
'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=True	,			_SCREAMING_SNAKE_CASE		:   List[Any]="pt"  ):
						_SCREAMING_SNAKE_CASE				= {"add_prefix_space": True} if isinstance(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) and not line.startswith(" "  ) else {}
						_SCREAMING_SNAKE_CASE				= padding_side
						return tokenizer(
						    [line]	,			max_length=_SCREAMING_SNAKE_CASE	,			padding="max_length" if pad_to_max_length else None	,			truncation=_SCREAMING_SNAKE_CASE	,			return_tensors=_SCREAMING_SNAKE_CASE	,			add_special_tokens=_SCREAMING_SNAKE_CASE	,			**_SCREAMING_SNAKE_CASE	,			)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]=None	,			):
						_SCREAMING_SNAKE_CASE				= input_ids.ne(_SCREAMING_SNAKE_CASE  ).any(dim=0  )
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase="train"					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=""					,						):
								super().__init__()
								_SCREAMING_SNAKE_CASE				= Path(UpperCamelCase      ).joinpath(type_path + ".source"      )
								_SCREAMING_SNAKE_CASE				= Path(UpperCamelCase      ).joinpath(type_path + ".target"      )
								_SCREAMING_SNAKE_CASE				= self.get_char_lens(self.src_file      )
								_SCREAMING_SNAKE_CASE				= max_source_length
								_SCREAMING_SNAKE_CASE				= max_target_length
								assert min(self.src_lens      ) > 0, F'found empty line in {self.src_file}'
								_SCREAMING_SNAKE_CASE				= tokenizer
								_SCREAMING_SNAKE_CASE				= prefix
								if n_obs is not None:
														_SCREAMING_SNAKE_CASE				= self.src_lens[:n_obs]
								_SCREAMING_SNAKE_CASE				= src_lang
								_SCREAMING_SNAKE_CASE				= tgt_lang




		def __len__(				self      ):
								return len(self.src_lens      )




		def __getitem__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= index + 1  # linecache starts at 1
								_SCREAMING_SNAKE_CASE				= self.prefix + linecache.getline(str(self.src_file      )					,						UpperCamelCase      ).rstrip("\n"      )
								_SCREAMING_SNAKE_CASE				= linecache.getline(str(self.tgt_file      )					,						UpperCamelCase      ).rstrip("\n"      )
								assert source_line, F'empty source line for index {index}'
								assert tgt_line, F'empty tgt line for index {index}'

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer					,						UpperCamelCase      ):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								_SCREAMING_SNAKE_CASE				= (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer					,						UpperCamelCase      ) else self.tokenizer
								)
								_SCREAMING_SNAKE_CASE				= self.tokenizer.generator if isinstance(self.tokenizer					,						UpperCamelCase      ) else self.tokenizer

								_SCREAMING_SNAKE_CASE				= encode_line(UpperCamelCase					,						UpperCamelCase					,						self.max_source_length					,						"right"      )
								_SCREAMING_SNAKE_CASE				= encode_line(UpperCamelCase					,						UpperCamelCase					,						self.max_target_length					,						"right"      )

								_SCREAMING_SNAKE_CASE				= source_inputs["input_ids"].squeeze()
								_SCREAMING_SNAKE_CASE				= target_inputs["input_ids"].squeeze()
								_SCREAMING_SNAKE_CASE				= source_inputs["attention_mask"].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}




		@staticmethod
		def     lowercase       (				UpperCamelCase      ):
								return [len(UpperCamelCase      ) for x in Path(UpperCamelCase      ).open().readlines()]




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= torch.stack([x["input_ids"] for x in batch]      )
								_SCREAMING_SNAKE_CASE				= torch.stack([x["attention_mask"] for x in batch]      )
								_SCREAMING_SNAKE_CASE				= torch.stack([x["decoder_input_ids"] for x in batch]      )
								_SCREAMING_SNAKE_CASE				= (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer					,						UpperCamelCase      )
								    else self.tokenizer.pad_token_id
								)
								_SCREAMING_SNAKE_CASE				= (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer					,						UpperCamelCase      )
								    else self.tokenizer.pad_token_id
								)
								_SCREAMING_SNAKE_CASE				= trim_batch(UpperCamelCase					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= trim_batch(UpperCamelCase					,						UpperCamelCase					,						attention_mask=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "input_ids": source_ids,
								    "attention_mask": source_mask,
								    "decoder_input_ids": y,
								}
								return batch
_snake_case				:   str     =				getLogger(__name__)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[List]  ):
						return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= get_git_info()
						save_json(_SCREAMING_SNAKE_CASE	,			os.path.join(_SCREAMING_SNAKE_CASE	,			"git_log.json"  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Dict=4	,			**_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						with open(_SCREAMING_SNAKE_CASE	,			"w"  ) as f:
												json.dump(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			indent=_SCREAMING_SNAKE_CASE	,			**_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						with open(_SCREAMING_SNAKE_CASE  ) as f:
												return json.load(_SCREAMING_SNAKE_CASE  )



def 			_a		(	):
						_SCREAMING_SNAKE_CASE				= git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= {
						    "repo_id": str(_SCREAMING_SNAKE_CASE  ),
						    "repo_sha": str(repo.head.object.hexsha  ),
						    "repo_branch": str(repo.active_branch  ),
						    "hostname": str(socket.gethostname()  ),
						}
						return repo_infos



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Callable	,			_SCREAMING_SNAKE_CASE		:   Iterable  ):
						return list(map(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						with open(_SCREAMING_SNAKE_CASE	,			"wb"  ) as f:
												return pickle.dump(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):

						def remove_articles(_SCREAMING_SNAKE_CASE		:   Any  ):
												return re.sub(r"\b(a|an|the)\b"	,			" "	,			_SCREAMING_SNAKE_CASE  )

						def white_space_fix(_SCREAMING_SNAKE_CASE		:   Dict  ):
												return " ".join(text.split()  )

						def remove_punc(_SCREAMING_SNAKE_CASE		:   str  ):
												_SCREAMING_SNAKE_CASE				= set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE  )  )  )  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Tuple  ):
						_SCREAMING_SNAKE_CASE				= normalize_answer(_SCREAMING_SNAKE_CASE  ).split()
						_SCREAMING_SNAKE_CASE				= normalize_answer(_SCREAMING_SNAKE_CASE  ).split()
						_SCREAMING_SNAKE_CASE				= Counter(_SCREAMING_SNAKE_CASE  ) & Counter(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= sum(common.values()  )
						if num_same == 0:
												return 0
						_SCREAMING_SNAKE_CASE				= 1.0 * num_same / len(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 1.0 * num_same / len(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (2 * precision * recall) / (precision + recall)
						return fa



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						return normalize_answer(_SCREAMING_SNAKE_CASE  ) == normalize_answer(_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
						assert len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 0
						for hypo, pred in zip(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ):
												em += exact_match_score(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						if len(_SCREAMING_SNAKE_CASE  ) > 0:
												em /= len(_SCREAMING_SNAKE_CASE  )
						return {"em": em}



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
						return model_prefix.startswith("rag"  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= {p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						_SCREAMING_SNAKE_CASE				= "dropout_rate"
						for p in extra_params:
												if getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ):
																		if not hasattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) and not hasattr(_SCREAMING_SNAKE_CASE	,			equivalent_param[p]  ):
																								logger.info("config doesn't have a `{}` attribute".format(_SCREAMING_SNAKE_CASE  )  )
																								delattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
																								continue
																		_SCREAMING_SNAKE_CASE				= p if hasattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) else equivalent_param[p]
																		setattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  )
																		delattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						return hparams, config

'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim * 4




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= MCLIPConfig(
								    numDims=self.cross_attention_dim					,						transformerDimensions=self.text_embedder_hidden_size					,						hidden_size=self.text_embedder_hidden_size					,						intermediate_size=37					,						num_attention_heads=4					,						num_hidden_layers=5					,						vocab_size=1_005					,						)

								_SCREAMING_SNAKE_CASE				= MultilingualCLIP(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= text_encoder.eval()

								return text_encoder




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )

								_SCREAMING_SNAKE_CASE				= {
								    "in_channels": 4,
								    # Out channels is double in channels because predicts mean and variance
								    "out_channels": 8,
								    "addition_embed_type": "text_image",
								    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
								    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
								    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
								    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    "layers_per_block": 1,
								    "encoder_hid_dim": self.text_embedder_hidden_size,
								    "encoder_hid_dim_type": "text_image_proj",
								    "cross_attention_dim": self.cross_attention_dim,
								    "attention_head_dim": 4,
								    "resnet_time_scale_shift": "scale_shift",
								    "class_embed_type": None,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "horse",
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= pipe(
								    **self.get_dummy_inputs(UpperCamelCase      )					,						return_dict=UpperCamelCase					,						)[0]

								_SCREAMING_SNAKE_CASE				= image[0, -3:, -3:, -1]
								_SCREAMING_SNAKE_CASE				= image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								_SCREAMING_SNAKE_CASE				= np.array(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								assert (
								    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


@slow
@require_torch_gpu
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
								_SCREAMING_SNAKE_CASE				= "A red cartoon frog, 4k"

								_SCREAMING_SNAKE_CASE				= KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior"					,						torch_dtype=torch.floataa      )
								pipe_prior.to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.Generator(device="cpu"      ).manual_seed(0      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe_prior(
								    UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=5					,						negative_prompt=""					,						).to_tuple()

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )