['''vision-encoder-decoder''']






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple   )     ->   int:






			"""simple docstring"""




			if is_optimum_available():
						from optimum.version import __version__ as optimum_version

						__A=  version.parse(_SCREAMING_SNAKE_CASE   )
						if parsed_optimum_version < version.parse(_SCREAMING_SNAKE_CASE   ):
									raise RuntimeError(
									    f"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
									    'can upgrade optimum by running: pip install -U optimum[exporters]'   )
			else:
						raise RuntimeError(
						    'transformers.onnx requires optimum to run, you can install the library by running: pip install '
						    'optimum[exporters]'   )
			__A=  [
			    sys.executable,
			    '-m',
			    'optimum.exporters.onnx',
			    f"""--model {args.model}""",
			    f"""--task {args.feature}""",
			    f"""--framework {args.framework}""" if args.framework is not None else '',
			    f"""{args.output}""",
			]
			__A=  subprocess.Popen(' '.join(_SCREAMING_SNAKE_CASE   ),stdout=subprocess.PIPE,shell=_SCREAMING_SNAKE_CASE   )
			proc.wait()

			logger.info(
			    'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
			    'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
			    'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'   )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str   )     ->   Optional[Any]:






			"""simple docstring"""




			__A=  args.output if args.output.is_file() else args.output.joinpath('model.onnx'   )
			if not args.output.parent.exists():
						args.output.parent.mkdir(parents=_SCREAMING_SNAKE_CASE   )

			# Allocate the model
			__A=  FeaturesManager.get_model_from_feature(
			    args.feature,args.model,framework=args.framework,cache_dir=args.cache_dir   )

			__A,  __A=  FeaturesManager.check_supported_model_or_raise(_SCREAMING_SNAKE_CASE,feature=args.feature   )
			__A=  model_onnx_config(model.config   )

			if model_kind in ENCODER_DECODER_MODELS:
						__A=  model.get_encoder()
						__A=  model.get_decoder()

						__A=  onnx_config.get_encoder_config(encoder_model.config   )
						__A=  onnx_config.get_decoder_config(
						    encoder_model.config,decoder_model.config,feature=args.feature   )

						if args.opset is None:
									__A=  max(encoder_onnx_config.default_onnx_opset,decoder_onnx_config.default_onnx_opset   )

						if args.opset < min(encoder_onnx_config.default_onnx_opset,decoder_onnx_config.default_onnx_opset   ):
									raise ValueError(
									    f"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
									    f""" {min(encoder_onnx_config.default_onnx_opset,decoder_onnx_config.default_onnx_opset   )} is required."""   )

						__A=  AutoFeatureExtractor.from_pretrained(args.model   )

						__A,  __A=  export(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.opset,args.output.parent.joinpath('encoder_model.onnx'   ),)

						validate_model_outputs(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.output.parent.joinpath('encoder_model.onnx'   ),_SCREAMING_SNAKE_CASE,args.atol if args.atol else encoder_onnx_config.atol_for_validation,)

						__A=  AutoTokenizer.from_pretrained(args.model   )

						__A,  __A=  export(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.opset,args.output.parent.joinpath('decoder_model.onnx'   ),)

						validate_model_outputs(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.output.parent.joinpath('decoder_model.onnx'   ),_SCREAMING_SNAKE_CASE,args.atol if args.atol else decoder_onnx_config.atol_for_validation,)
						logger.info(
						    f"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"   ).as_posix()},"""
						    f""" {args.output.parent.joinpath("decoder_model.onnx"   ).as_posix()}"""   )

			else:
						# Instantiate the appropriate preprocessor
						if args.preprocessor == "auto":
									__A=  get_preprocessor(args.model   )
						elif args.preprocessor == "tokenizer":
									__A=  AutoTokenizer.from_pretrained(args.model   )
						elif args.preprocessor == "image_processor":
									__A=  AutoImageProcessor.from_pretrained(args.model   )
						elif args.preprocessor == "feature_extractor":
									__A=  AutoFeatureExtractor.from_pretrained(args.model   )
						elif args.preprocessor == "processor":
									__A=  AutoProcessor.from_pretrained(args.model   )
						else:
									raise ValueError(f"""Unknown preprocessor type '{args.preprocessor}'"""   )

						# Ensure the requested opset is sufficient
						if args.opset is None:
									__A=  onnx_config.default_onnx_opset

						if args.opset < onnx_config.default_onnx_opset:
									raise ValueError(
									    f"""Opset {args.opset} is not sufficient to export {model_kind}. """
									    f"""At least  {onnx_config.default_onnx_opset} is required."""   )

						__A,  __A=  export(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.opset,args.output,)

						if args.atol is None:
									__A=  onnx_config.atol_for_validation

						validate_model_outputs(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,args.output,_SCREAMING_SNAKE_CASE,args.atol   )
						logger.info(f"""All good, model saved at: {args.output.as_posix()}"""   )
						warnings.warn(
						    'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
						    ' using optimum.exporters.onnx in future. You can find more information here:'
						    ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.',_SCREAMING_SNAKE_CASE,)






def 	UpperCAmelCase__(							)     ->   Optional[int]:






			"""simple docstring"""




			__A=  ArgumentParser('Hugging Face Transformers ONNX exporter'   )
			parser.add_argument(
			    '-m','--model',type=_SCREAMING_SNAKE_CASE,required=_SCREAMING_SNAKE_CASE,help='Model ID on huggingface.co or path on disk to load model from.'   )
			parser.add_argument(
			    '--feature',default='default',help='The type of features to export the model with.',)
			parser.add_argument('--opset',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='ONNX opset version to export the model with.'   )
			parser.add_argument(
			    '--atol',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='Absolute difference tolerance when validating the model.'   )
			parser.add_argument(
			    '--framework',type=_SCREAMING_SNAKE_CASE,choices=['pt', 'tf'],default=_SCREAMING_SNAKE_CASE,help=(
			        'The framework to use for the ONNX export.'
			        ' If not provided, will attempt to use the local checkpoint\'s original framework'
			        ' or what is available in the environment.'
			    ),)
			parser.add_argument('output',type=_SCREAMING_SNAKE_CASE,help='Path indicating where to store generated ONNX model.'   )
			parser.add_argument('--cache_dir',type=_SCREAMING_SNAKE_CASE,default=_SCREAMING_SNAKE_CASE,help='Path indicating where to store cache.'   )
			parser.add_argument(
			    '--preprocessor',type=_SCREAMING_SNAKE_CASE,choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor'],default='auto',help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.',)
			parser.add_argument(
			    '--export_with_transformers',action='store_true',help=(
			        'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
			        'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
			        'recommended.'
			    ),)

			__A=  parser.parse_args()
			if args.export_with_transformers or not is_optimum_available():
						export_with_transformers(_SCREAMING_SNAKE_CASE   )
			else:
						export_with_optimum(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
					UpperCAmelCase__      =							logging.get_logger('''transformers.onnx''')  # pylint: disable=invalid-name
					logger.setLevel(logging.INFO)
					main()







'''simple docstring'''





# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 	a__				(				a_			,     a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Optional[int]							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							List[Any]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							Any							= frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
			A	:							List[Any]							= TEXT_TO_IMAGE_IMAGE_PARAMS
			def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
						torch.manual_seed(0  )
						__A=  UNetaDConditionModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						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_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						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=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__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 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : int=0  )			->		str:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2
						__A=  randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,						generator=lowerCAmelCase_ ,						device=torch.device(lowerCAmelCase_  ) ,						)
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).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,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Optional[int]:
						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 				lowerCAmelCase    (     self       : Optional[int]  )			->		str:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )

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


class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= StableDiffusionControlNetInpaintPipeline
			A	:							List[str]							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Tuple							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A	:							int							= frozenset([]      )
			def 				lowerCAmelCase    (     self       : int  )			->		List[Any]:
						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_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						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=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__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


class 	a__				(				a_			,     a_			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							Tuple							= StableDiffusionControlNetInpaintPipeline
			A	:							int							= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A	:							Optional[int]							= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
						torch.manual_seed(0  )
						__A=  UNetaDConditionModel(
						    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=9 ,						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_       : Optional[int]  ):
									if isinstance(lowerCAmelCase_ ,						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(lowerCAmelCase_  )

						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(lowerCAmelCase_  )

						torch.manual_seed(0  )
						__A=  DDIMScheduler(
						    beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear' ,						clip_sample=lowerCAmelCase_ ,						set_alpha_to_one=lowerCAmelCase_ ,						)
						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=1_000 ,						)
						__A=  CLIPTextModel(lowerCAmelCase_  )
						__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 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[Any]=0  )			->		List[Any]:
						if str(lowerCAmelCase_  ).startswith('mps'  ):
									__A=  torch.manual_seed(lowerCAmelCase_  )
						else:
									__A=  torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )

						__A=  2

						__A=  [
						    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_  ) ,						),
						]
						__A=  floats_tensor((1, 3, 32, 32) ,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
						__A=  init_image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]

						__A=  Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  ).resize((64, 64)  )
						__A=  Image.fromarray(np.uinta(init_image + 4  )  ).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,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
						__A=  self.get_dummy_components()
						__A=  self.pipeline_class(**lowerCAmelCase_  )
						pipe.to(lowerCAmelCase_  )

						__A=  10.0
						__A=  4

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  pipe(**lowerCAmelCase_  )[0]

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

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

						__A=  self.get_dummy_inputs(lowerCAmelCase_  )
						__A=  steps
						__A=  scale
						__A=  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 				lowerCAmelCase    (     self       : 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 				lowerCAmelCase    (     self       : Any  )			->		int:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )
			def 				lowerCAmelCase    (     self       : Dict  )			->		Dict:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3  )

			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Optional[int]:
						__A=  self.get_dummy_components()
						__A=  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 	a__				(				unittest.TestCase      ):





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(0  )
						__A=  load_image(
						    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
						    '/stable_diffusion_inpaint/input_bench_mask.png'  ).resize((512, 512)  )

						__A=  'pitch black hole'

						__A=  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'  ).resize((512, 512)  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						output_type='np' ,						num_inference_steps=3 ,						)

						__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/inpaint.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2

			def 				lowerCAmelCase    (     self       : str  )			->		Tuple:
						__A=  ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )

						__A=  StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-v1-5' ,						safety_checker=lowerCAmelCase_ ,						controlnet=lowerCAmelCase_  )
						__A=  DDIMScheduler.from_config(pipe.scheduler.config  )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

						__A=  torch.Generator(device='cpu'  ).manual_seed(33  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
						__A=  init_image.resize((512, 512)  )

						__A=  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'  )
						__A=  mask_image.resize((512, 512)  )

						__A=  'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(lowerCAmelCase_       : int ,						lowerCAmelCase_       : str  ):
									__A=  np.array(image.convert('RGB'  )  ).astype(np.floataa  ) / 2_55.0
									__A=  np.array(image_mask.convert('L'  )  ).astype(np.floataa  ) / 2_55.0

									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
									__A=  -1.0  # set as masked pixel
									__A=  np.expand_dims(lowerCAmelCase_ ,						0  ).transpose(0 ,						3 ,						1 ,						2  )
									__A=  torch.from_numpy(lowerCAmelCase_  )
									return image

						__A=  make_inpaint_condition(lowerCAmelCase_ ,						lowerCAmelCase_  )

						__A=  pipe(
						    lowerCAmelCase_ ,						image=lowerCAmelCase_ ,						mask_image=lowerCAmelCase_ ,						control_image=lowerCAmelCase_ ,						guidance_scale=9.0 ,						eta=1.0 ,						generator=lowerCAmelCase_ ,						num_inference_steps=20 ,						output_type='np' ,						)
						__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/boy_ray_ban.npy'  )

						assert np.abs(expected_image - image  ).max() < 9E-2



