


import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


A	:		Tuple    =       logging.getLogger(__name__)
A	:		List[str]    =       list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A	:		List[Any]    =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 							A   :
					'''simple docstring'''






					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase__							)}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={
					        '''help''': (
					            '''Override some existing default config settings when a model is trained from scratch. Example: '''
					            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
					        )
					    }			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}			,  )
					A__      =			field(
					    default='''main'''			,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }			,  )

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
									raise ValueError(
									    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""		)






@dataclass
class 							A   :
					'''simple docstring'''






					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}							)
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
					A__      =			field(default=UpperCAmelCase__			,  metadata={'''help''': '''The input training data file (a text file).'''}							)
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)
					A__      =			field(
					    default=5			,  metadata={
					        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
					    }			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated. Default to the max input length of the model.'''
					        )
					    }			,  )
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,  )
					A__      =			field(
					    default=0.15			,  metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}							)
					A__      =			field(
					    default=UpperCAmelCase__			,  metadata={
					        '''help''': (
					            '''Whether to pad all samples to `max_seq_length`. '''
					            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
					        )
					    }			,  )

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




							"""simple docstring"""


							if self.train_file is not None:
									lowercase__					      =		self.train_file.split("""."""		)[-1]
									assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
							if self.validation_file is not None:
									lowercase__					      =		self.validation_file.split("""."""		)[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[int]					)       ->  str:
		"""simple docstring"""




		with open(__magic_name__   ,   """r"""   ,   encoding="""utf-8"""					) as f:
				lowercase__					      =		[json.loads(__magic_name__					) for line in f.read().splitlines() if (len(__magic_name__					) > 0 and not line.isspace())]
		assert len(__magic_name__					) == len(__magic_name__					)

		lowercase__					      =		{c: dataset[c] for c in dataset.column_names}
		lowercase__					      =		refs
		return Dataset.from_dict(__magic_name__					)






def 			UpperCamelCase (     )       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
		if len(sys.argv					) == 2 and sys.argv[1].endswith(""".json"""					):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowercase__      ,     lowercase__      ,     lowercase__					      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
		else:
				lowercase__      ,     lowercase__      ,     lowercase__					      =		parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		lowercase__					      =		None
		if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
				lowercase__					      =		get_last_checkpoint(training_args.output_dir					)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    """Use --overwrite_output_dir to overcome."""					)
				elif last_checkpoint is not None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""					)

    # Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,   datefmt="""%m/%d/%Y %H:%M:%S"""   ,   handlers=[logging.StreamHandler(sys.stdout					)]   ,   )
		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'''					)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank					):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""   ,   __magic_name__					)

		# Set seed before initializing model.
		set_seed(training_args.seed					)

		# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
		# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
		# (the dataset will be downloaded automatically from the datasets Hub).
		#
		# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
		# 'text' is found. You can easily tweak this behavior (see below).
		#
		# In distributed training, the load_dataset function guarantee that only one local process can concurrently
		# download the dataset.
		if data_args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				lowercase__					      =		load_dataset(data_args.dataset_name   ,   data_args.dataset_config_name					)
				if "validation" not in datasets.keys():
						lowercase__					      =		load_dataset(
						    data_args.dataset_name   ,   data_args.dataset_config_name   ,   split=f'''train[:{data_args.validation_split_percentage}%]'''   ,   )
						lowercase__					      =		load_dataset(
						    data_args.dataset_name   ,   data_args.dataset_config_name   ,   split=f'''train[{data_args.validation_split_percentage}%:]'''   ,   )
		else:
				lowercase__					      =		{}
				if data_args.train_file is not None:
						lowercase__					      =		data_args.train_file
				if data_args.validation_file is not None:
						lowercase__					      =		data_args.validation_file
				lowercase__					      =		data_args.train_file.split("""."""					)[-1]
				if extension == "txt":
						lowercase__					      =		"""text"""
				lowercase__					      =		load_dataset(__magic_name__   ,   data_files=__magic_name__					)
		# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
		# https://huggingface.co/docs/datasets/loading_datasets.html.

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowercase__					      =		{
		    """cache_dir""": model_args.cache_dir,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				lowercase__					      =		AutoConfig.from_pretrained(model_args.config_name   ,   **__magic_name__					)
		elif model_args.model_name_or_path:
				lowercase__					      =		AutoConfig.from_pretrained(model_args.model_name_or_path   ,   **__magic_name__					)
		else:
				lowercase__					      =		CONFIG_MAPPING[model_args.model_type]()
				logger.warning("""You are instantiating a new config instance from scratch."""					)
				if model_args.config_overrides is not None:
						logger.info(f'''Overriding config: {model_args.config_overrides}'''					)
						config.update_from_string(model_args.config_overrides					)
						logger.info(f'''New config: {config}'''					)

		lowercase__					      =		{
		    """cache_dir""": model_args.cache_dir,
		    """use_fast""": model_args.use_fast_tokenizer,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.tokenizer_name:
				lowercase__					      =		AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,   **__magic_name__					)
		elif model_args.model_name_or_path:
				lowercase__					      =		AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,   **__magic_name__					)
		else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
				    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""					)

		if model_args.model_name_or_path:
				lowercase__					      =		AutoModelForMaskedLM.from_pretrained(
				    model_args.model_name_or_path   ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)   ,   config=__magic_name__   ,   cache_dir=model_args.cache_dir   ,   revision=model_args.model_revision   ,   use_auth_token=True if model_args.use_auth_token else None   ,   )
		else:
				logger.info("""Training new model from scratch"""					)
				lowercase__					      =		AutoModelForMaskedLM.from_config(__magic_name__					)

		model.resize_token_embeddings(len(__magic_name__					)					)

		# Preprocessing the datasets.
		# First we tokenize all the texts.
		if training_args.do_train:
				lowercase__					      =		datasets["""train"""].column_names
		else:
				lowercase__					      =		datasets["""validation"""].column_names
		lowercase__					      =		"""text""" if """text""" in column_names else column_names[0]

		lowercase__					      =		"""max_length""" if data_args.pad_to_max_length else False

		def tokenize_function(__magic_name__					:							int					):
				# Remove empty lines
				lowercase__					      =		[line for line in examples["""text"""] if len(__magic_name__					) > 0 and not line.isspace()]
				return tokenizer(examples["""text"""]   ,   padding=__magic_name__   ,   truncation=__magic_name__   ,   max_length=data_args.max_seq_length					)

		lowercase__					      =		datasets.map(
		    __magic_name__   ,   batched=__magic_name__   ,   num_proc=data_args.preprocessing_num_workers   ,   remove_columns=[text_column_name]   ,   load_from_cache_file=not data_args.overwrite_cache   ,   )

		# Add the chinese references if provided
		if data_args.train_ref_file is not None:
				lowercase__					      =		add_chinese_references(tokenized_datasets["""train"""]   ,   data_args.train_ref_file					)
		if data_args.validation_ref_file is not None:
				lowercase__					      =		add_chinese_references(
				    tokenized_datasets["""validation"""]   ,   data_args.validation_ref_file					)
		# If we have ref files, need to avoid it removed by trainer
		lowercase__					      =		data_args.train_ref_file or data_args.validation_ref_file
		if has_ref:
				lowercase__					      =		False

		# Data collator
		# This one will take care of randomly masking the tokens.
		lowercase__					      =		DataCollatorForWholeWordMask(tokenizer=__magic_name__   ,   mlm_probability=data_args.mlm_probability					)

		# Initialize our Trainer
		lowercase__					      =		Trainer(
		    model=__magic_name__   ,   args=__magic_name__   ,   train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None   ,   eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None   ,   tokenizer=__magic_name__   ,   data_collator=__magic_name__   ,   )

		# Training
		if training_args.do_train:
				if last_checkpoint is not None:
						lowercase__					      =		last_checkpoint
				elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
						lowercase__					      =		model_args.model_name_or_path
				else:
						lowercase__					      =		None
				lowercase__					      =		trainer.train(resume_from_checkpoint=__magic_name__					)
				trainer.save_model()  # Saves the tokenizer too for easy upload

				lowercase__					      =		os.path.join(training_args.output_dir   ,   """train_results.txt"""					)
				if trainer.is_world_process_zero():
						with open(__magic_name__   ,   """w"""					) as writer:
								logger.info("""***** Train results *****"""					)
								for key, value in sorted(train_result.metrics.items()					):
										logger.info(f'''  {key} = {value}'''					)
										writer.write(f'''{key} = {value}\n'''					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir   ,   """trainer_state.json"""					)					)

    # Evaluation
		lowercase__					      =		{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""					)

				lowercase__					      =		trainer.evaluate()

				lowercase__					      =		math.exp(eval_output["""eval_loss"""]					)
				lowercase__					      =		perplexity

				lowercase__					      =		os.path.join(training_args.output_dir   ,   """eval_results_mlm_wwm.txt"""					)
				if trainer.is_world_process_zero():
						with open(__magic_name__   ,   """w"""					) as writer:
								logger.info("""***** Eval results *****"""					)
								for key, value in sorted(results.items()					):
										logger.info(f'''  {key} = {value}'''					)
										writer.write(f'''{key} = {value}\n'''					)

		return results






def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  int:
		"""simple docstring"""




		main()


if __name__ == "__main__":
	main()





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		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				, attention_head_dim=(2, 4)				, use_linear_projection=_UpperCAmelCase				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		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				, sample_size=128				, )
							torch.manual_seed(0		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": init_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		self.get_dummy_inputs(_UpperCAmelCase		)
							lowercase__					      =		sd_pipe(**_UpperCAmelCase		).images
							lowercase__					      =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

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


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






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




							"""simple docstring"""


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

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 9E-3

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=_UpperCAmelCase				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 5E-1


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




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		PNDMScheduler.from_pretrained(_UpperCAmelCase				, subfolder="""scheduler"""		)
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, safety_checker=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, torch_dtype=torch.floataa				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

							lowercase__					      =		torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9

