





"""simple docstring"""




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class  _a						:






				def __init__( self		:    Any, lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    Any							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    cuda
										_UpperCamelCase			:					Optional[int]     =    AutoConfig.from_pretrained(lowerCAmelCase__							)
										_UpperCamelCase			:					List[str]     =    AutoModelForCTC.from_pretrained(lowerCAmelCase__							)
										self.model.eval()
										if self.cuda:
																self.model.to(device='''cuda'''							)
										_UpperCamelCase			:					Optional[int]     =    AutoProcessor.from_pretrained(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    torchaudio.transforms.Resample(lowerCAmelCase__, 1_6_0_0_0							)
										_UpperCamelCase			:					Optional[Any]     =    0
										_UpperCamelCase			:					Union[str, Any]     =    list(self.processor.tokenizer.get_vocab().keys()							)
										for i in range(len(lowerCAmelCase__							)							):
																if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						_UpperCamelCase			:					Union[str, Any]     =    i
										print('''Blank Token id [PAD]/<pad>''', lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    blank_id






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




										'''simple docstring'''



										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    torchaudio.load(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    self.resampler(lowerCAmelCase__							).squeeze().numpy()
										return speech






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




										'''simple docstring'''



										_UpperCamelCase			:					int     =    self.blank_id
										_UpperCamelCase			:					Union[str, Any]     =    '''|'''.join(item['''sent'''].split(''' '''							)							)
										if not os.path.isfile(item['''wav_path''']							):
																print(item['''wav_path'''], '''not found in wavs directory'''							)

										_UpperCamelCase			:					List[Any]     =    self.speech_file_to_array_fn(item['''wav_path''']							)
										_UpperCamelCase			:					Any     =    self.processor(lowerCAmelCase__, sampling_rate=1_6_0_0_0, return_tensors='''pt''', padding=lowerCAmelCase__							)
										if self.cuda:
																_UpperCamelCase			:					Dict     =    inputs.to(device='''cuda'''							)

										with torch.no_grad():
																_UpperCamelCase			:					Any     =    self.model(inputs.input_values							).logits

										# get the emission probability at frame level
										_UpperCamelCase			:					str     =    torch.log_softmax(lowerCAmelCase__, dim=-1							)
										_UpperCamelCase			:					List[str]     =    emissions[0].cpu().detach()

										# get labels from vocab
										_UpperCamelCase			:					str     =    ([''''''] + list(self.processor.tokenizer.get_vocab().keys()							))[
										    :-1
										]  # logits don't align with the tokenizer's vocab

										_UpperCamelCase			:					Dict     =    {c: i for i, c in enumerate(lowerCAmelCase__							)}
										_UpperCamelCase			:					str     =    []
										for c in transcript:
																if c in dictionary:
																						tokens.append(dictionary[c]							)

										def get_trellis(lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    List[Any]=0							):
																_UpperCamelCase			:					Any     =    emission.size(0							)
																_UpperCamelCase			:					Optional[int]     =    len(lowerCAmelCase__							)

																# Trellis has extra diemsions for both time axis and tokens.
																# The extra dim for tokens represents <SoS> (start-of-sentence)
																# The extra dim for time axis is for simplification of the code.
																_UpperCamelCase			:					Tuple     =    torch.full((num_frames + 1, num_tokens + 1), -float('''inf'''							)							)
																_UpperCamelCase			:					int     =    0
																for t in range(lowerCAmelCase__							):
																						_UpperCamelCase			:					List[Any]     =    torch.maximum(
																						    # Score for staying at the same token
																						    trellis[t, 1:] + emission[t, blank_id], trellis[t, :-1] + emission[t, tokens], )
																return trellis

										_UpperCamelCase			:					int     =    get_trellis(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__							)




										@dataclass
										class  _a						:
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42

										def backtrack(lowerCAmelCase__		:    str, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    List[str]=0							):
																_UpperCamelCase			:					Optional[Any]     =    trellis.size(1							) - 1
																_UpperCamelCase			:					int     =    torch.argmax(trellis[:, j]							).item()

																_UpperCamelCase			:					Optional[Any]     =    []
																for t in range(lowerCAmelCase__, 0, -1							):
																						# 1. Figure out if the current position was stay or change
																						# Note (again):
																						# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
																						# Score for token staying the same from time frame J-1 to T.
																						_UpperCamelCase			:					Optional[int]     =    trellis[t - 1, j] + emission[t - 1, blank_id]
																						# Score for token changing from C-1 at T-1 to J at T.
																						_UpperCamelCase			:					Optional[int]     =    trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						_UpperCamelCase			:					List[str]     =    emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
																						# Return token index and time index in non-trellis coordinate.
																						path.append(Point(j - 1, t - 1, lowerCAmelCase__							)							)

																						# 3. Update the token
																						if changed > stayed:
																												j -= 1
																												if j == 0:
																																		break
																else:
																						raise ValueError('''Failed to align'''							)
																return path[::-1]

										_UpperCamelCase			:					List[str]     =    backtrack(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__							)




										@dataclass
										class  _a						:
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42






														def __repr__( self		:    Dict							)	->      int:




																				'''simple docstring'''



																				return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""






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




																				'''simple docstring'''



																				return self.end - self.start

										def merge_repeats(lowerCAmelCase__		:    int							):
																_UpperCamelCase   ,							_UpperCamelCase			:					Dict     =    0, 0
																_UpperCamelCase			:					List[str]     =    []
																while ia < len(lowerCAmelCase__							):
																						while ia < len(lowerCAmelCase__							) and path[ia].token_index == path[ia].token_index:
																												ia += 1
																						_UpperCamelCase			:					Any     =    sum(path[k].score for k in range(lowerCAmelCase__, lowerCAmelCase__							)							) / (ia - ia)
																						segments.append(
																						    Segment(
																						        transcript[path[ia].token_index], path[ia].time_index, path[ia - 1].time_index + 1, lowerCAmelCase__, )							)
																						_UpperCamelCase			:					Tuple     =    ia
																return segments

										_UpperCamelCase			:					int     =    merge_repeats(lowerCAmelCase__							)
										with open(item['''out_path'''], '''w'''							) as out_align:
																for seg in segments:
																						out_align.write(str(lowerCAmelCase__							) + '''\n'''							)






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




										'''simple docstring'''



										if not os.path.exists(lowerCAmelCase__							):
																os.makedirs(lowerCAmelCase__							)

										# load text file
										_UpperCamelCase			:					Dict     =    open(lowerCAmelCase__, encoding='''utf8'''							).readlines()

										_UpperCamelCase			:					List[Any]     =    []
										for line in lines:
																if len(line.strip().split('''\t'''							)							) != 2:
																						print('''Script must be in format: 00001  this is my sentence'''							)
																						exit()

																_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    line.strip().split('''\t'''							)
																_UpperCamelCase			:					Any     =    os.path.join(lowerCAmelCase__, wav_name + '''.wav'''							)
																_UpperCamelCase			:					Dict     =    os.path.join(lowerCAmelCase__, wav_name + '''.txt'''							)

																items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path}							)
										print('''Number of samples found in script file''', len(lowerCAmelCase__							)							)

										for item in tqdm(lowerCAmelCase__							):
																self.align_single_sample(lowerCAmelCase__							)





def        a_				(							):
						_UpperCamelCase			:					Union[str, Any]     =    argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name'''       ,						type=_lowercase       ,						default='''arijitx/wav2vec2-xls-r-300m-bengali'''       ,						help='''wav2vec model name'''   )
						parser.add_argument('''--wav_dir'''       ,						type=_lowercase       ,						default='''./wavs'''       ,						help='''directory containing wavs'''   )
						parser.add_argument('''--text_file'''       ,						type=_lowercase       ,						default='''script.txt'''       ,						help='''file containing text'''   )
						parser.add_argument('''--input_wavs_sr'''       ,						type=_lowercase       ,						default=1_6000       ,						help='''sampling rate of input audios'''   )
						parser.add_argument(
						    '''--output_dir'''       ,						type=_lowercase       ,						default='''./out_alignment'''       ,						help='''output directory containing the alignment files'''   )
						parser.add_argument('''--cuda'''       ,						action='''store_true'''   )

						_UpperCamelCase			:					Optional[Any]     =    parser.parse_args()

						_UpperCamelCase			:					str     =    WavaVecaAligner(args.model_name       ,						args.input_wavs_sr       ,						args.cuda   )
						aligner.align_data(args.wav_dir       ,						args.text_file       ,						args.output_dir   )


if __name__ == "__main__":
						main()










"""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



