code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  UpperCAmelCase__      =							'''\
  @inproceedings{xu-etal-2016-optimizing,
  title = {Optimizing Statistical Machine Translation for Text Simplification},
  authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
  journal = {Transactions of the Association for Computational Linguistics},
  volume = {4},
  year={2016},
  url = {https://www.aclweb.org/anthology/Q16-1029},
  pages = {401--415},
  }
  '''
  
  UpperCAmelCase__      =							'''\
  SARI is a metric used for evaluating automatic text simplification systems.
  The metric compares the predicted simplified sentences against the reference
  and the source sentences. It explicitly measures the goodness of words that are
  added, deleted and kept by the system.
  Sari = (F1_add + F1_keep + P_del) / 3
  where
  F1_add: n-gram F1 score for add operation
  F1_keep: n-gram F1 score for keep operation
  P_del: n-gram precision score for delete operation
  n = 4, as in the original paper.
  
  This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
  It has two differences with the original GitHub [1] implementation:
    (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
        a target exactly.
    (2) Fixes an alleged bug [2] in the keep score computation.
  [1] https://github.com/cocoxu/simplification/blob/master/SARI.py
      (commit 0210f15)
  [2] https://github.com/cocoxu/simplification/issues/6
  [3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
  '''
  
  
  UpperCAmelCase__      =							'''
  Calculates sari score (between 0 and 100) given a list of source and predicted
  sentences, and a list of lists of reference sentences.
  Args:
      sources: list of source sentences where each sentence should be a string.
      predictions: list of predicted sentences where each sentence should be a string.
      references: list of lists of reference sentences where each sentence should be a string.
  Returns:
      sari: sari score
  Examples:
      >>> sources=["About 95 species are currently accepted ."]
      >>> predictions=["About 95 you now get in ."]
      >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
      >>> sari = datasets.load_metric("sari")
      >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
      >>> print(results)
      {\'sari\': 26.953601953601954}
  '''
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[Any],_SCREAMING_SNAKE_CASE				:   Optional[Any],_SCREAMING_SNAKE_CASE				:   Dict,_SCREAMING_SNAKE_CASE				:   str   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  [rgram for rgrams in rgramslist for rgram in rgrams]
  			__A=  Counter(_SCREAMING_SNAKE_CASE   )
  
  			__A=  Counter(_SCREAMING_SNAKE_CASE   )
  			__A=  Counter()
  			for sgram, scount in sgramcounter.items():
  						__A=  scount * numref
  
  			__A=  Counter(_SCREAMING_SNAKE_CASE   )
  			__A=  Counter()
  			for cgram, ccount in cgramcounter.items():
  						__A=  ccount * numref
  
  			# KEEP
  			__A=  sgramcounter_rep & cgramcounter_rep
  			__A=  keepgramcounter_rep & rgramcounter
  			__A=  sgramcounter_rep & rgramcounter
  
  			__A=  0
  			__A=  0
  			for keepgram in keepgramcountergood_rep:
  						keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
  						# Fix an alleged bug [2] in the keep score computation.
  						# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
  						keeptmpscorea += keepgramcountergood_rep[keepgram]
  			# Define 0/0=1 instead of 0 to give higher scores for predictions that match
  			#      a target exactly.
  			__A=  1
  			__A=  1
  			if len(_SCREAMING_SNAKE_CASE   ) > 0:
  						__A=  keeptmpscorea / len(_SCREAMING_SNAKE_CASE   )
  			if len(_SCREAMING_SNAKE_CASE   ) > 0:
  						# Fix an alleged bug [2] in the keep score computation.
  						# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  						__A=  keeptmpscorea / sum(keepgramcounterall_rep.values()   )
  			__A=  0
  			if keepscore_precision > 0 or keepscore_recall > 0:
  						__A=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
  			# DELETION
  			__A=  sgramcounter_rep - cgramcounter_rep
  			__A=  delgramcounter_rep - rgramcounter
  			__A=  sgramcounter_rep - rgramcounter
  			__A=  0
  			__A=  0
  			for delgram in delgramcountergood_rep:
  						deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
  						deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
  			# Define 0/0=1 instead of 0 to give higher scores for predictions that match
  			# a target exactly.
  			__A=  1
  			if len(_SCREAMING_SNAKE_CASE   ) > 0:
  						__A=  deltmpscorea / len(_SCREAMING_SNAKE_CASE   )
  
  			# ADDITION
  			__A=  set(_SCREAMING_SNAKE_CASE   ) - set(_SCREAMING_SNAKE_CASE   )
  			__A=  set(_SCREAMING_SNAKE_CASE   ) & set(_SCREAMING_SNAKE_CASE   )
  			__A=  set(_SCREAMING_SNAKE_CASE   ) - set(_SCREAMING_SNAKE_CASE   )
  
  			__A=  0
  			for addgram in addgramcountergood:
  						addtmpscore += 1
  
  			# Define 0/0=1 instead of 0 to give higher scores for predictions that match
  			# a target exactly.
  			__A=  1
  			__A=  1
  			if len(_SCREAMING_SNAKE_CASE   ) > 0:
  						__A=  addtmpscore / len(_SCREAMING_SNAKE_CASE   )
  			if len(_SCREAMING_SNAKE_CASE   ) > 0:
  						__A=  addtmpscore / len(_SCREAMING_SNAKE_CASE   )
  			__A=  0
  			if addscore_precision > 0 or addscore_recall > 0:
  						__A=  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
  			return (keepscore, delscore_precision, addscore)
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[Any],_SCREAMING_SNAKE_CASE				:   List[Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  len(_SCREAMING_SNAKE_CASE   )
  
  			__A=  ssent.split(' '   )
  			__A=  csent.split(' '   )
  			__A=  []
  			__A=  []
  			__A=  []
  			__A=  []
  			__A=  []
  			__A=  []
  
  			__A=  []
  			__A=  []
  			__A=  []
  			__A=  []
  			for rsent in rsents:
  						__A=  rsent.split(' '   )
  						__A=  []
  						__A=  []
  						__A=  []
  						ragramslist.append(_SCREAMING_SNAKE_CASE   )
  						for i in range(0,len(_SCREAMING_SNAKE_CASE   ) - 1   ):
  									if i < len(_SCREAMING_SNAKE_CASE   ) - 1:
  												__A=  ragrams[i] + ' ' + ragrams[i + 1]
  												ragrams.append(_SCREAMING_SNAKE_CASE   )
  									if i < len(_SCREAMING_SNAKE_CASE   ) - 2:
  												__A=  ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
  												ragrams.append(_SCREAMING_SNAKE_CASE   )
  									if i < len(_SCREAMING_SNAKE_CASE   ) - 3:
  												__A=  ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
  												ragrams.append(_SCREAMING_SNAKE_CASE   )
  						ragramslist.append(_SCREAMING_SNAKE_CASE   )
  						ragramslist.append(_SCREAMING_SNAKE_CASE   )
  						ragramslist.append(_SCREAMING_SNAKE_CASE   )
  
  			for i in range(0,len(_SCREAMING_SNAKE_CASE   ) - 1   ):
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 1:
  									__A=  sagrams[i] + ' ' + sagrams[i + 1]
  									sagrams.append(_SCREAMING_SNAKE_CASE   )
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 2:
  									__A=  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
  									sagrams.append(_SCREAMING_SNAKE_CASE   )
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 3:
  									__A=  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
  									sagrams.append(_SCREAMING_SNAKE_CASE   )
  
  			for i in range(0,len(_SCREAMING_SNAKE_CASE   ) - 1   ):
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 1:
  									__A=  cagrams[i] + ' ' + cagrams[i + 1]
  									cagrams.append(_SCREAMING_SNAKE_CASE   )
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 2:
  									__A=  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
  									cagrams.append(_SCREAMING_SNAKE_CASE   )
  						if i < len(_SCREAMING_SNAKE_CASE   ) - 3:
  									__A=  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
  									cagrams.append(_SCREAMING_SNAKE_CASE   )
  
  			((__A),  (__A),  (__A))=  SARIngram(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			((__A),  (__A),  (__A))=  SARIngram(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			((__A),  (__A),  (__A))=  SARIngram(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			((__A),  (__A),  (__A))=  SARIngram(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			__A=  sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
  			__A=  sum([delascore, delascore, delascore, delascore]   ) / 4
  			__A=  sum([addascore, addascore, addascore, addascore]   ) / 4
  			__A=  (avgkeepscore + avgdelscore + avgaddscore) / 3
  			return finalscore
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   bool = True,_SCREAMING_SNAKE_CASE				:   str = "13a",_SCREAMING_SNAKE_CASE				:   bool = True   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if lowercase:
  						__A=  sentence.lower()
  
  			if tokenizer in ["13a", "intl"]:
  						if version.parse(sacrebleu.__version__   ).major >= 2:
  									__A=  sacrebleu.metrics.bleu._get_tokenizer(_SCREAMING_SNAKE_CASE   )()(_SCREAMING_SNAKE_CASE   )
  						else:
  									__A=  sacrebleu.TOKENIZERS[tokenizer]()(_SCREAMING_SNAKE_CASE   )
  			elif tokenizer == "moses":
  						__A=  sacremoses.MosesTokenizer().tokenize(_SCREAMING_SNAKE_CASE,return_str=_SCREAMING_SNAKE_CASE,escape=_SCREAMING_SNAKE_CASE   )
  			elif tokenizer == "penn":
  						__A=  sacremoses.MosesTokenizer().penn_tokenize(_SCREAMING_SNAKE_CASE,return_str=_SCREAMING_SNAKE_CASE   )
  			else:
  						__A=  sentence
  
  			if not return_str:
  						__A=  normalized_sent.split()
  
  			return normalized_sent
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,     _KWARGS_DESCRIPTION      )
  class 	a__				(				datasets.Metric      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : str  )			->		Optional[int]:
  						return datasets.MetricInfo(
  						    description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
  						        {
  						            'sources': datasets.Value('string' ,						id='sequence'  ),
  						            'predictions': datasets.Value('string' ,						id='sequence'  ),
  						            'references': datasets.Sequence(datasets.Value('string' ,						id='sequence'  ) ,						id='references'  ),
  						        }  ) ,						codebase_urls=[
  						        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
  						        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
  						    ] ,						reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf'] ,						)
  
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Dict  )			->		Any:
  						if not (len(lowerCAmelCase_  ) == len(lowerCAmelCase_  ) == len(lowerCAmelCase_  )):
  									raise ValueError('Sources length must match predictions and references lengths.'  )
  						__A=  0
  						for src, pred, refs in zip(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									sari_score += SARIsent(normalize(lowerCAmelCase_  ) ,						normalize(lowerCAmelCase_  ) ,						[normalize(lowerCAmelCase_  ) for sent in refs]  )
  						__A=  sari_score / len(lowerCAmelCase_  )
  						return {"sari": 100 * sari_score}
  
  
  
  
style_context: 
  
  
  '''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
  
  
  
  