
"""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 		_snake_case			(      lowercase__						:    Tuple ,	lowercase__						:    Union[str, Any] ,	lowercase__						:    str ,	lowercase__						:    str						)    ->							int:


			'''simple docstring'''





			lowerCAmelCase_       :List[str]     	=    [rgram for rgrams in rgramslist for rgram in rgrams]
			lowerCAmelCase_       :Any     	=    Counter(UpperCAmelCase__						)

			lowerCAmelCase_       :Optional[Any]     	=    Counter(UpperCAmelCase__						)
			lowerCAmelCase_       :List[Any]     	=    Counter()
			for sgram, scount in sgramcounter.items():
						lowerCAmelCase_       :List[str]     	=    scount * numref

			lowerCAmelCase_       :List[Any]     	=    Counter(UpperCAmelCase__						)
			lowerCAmelCase_       :Tuple     	=    Counter()
			for cgram, ccount in cgramcounter.items():
						lowerCAmelCase_       :List[str]     	=    ccount * numref

			# KEEP
			lowerCAmelCase_       :Tuple     	=    sgramcounter_rep & cgramcounter_rep
			lowerCAmelCase_       :Optional[Any]     	=    keepgramcounter_rep & rgramcounter
			lowerCAmelCase_       :Dict     	=    sgramcounter_rep & rgramcounter

			lowerCAmelCase_       :Optional[int]     	=    0
			lowerCAmelCase_       :Dict     	=    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.
			lowerCAmelCase_       :int     	=    1
			lowerCAmelCase_       :Optional[int]     	=    1
			if len(UpperCAmelCase__						) > 0:
						lowerCAmelCase_       :int     	=    keeptmpscorea / len(UpperCAmelCase__						)
			if len(UpperCAmelCase__						) > 0:
						# Fix an alleged bug [2] in the keep score computation.
						# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
						lowerCAmelCase_       :List[str]     	=    keeptmpscorea / sum(keepgramcounterall_rep.values()						)
			lowerCAmelCase_       :Union[str, Any]     	=    0
			if keepscore_precision > 0 or keepscore_recall > 0:
						lowerCAmelCase_       :int     	=    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

			# DELETION
			lowerCAmelCase_       :List[Any]     	=    sgramcounter_rep - cgramcounter_rep
			lowerCAmelCase_       :int     	=    delgramcounter_rep - rgramcounter
			lowerCAmelCase_       :List[str]     	=    sgramcounter_rep - rgramcounter
			lowerCAmelCase_       :Optional[int]     	=    0
			lowerCAmelCase_       :List[str]     	=    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.
			lowerCAmelCase_       :List[str]     	=    1
			if len(UpperCAmelCase__						) > 0:
						lowerCAmelCase_       :Optional[int]     	=    deltmpscorea / len(UpperCAmelCase__						)

			# ADDITION
			lowerCAmelCase_       :Dict     	=    set(UpperCAmelCase__						) - set(UpperCAmelCase__						)
			lowerCAmelCase_       :List[str]     	=    set(UpperCAmelCase__						) & set(UpperCAmelCase__						)
			lowerCAmelCase_       :Optional[Any]     	=    set(UpperCAmelCase__						) - set(UpperCAmelCase__						)

			lowerCAmelCase_       :Optional[Any]     	=    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.
			lowerCAmelCase_       :List[str]     	=    1
			lowerCAmelCase_       :Optional[Any]     	=    1
			if len(UpperCAmelCase__						) > 0:
						lowerCAmelCase_       :List[str]     	=    addtmpscore / len(UpperCAmelCase__						)
			if len(UpperCAmelCase__						) > 0:
						lowerCAmelCase_       :List[Any]     	=    addtmpscore / len(UpperCAmelCase__						)
			lowerCAmelCase_       :str     	=    0
			if addscore_precision > 0 or addscore_recall > 0:
						lowerCAmelCase_       :List[str]     	=    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)






def 		_snake_case			(      lowercase__						:    Union[str, Any] ,	lowercase__						:    Any ,	lowercase__						:    Tuple						)    ->							Any:


			'''simple docstring'''





			lowerCAmelCase_       :Tuple     	=    len(UpperCAmelCase__						)

			lowerCAmelCase_       :List[str]     	=    ssent.split(""" """						)
			lowerCAmelCase_       :Tuple     	=    csent.split(""" """						)
			lowerCAmelCase_       :List[Any]     	=    []
			lowerCAmelCase_       :Union[str, Any]     	=    []
			lowerCAmelCase_       :int     	=    []
			lowerCAmelCase_       :str     	=    []
			lowerCAmelCase_       :int     	=    []
			lowerCAmelCase_       :List[str]     	=    []

			lowerCAmelCase_       :Any     	=    []
			lowerCAmelCase_       :Union[str, Any]     	=    []
			lowerCAmelCase_       :List[Any]     	=    []
			lowerCAmelCase_       :Optional[int]     	=    []
			for rsent in rsents:
						lowerCAmelCase_       :Dict     	=    rsent.split(""" """						)
						lowerCAmelCase_       :Any     	=    []
						lowerCAmelCase_       :Union[str, Any]     	=    []
						lowerCAmelCase_       :Any     	=    []
						ragramslist.append(UpperCAmelCase__						)
						for i in range(0 ,	len(UpperCAmelCase__						) - 1						):
									if i < len(UpperCAmelCase__						) - 1:
												lowerCAmelCase_       :Tuple     	=    ragrams[i] + """ """ + ragrams[i + 1]
												ragrams.append(UpperCAmelCase__						)
									if i < len(UpperCAmelCase__						) - 2:
												lowerCAmelCase_       :str     	=    ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
												ragrams.append(UpperCAmelCase__						)
									if i < len(UpperCAmelCase__						) - 3:
												lowerCAmelCase_       :List[Any]     	=    ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
												ragrams.append(UpperCAmelCase__						)
						ragramslist.append(UpperCAmelCase__						)
						ragramslist.append(UpperCAmelCase__						)
						ragramslist.append(UpperCAmelCase__						)

			for i in range(0 ,	len(UpperCAmelCase__						) - 1						):
						if i < len(UpperCAmelCase__						) - 1:
									lowerCAmelCase_       :Optional[Any]     	=    sagrams[i] + """ """ + sagrams[i + 1]
									sagrams.append(UpperCAmelCase__						)
						if i < len(UpperCAmelCase__						) - 2:
									lowerCAmelCase_       :List[Any]     	=    sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
									sagrams.append(UpperCAmelCase__						)
						if i < len(UpperCAmelCase__						) - 3:
									lowerCAmelCase_       :str     	=    sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
									sagrams.append(UpperCAmelCase__						)

			for i in range(0 ,	len(UpperCAmelCase__						) - 1						):
						if i < len(UpperCAmelCase__						) - 1:
									lowerCAmelCase_       :List[Any]     	=    cagrams[i] + """ """ + cagrams[i + 1]
									cagrams.append(UpperCAmelCase__						)
						if i < len(UpperCAmelCase__						) - 2:
									lowerCAmelCase_       :Any     	=    cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
									cagrams.append(UpperCAmelCase__						)
						if i < len(UpperCAmelCase__						) - 3:
									lowerCAmelCase_       :Tuple     	=    cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
									cagrams.append(UpperCAmelCase__						)

			((lowerCAmelCase_)	,			(lowerCAmelCase_)	,			(lowerCAmelCase_))       :List[str]     	=    SARIngram(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__						)
			((lowerCAmelCase_)	,			(lowerCAmelCase_)	,			(lowerCAmelCase_))       :Optional[Any]     	=    SARIngram(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__						)
			((lowerCAmelCase_)	,			(lowerCAmelCase_)	,			(lowerCAmelCase_))       :Optional[int]     	=    SARIngram(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__						)
			((lowerCAmelCase_)	,			(lowerCAmelCase_)	,			(lowerCAmelCase_))       :Optional[int]     	=    SARIngram(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__						)
			lowerCAmelCase_       :int     	=    sum([keepascore, keepascore, keepascore, keepascore]						) / 4
			lowerCAmelCase_       :Optional[Any]     	=    sum([delascore, delascore, delascore, delascore]						) / 4
			lowerCAmelCase_       :Optional[Any]     	=    sum([addascore, addascore, addascore, addascore]						) / 4
			lowerCAmelCase_       :Any     	=    (avgkeepscore + avgdelscore + avgaddscore) / 3
			return finalscore






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    bool = True ,	lowercase__						:    str = "13a" ,	lowercase__						:    bool = True						)    ->							Any:


			'''simple docstring'''





			if lowercase:
						lowerCAmelCase_       :Optional[Any]     	=    sentence.lower()

			if tokenizer in ["13a", "intl"]:
						if version.parse(sacrebleu.__version__						).major >= 2:
									lowerCAmelCase_       :Any     	=    sacrebleu.metrics.bleu._get_tokenizer(UpperCAmelCase__						)()(UpperCAmelCase__						)
						else:
									lowerCAmelCase_       :Any     	=    sacrebleu.TOKENIZERS[tokenizer]()(UpperCAmelCase__						)
			elif tokenizer == "moses":
						lowerCAmelCase_       :Optional[Any]     	=    sacremoses.MosesTokenizer().tokenize(UpperCAmelCase__ ,	return_str=UpperCAmelCase__ ,	escape=UpperCAmelCase__						)
			elif tokenizer == "penn":
						lowerCAmelCase_       :Dict     	=    sacremoses.MosesTokenizer().penn_tokenize(UpperCAmelCase__ ,	return_str=UpperCAmelCase__						)
			else:
						lowerCAmelCase_       :List[Any]     	=    sentence

			if not return_str:
						lowerCAmelCase_       :Any     	=    normalized_sent.split()

			return normalized_sent




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION				)
class      _SCREAMING_SNAKE_CASE   (     datasets.Metric				):






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						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			,  __A			,  __A			,  __A			)     ->	Any:






						if not (len(__lowerCAmelCase			) == len(__lowerCAmelCase			) == len(__lowerCAmelCase			)):
									raise ValueError("""Sources length must match predictions and references lengths."""			)
						lowerCAmelCase_       :str     	=    0
						for src, pred, refs in zip(__lowerCAmelCase			,  __lowerCAmelCase			,  __lowerCAmelCase			):
									sari_score += SARIsent(normalize(__lowerCAmelCase			)			,  normalize(__lowerCAmelCase			)			,  [normalize(__lowerCAmelCase			) for sent in refs]			)
						lowerCAmelCase_       :Optional[Any]     	=    sari_score / len(__lowerCAmelCase			)
						return {"sari": 100 * sari_score}











# 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 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
   _UpperCAmelCase  						=      TEXT_TO_IMAGE_IMAGE_PARAMS

   def 		A				(   self      :							Dict				)							->					int:


    """simple docstring"""






    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_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				)
    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=1000				, )
    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 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Tuple=0				)							->					str:


    """simple docstring"""






    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 		A				(   self      :							Optional[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 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset([]						)

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






    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_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				)
    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=1000				, )
    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 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

   def 		A				(   self      :							Tuple				)							->					Union[str, Any]:


    """simple docstring"""






    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      :							Tuple				):
     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_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				)
    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=1000				, )
    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 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=0				)							->					str:


    """simple docstring"""






    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 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)

    a							=		1_0.0
    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 		A				(   self      :							Dict				)							->					List[Any]:


    """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 		A				(   self      :							Optional[int]				)							->					str:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)

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


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)




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


    """simple docstring"""






    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 		_lowercase		(							unittest.TestCase						):

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






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

   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






    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 		A				(   self      :							Any				)							->					Dict:


    """simple docstring"""






    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      :							Any				, __lowerCAmelCase      :							int				):
     a							=		np.array(image.convert("RGB"				)				).astype(np.floataa				) / 2_5_5.0
     a							=		np.array(image_mask.convert("L"				)				).astype(np.floataa				) / 2_5_5.0

     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

