"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 			lowerCamelCase_ (					)							->					Any:




						'''simple docstring'''



						def filter_log(__lowerCAmelCase				):
												return False if "This IS expected if you are initializing" in record.msg else True

						lowerCamelCase__					=datasets.utils.logging.get_logger("transformers.modeling_utils"				)
						logger.addFilter(__lowerCAmelCase				)
						try:
												yield
						finally:
												logger.removeFilter(__lowerCAmelCase				)


a							='\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

a							='\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

a							='\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'






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

				def 		_a	(      self      ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION	, citation=_CITATION	, homepage="https://github.com/Tiiiger/bert_score"	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"	, id="sequence"      ),
										            "references": datasets.Sequence(datasets.Value("string"	, id="sequence"      )	, id="references"      ),
										        }      )	, codebase_urls=["https://github.com/Tiiiger/bert_score"]	, reference_urls=[
										        "https://github.com/Tiiiger/bert_score",
										        "https://arxiv.org/abs/1904.09675",
										    ]	, )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=None	, _lowerCamelCase=64	, _lowerCamelCase=4	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=None	, _lowerCamelCase=False	, ):
										lowerCamelCase__					=bert_score.utils.get_hash
										lowerCamelCase__					=bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
																lowerCamelCase__					=functools.partial(_lowerCamelCase	, use_fast_tokenizer=_lowerCamelCase      )
																lowerCamelCase__					=functools.partial(_lowerCamelCase	, use_fast_tokenizer=_lowerCamelCase      )
										elif use_fast_tokenizer:
																raise ImportWarning(
																    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
																    "You can install it with `pip install \"bert-score>=0.3.10\"`."      )

										if model_type is None:
																assert lang is not None, "either lang or model_type should be specified"
																lowerCamelCase__					=bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																lowerCamelCase__					=bert_score.utils.modelalayers[model_type]

										lowerCamelCase__					=get_hash(
										    model=_lowerCamelCase	, num_layers=_lowerCamelCase	, idf=_lowerCamelCase	, rescale_with_baseline=_lowerCamelCase	, use_custom_baseline=baseline_path is not None	, )

										with filter_logging_context():
																if not hasattr(self	, "cached_bertscorer"      ) or self.cached_bertscorer.hash != hashcode:
																						lowerCamelCase__					=scorer(
																						    model_type=_lowerCamelCase	, num_layers=_lowerCamelCase	, batch_size=_lowerCamelCase	, nthreads=_lowerCamelCase	, all_layers=_lowerCamelCase	, idf=_lowerCamelCase	, device=_lowerCamelCase	, lang=_lowerCamelCase	, rescale_with_baseline=_lowerCamelCase	, baseline_path=_lowerCamelCase	, )

										((lowerCamelCase__)					,      (lowerCamelCase__)					,      (lowerCamelCase__))					=self.cached_bertscorer.score(
										    cands=_lowerCamelCase	, refs=_lowerCamelCase	, verbose=_lowerCamelCase	, batch_size=_lowerCamelCase	, )
										lowerCamelCase__					={
										    "precision": P.tolist(),
										    "recall": R.tolist(),
										    "f1": F.tolist(),
										    "hashcode": hashcode,
										}
										return output_dict

				def 		_a	(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, **_lowerCamelCase      ):
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if references is not None:
																lowerCamelCase__					=[[ref] if isinstance(_lowerCamelCase	, _lowerCamelCase      ) else ref for ref in references]
										super().add_batch(predictions=_lowerCamelCase	, references=_lowerCamelCase	, **_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, **_lowerCamelCase      ):
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if isinstance(_lowerCamelCase	, _lowerCamelCase      ):
																lowerCamelCase__					=[reference]
										super().add(prediction=_lowerCamelCase	, reference=_lowerCamelCase	, **_lowerCamelCase      )









"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    Any							)	->      Any:




										'''simple docstring'''



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






				@property
				def 				snake_case	( self		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    1
										_UpperCamelCase			:					Union[str, Any]     =    3
										_UpperCamelCase			:					Tuple     =    (3_2, 3_2)

										_UpperCamelCase			:					Any     =    floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0							)							).to(lowerCAmelCase__							)
										return image






				@property
				def 				snake_case	( self		:    Tuple							)	->      Union[str, Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Dict     =    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=3_2, )
										return model






				@property
				def 				snake_case	( self		:    Dict							)	->      List[Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					List[str]     =    AutoencoderKL(
										    block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
										return model






				@property
				def 				snake_case	( self		:    List[str]							)	->      Tuple:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Any     =    CLIPTextConfig(
										    bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-0_5, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
										return CLIPTextModel(lowerCAmelCase__							)






				@property
				def 				snake_case	( self		:    Dict							)	->      str:




										'''simple docstring'''



										def extract(*lowerCAmelCase__		:    int, **lowerCAmelCase__		:    int							):




																class  _a						:






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




																										'''simple docstring'''



																										_UpperCamelCase			:					Optional[int]     =    torch.ones([0]							)






																				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    List[str]							)	->      Tuple:




																										'''simple docstring'''



																										self.pixel_values.to(lowerCAmelCase__							)
																										return self

																return Out()

										return extract






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_cond_unet
										_UpperCamelCase			:					int     =    DDIMScheduler(
										    beta_start=0.00_085, beta_end=0.012, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase__, set_alpha_to_one=lowerCAmelCase__, )

										_UpperCamelCase			:					Union[str, Any]     =    self.dummy_vae
										_UpperCamelCase			:					Any     =    self.dummy_text_encoder
										_UpperCamelCase			:					Any     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Tuple     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					str     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''

										_UpperCamelCase			:					Any     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)
										_UpperCamelCase			:					Optional[Any]     =    output.images

										_UpperCamelCase			:					str     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					List[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Tuple     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Tuple     =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[int]     =    self.dummy_cond_unet
										_UpperCamelCase			:					List[str]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					int     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					List[str]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Tuple     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					str     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)

										_UpperCamelCase			:					Dict     =    output.images

										_UpperCamelCase			:					List[Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					int     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Union[str, Any]     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Optional[Any]     =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe''', safety_checker=lowerCAmelCase__							)
										assert isinstance(lowerCAmelCase__, lowerCAmelCase__							)
										assert isinstance(pipe.scheduler, lowerCAmelCase__							)
										assert pipe.safety_checker is None

										_UpperCamelCase			:					Optional[Any]     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained(lowerCAmelCase__							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_UpperCamelCase			:					int     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None






				@unittest.skipIf(torch_device != '''cuda''', '''This test requires a GPU'''							)
				def 				snake_case	( self		:    Any							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    self.dummy_cond_unet
										_UpperCamelCase			:					Union[str, Any]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					List[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# put models in fp16
										_UpperCamelCase			:					int     =    unet.half()
										_UpperCamelCase			:					Any     =    vae.half()
										_UpperCamelCase			:					List[Any]     =    bert.half()

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Optional[int]     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					Optional[int]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					List[str]     =    sd_pipe([prompt], num_inference_steps=2, output_type='''np'''							).images

										assert image.shape == (1, 6_4, 6_4, 3)




@nightly
@require_torch_gpu
class  _a						(      unittest.TestCase				):






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




										'''simple docstring'''



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






				def 				snake_case	( self		:    int							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					List[Any]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					str     =    '''a photo of a cat'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Optional[int]     =    3
										_UpperCamelCase			:					int     =    7

										# no sega enabled
										_UpperCamelCase			:					Tuple     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Tuple     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[str]     =    [
										    0.34_673_113,
										    0.38_492_733,
										    0.37_597_352,
										    0.34_086_335,
										    0.35_650_748,
										    0.35_579_205,
										    0.3_384_763,
										    0.34_340_236,
										    0.3_573_271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Dict     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[int]     =    [
										    0.41_887_826,
										    0.37_728_766,
										    0.30_138_272,
										    0.41_416_335,
										    0.41_664_985,
										    0.36_283_392,
										    0.36_191_246,
										    0.43_364_465,
										    0.43_001_732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					List[Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					int     =    '''an image of a crowded boulevard, realistic, 4k'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Dict     =    9
										_UpperCamelCase			:					Optional[Any]     =    7

										# no sega enabled
										_UpperCamelCase			:					Optional[int]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					List[str]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.43_497_998,
										    0.91_814_065,
										    0.7_540_739,
										    0.55_580_205,
										    0.8_467_265,
										    0.5_389_691,
										    0.62_574_506,
										    0.58_897_763,
										    0.50_926_757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					Optional[int]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.3_089_719,
										    0.30_500_144,
										    0.29_016_042,
										    0.30_630_964,
										    0.325_687,
										    0.29_419_225,
										    0.2_908_091,
										    0.28_723_598,
										    0.27_696_294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					Union[str, Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[int]     =    '''a castle next to a river'''
										_UpperCamelCase			:					Optional[int]     =    {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    4_8
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Optional[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					str     =    [
										    0.75_163_555,
										    0.76_037_145,
										    0.61_785,
										    0.9_189_673,
										    0.8_627_701,
										    0.85_189_694,
										    0.8_512_813,
										    0.87_012_076,
										    0.8_312_857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					List[str]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.73_553_365,
										    0.7_537_271,
										    0.74_341_905,
										    0.66_480_356,
										    0.6_472_925,
										    0.63_039_416,
										    0.64_812_905,
										    0.6_749_717,
										    0.6_517_102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






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




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    '''cuda'''
										_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', torch_dtype=torch.floataa							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					List[Any]     =    '''a photo of a cat'''
										_UpperCamelCase			:					str     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    3
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Any     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Union[str, Any]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.34_887_695,
										    0.3_876_953,
										    0.375,
										    0.34_423_828,
										    0.3_581_543,
										    0.35_717_773,
										    0.3_383_789,
										    0.34_570_312,
										    0.359_375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Tuple     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[Any]     =    [
										    0.42_285_156,
										    0.36_914_062,
										    0.29_077_148,
										    0.42_041_016,
										    0.41_918_945,
										    0.35_498_047,
										    0.3_618_164,
										    0.4_423_828,
										    0.43_115_234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2



