





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCAmelCase_												=       logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCAmelCase_												=       {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCAmelCase_												=       {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCAmelCase_												=       sorted(arg_to_scheduler.keys())
lowerCAmelCase_												=       '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class       __lowerCAmelCase (    pl.LightningModule  ):






			def __init__(self      ,							__magic_name__      ,							__magic_name__=None      ,							__magic_name__="base"      ,							__magic_name__=None      ,							__magic_name__=None      ,							__magic_name__=None      ,							**__magic_name__      ,							)  ->     List[str]:


							'''simple docstring'''



							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(__magic_name__					)
							snake_case_		:					List[Any]			    =						0
							snake_case_		:					Tuple			    =						Path(self.hparams.output_dir					)
							snake_case_		:					Any			    =						self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
											snake_case_		:					Any			    =						AutoConfig.from_pretrained(
											    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path      ,							**({'''num_labels''': num_labels} if num_labels is not None else {})      ,							cache_dir=__magic_name__      ,							**__magic_name__      ,							)
							else:
											snake_case_		:					PretrainedConfig			    =						config

							snake_case_		:					Tuple			    =						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
							for p in extra_model_params:
											if getattr(self.hparams      ,							__magic_name__      ,							__magic_name__					):
															assert hasattr(self.config      ,							__magic_name__					), F'''model config doesn\'t have a `{p}` attribute'''
															setattr(self.config      ,							__magic_name__      ,							getattr(self.hparams      ,							__magic_name__					)					)

							if tokenizer is None:
											snake_case_		:					List[Any]			    =						AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,							cache_dir=__magic_name__      ,							)
							else:
											snake_case_		:					PreTrainedTokenizer			    =						tokenizer
							snake_case_		:					Union[str, Any]			    =						MODEL_MODES[mode]
							if model is None:
											snake_case_		:					Dict			    =						self.model_type.from_pretrained(
											    self.hparams.model_name_or_path      ,							from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path					)      ,							config=self.config      ,							cache_dir=__magic_name__      ,							)
							else:
											snake_case_		:					int			    =						model






			def     lowerCamelCase						(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						self.model_type.from_pretrained(*__magic_name__      ,							**__magic_name__					)






			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							snake_case_		:					Any			    =						arg_to_scheduler[self.hparams.lr_scheduler]
							snake_case_		:					List[Any]			    =						get_schedule_func(
							    self.opt      ,							num_warmup_steps=self.hparams.warmup_steps      ,							num_training_steps=self.total_steps()					)
							snake_case_		:					int			    =						{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
							return scheduler






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.model
							snake_case_		:					Union[str, Any]			    =						['''bias''', '''LayerNorm.weight''']
							snake_case_		:					int			    =						[
							    {
							        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
							        '''weight_decay''': self.hparams.weight_decay,
							    },
							    {
							        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
							        '''weight_decay''': 0.0,
							    },
							]
							if self.hparams.adafactor:
											snake_case_		:					Optional[Any]			    =						Adafactor(
											    __magic_name__      ,							lr=self.hparams.learning_rate      ,							scale_parameter=__magic_name__      ,							relative_step=__magic_name__					)

							else:
											snake_case_		:					Any			    =						AdamW(
											    __magic_name__      ,							lr=self.hparams.learning_rate      ,							eps=self.hparams.adam_epsilon					)
							snake_case_		:					Tuple			    =						optimizer

							snake_case_		:					Any			    =						self.get_lr_scheduler()

							return [optimizer], [scheduler]






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__					)  ->     Tuple:


							'''simple docstring'''



							return self.validation_step(__magic_name__      ,							__magic_name__					)






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


							'''simple docstring'''



							return self.validation_end(__magic_name__					)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						max(1      ,							self.hparams.gpus					)  # TODO: consider num_tpu_cores
							snake_case_		:					Tuple			    =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							if mode == "test":
											snake_case_		:					List[str]			    =						len(self.test_dataloader().dataset					)
							else:
											snake_case_		:					Union[str, Any]			    =						self.get_dataloader('''train'''      ,							self.hparams.train_batch_size      ,							shuffle=__magic_name__					)
											snake_case_		:					Optional[Any]			    =						len(self.train_dataloader().dataset					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__ = False					)  ->     Optional[int]:


							'''simple docstring'''



							raise NotImplementedError('''You must implement this for your task'''					)






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



							return self.train_loader






			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							return self.get_dataloader('''dev'''      ,							self.hparams.eval_batch_size      ,							shuffle=__magic_name__					)






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							return self.get_dataloader('''test'''      ,							self.hparams.eval_batch_size      ,							shuffle=__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							return os.path.join(
							    self.hparams.data_dir      ,							'''cached_{}_{}_{}'''.format(
							        __magic_name__      ,							list(filter(__magic_name__      ,							self.hparams.model_name_or_path.split('''/'''					)					)					).pop()      ,							str(self.hparams.max_seq_length					)      ,							)      ,							)






			@pl.utilities.rank_zero_only
			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     None:


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						self.output_dir.joinpath('''best_tfmr'''					)
							snake_case_		:					str			    =						self.step_count
							self.model.save_pretrained(__magic_name__					)
							self.tokenizer.save_pretrained(__magic_name__					)






			@staticmethod
			def     lowerCamelCase						(__magic_name__      ,							__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



							parser.add_argument(
							    '''--model_name_or_path'''      ,							default=__magic_name__      ,							type=__magic_name__      ,							required=__magic_name__      ,							help='''Path to pretrained model or model identifier from huggingface.co/models'''      ,							)
							parser.add_argument(
							    '''--config_name'''      ,							default=''''''      ,							type=__magic_name__      ,							help='''Pretrained config name or path if not the same as model_name'''					)
							parser.add_argument(
							    '''--tokenizer_name'''      ,							default=__magic_name__      ,							type=__magic_name__      ,							help='''Pretrained tokenizer name or path if not the same as model_name'''      ,							)
							parser.add_argument(
							    '''--cache_dir'''      ,							default=''''''      ,							type=__magic_name__      ,							help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''      ,							)
							parser.add_argument(
							    '''--encoder_layerdrop'''      ,							type=__magic_name__      ,							help='''Encoder layer dropout probability (Optional). Goes into model.config'''      ,							)
							parser.add_argument(
							    '''--decoder_layerdrop'''      ,							type=__magic_name__      ,							help='''Decoder layer dropout probability (Optional). Goes into model.config'''      ,							)
							parser.add_argument(
							    '''--dropout'''      ,							type=__magic_name__      ,							help='''Dropout probability (Optional). Goes into model.config'''      ,							)
							parser.add_argument(
							    '''--attention_dropout'''      ,							type=__magic_name__      ,							help='''Attention dropout probability (Optional). Goes into model.config'''      ,							)
							parser.add_argument('''--learning_rate'''      ,							default=5e-5      ,							type=__magic_name__      ,							help='''The initial learning rate for Adam.'''					)
							parser.add_argument(
							    '''--lr_scheduler'''      ,							default='''linear'''      ,							choices=__magic_name__      ,							metavar=__magic_name__      ,							type=__magic_name__      ,							help='''Learning rate scheduler'''      ,							)
							parser.add_argument('''--weight_decay'''      ,							default=0.0      ,							type=__magic_name__      ,							help='''Weight decay if we apply some.'''					)
							parser.add_argument('''--adam_epsilon'''      ,							default=1e-8      ,							type=__magic_name__      ,							help='''Epsilon for Adam optimizer.'''					)
							parser.add_argument('''--warmup_steps'''      ,							default=0      ,							type=__magic_name__      ,							help='''Linear warmup over warmup_steps.'''					)
							parser.add_argument('''--num_workers'''      ,							default=4      ,							type=__magic_name__      ,							help='''kwarg passed to DataLoader'''					)
							parser.add_argument('''--num_train_epochs'''      ,							dest='''max_epochs'''      ,							default=3      ,							type=__magic_name__					)
							parser.add_argument('''--train_batch_size'''      ,							default=32      ,							type=__magic_name__					)
							parser.add_argument('''--eval_batch_size'''      ,							default=32      ,							type=__magic_name__					)
							parser.add_argument('''--adafactor'''      ,							action='''store_true'''					)




class       __lowerCAmelCase (    pl.Callback  ):






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__					)  ->     int:


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						trainer.lr_schedulers[0]['''scheduler''']
							snake_case_		:					Tuple			    =						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
							pl_module.logger.log_metrics(__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



							rank_zero_info('''***** Validation results *****'''					)
							snake_case_		:					Optional[Any]			    =						trainer.callback_metrics
							# Log results
							for key in sorted(__magic_name__					):
											if key not in ["log", "progress_bar"]:
															rank_zero_info('''{} = {}\n'''.format(__magic_name__      ,							str(metrics[key]					)					)					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__					)  ->     str:


							'''simple docstring'''



							rank_zero_info('''***** Test results *****'''					)
							snake_case_		:					str			    =						trainer.callback_metrics
							# Log and save results to file
							snake_case_		:					Tuple			    =						os.path.join(pl_module.hparams.output_dir      ,							'''test_results.txt'''					)
							with open(__magic_name__      ,							'''w'''					) as writer:
											for key in sorted(__magic_name__					):
															if key not in ["log", "progress_bar"]:
																			rank_zero_info('''{} = {}\n'''.format(__magic_name__      ,							str(metrics[key]					)					)					)
																			writer.write('''{} = {}\n'''.format(__magic_name__      ,							str(metrics[key]					)					)					)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		None:






				"""simple docstring"""


				parser.add_argument(
				    '''--output_dir'''				,       default=_UpperCamelCase				,       type=_UpperCamelCase				,       required=_UpperCamelCase				,       help='''The output directory where the model predictions and checkpoints will be written.'''				,       )
				parser.add_argument(
				    '''--fp16'''				,       action='''store_true'''				,       help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,       )

				parser.add_argument(
				    '''--fp16_opt_level'''				,       type=_UpperCamelCase				,       default='''O2'''				,       help=(
				        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
				        '''See details at https://nvidia.github.io/apex/amp.html'''
				    )				,       )
				parser.add_argument('''--n_tpu_cores'''				,       dest='''tpu_cores'''				,       type=_UpperCamelCase				)
				parser.add_argument('''--max_grad_norm'''				,       dest='''gradient_clip_val'''				,       default=1.0				,       type=_UpperCamelCase				,       help='''Max gradient norm'''				)
				parser.add_argument('''--do_train'''				,       action='''store_true'''				,       help='''Whether to run training.'''				)
				parser.add_argument('''--do_predict'''				,       action='''store_true'''				,       help='''Whether to run predictions on the test set.'''				)
				parser.add_argument(
				    '''--gradient_accumulation_steps'''				,       dest='''accumulate_grad_batches'''				,       type=_UpperCamelCase				,       default=1				,       help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,       )
				parser.add_argument('''--seed'''				,       type=_UpperCamelCase				,       default=42				,       help='''random seed for initialization'''				)
				parser.add_argument(
				    '''--data_dir'''				,       default=_UpperCamelCase				,       type=_UpperCamelCase				,       required=_UpperCamelCase				,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,       )






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase=None				,       _UpperCamelCase=True				,       _UpperCamelCase=[]				,       _UpperCamelCase=None				,       _UpperCamelCase=None				,       **_UpperCamelCase				,       )	->		Optional[Any]:






				"""simple docstring"""


				pl.seed_everything(args.seed				)

				# init model
				snake_case_		:					List[Any]			    =						Path(model.hparams.output_dir				)
				odir.mkdir(exist_ok=_UpperCamelCase				)

				# add custom checkpoints
				if checkpoint_callback is None:
								snake_case_		:					Optional[Any]			    =						pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir				,       prefix='''checkpoint'''				,       monitor='''val_loss'''				,       mode='''min'''				,       save_top_k=1				)
				if early_stopping_callback:
								extra_callbacks.append(_UpperCamelCase				)
				if logging_callback is None:
								snake_case_		:					List[Any]			    =						LoggingCallback()

				snake_case_		:					int			    =						{}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								snake_case_		:					List[str]			    =						16
								snake_case_		:					int			    =						args.fpaa_opt_level

				if args.gpus > 1:
								snake_case_		:					Any			    =						'''ddp'''

				snake_case_		:					int			    =						args.accumulate_grad_batches
				snake_case_		:					Union[str, Any]			    =						extra_train_kwargs.get('''accelerator'''				,       _UpperCamelCase				)
				snake_case_		:					Optional[Any]			    =						extra_train_kwargs.get('''profiler'''				,       _UpperCamelCase				)

				snake_case_		:					Optional[int]			    =						pl.Trainer.from_argparse_args(
				    _UpperCamelCase				,       weights_summary=_UpperCamelCase				,       callbacks=[logging_callback] + extra_callbacks				,       logger=_UpperCamelCase				,       checkpoint_callback=_UpperCamelCase				,       **_UpperCamelCase				,       )

				if args.do_train:
								trainer.fit(_UpperCamelCase				)

				return trainer












# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class       __lowerCAmelCase (    _a,  _a,  _a,  unittest.TestCase  ):
			lowerCamelCase_  :  List[str]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  List[Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  List[Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase_  :  Tuple     =   frozenset({'''control_image'''}  )  # skip `image` and `mask` for now, only test for control_image
			lowerCamelCase_  :  Any     =   TEXT_TO_IMAGE_IMAGE_PARAMS






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


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					List[Any]			    =						UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=9      ,							out_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,							cross_attention_dim=32      ,							)
							torch.manual_seed(0					)
							snake_case_		:					List[str]			    =						ControlNetModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
							torch.manual_seed(0					)
							snake_case_		:					int			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Any			    =						AutoencoderKL(
							    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,							latent_channels=4      ,							)
							torch.manual_seed(0					)
							snake_case_		:					int			    =						CLIPTextConfig(
							    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
							snake_case_		:					List[str]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Optional[int]			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							snake_case_		:					Any			    =						{
							    '''unet''': unet,
							    '''controlnet''': controlnet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__=0					)  ->     Any:


							'''simple docstring'''



							if str(__magic_name__					).startswith('''mps'''					):
											snake_case_		:					Union[str, Any]			    =						torch.manual_seed(__magic_name__					)
							else:
											snake_case_		:					str			    =						torch.Generator(device=__magic_name__					).manual_seed(__magic_name__					)

							snake_case_		:					int			    =						2
							snake_case_		:					Any			    =						randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							)
							snake_case_		:					List[Any]			    =						floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__magic_name__					)					).to(__magic_name__					)
							snake_case_		:					Tuple			    =						init_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]

							snake_case_		:					Optional[Any]			    =						Image.fromarray(np.uinta(__magic_name__					)					).convert('''RGB'''					).resize((64, 64)					)
							snake_case_		:					str			    =						Image.fromarray(np.uinta(init_image + 4					)					).convert('''RGB'''					).resize((64, 64)					)

							snake_case_		:					List[str]			    =						{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''mask_image''': mask_image,
							    '''control_image''': control_image,
							}

							return inputs






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()      ,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,							)
			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class       __lowerCAmelCase (    _a  ):
			lowerCamelCase_  :  Optional[int]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  Union[str, Any]     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  Optional[int]     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase_  :  Optional[int]     =   frozenset([]  )






			def     lowerCamelCase						(self					)  ->     Optional[int]:


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					int			    =						UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,							cross_attention_dim=32      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Union[str, Any]			    =						ControlNetModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Optional[int]			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Any			    =						AutoencoderKL(
							    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,							latent_channels=4      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Optional[int]			    =						CLIPTextConfig(
							    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
							snake_case_		:					Union[str, Any]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							snake_case_		:					int			    =						{
							    '''unet''': unet,
							    '''controlnet''': controlnet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




class       __lowerCAmelCase (    _a,  _a,  unittest.TestCase  ):
			lowerCamelCase_  :  Optional[int]     =   StableDiffusionControlNetInpaintPipeline
			lowerCamelCase_  :  Dict     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase_  :  str     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






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


							'''simple docstring'''



							torch.manual_seed(0					)
							snake_case_		:					Tuple			    =						UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=9      ,							out_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,							cross_attention_dim=32      ,							)
							torch.manual_seed(0					)

							def init_weights(__magic_name__					):
											if isinstance(__magic_name__      ,							torch.nn.Convad					):
															torch.nn.init.normal(m.weight					)
															m.bias.data.fill_(1.0					)

							snake_case_		:					Tuple			    =						ControlNetModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
							controlneta.controlnet_down_blocks.apply(__magic_name__					)

							torch.manual_seed(0					)
							snake_case_		:					str			    =						ControlNetModel(
							    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
							controlneta.controlnet_down_blocks.apply(__magic_name__					)

							torch.manual_seed(0					)
							snake_case_		:					Optional[Any]			    =						DDIMScheduler(
							    beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear'''      ,							clip_sample=__magic_name__      ,							set_alpha_to_one=__magic_name__      ,							)
							torch.manual_seed(0					)
							snake_case_		:					int			    =						AutoencoderKL(
							    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,							latent_channels=4      ,							)
							torch.manual_seed(0					)
							snake_case_		:					Dict			    =						CLIPTextConfig(
							    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
							snake_case_		:					Optional[int]			    =						CLIPTextModel(__magic_name__					)
							snake_case_		:					Dict			    =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

							snake_case_		:					str			    =						MultiControlNetModel([controlneta, controlneta]					)

							snake_case_		:					List[Any]			    =						{
							    '''unet''': unet,
							    '''controlnet''': controlnet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__=0					)  ->     str:


							'''simple docstring'''



							if str(__magic_name__					).startswith('''mps'''					):
											snake_case_		:					Dict			    =						torch.manual_seed(__magic_name__					)
							else:
											snake_case_		:					List[str]			    =						torch.Generator(device=__magic_name__					).manual_seed(__magic_name__					)

							snake_case_		:					Optional[int]			    =						2

							snake_case_		:					List[Any]			    =						[
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=__magic_name__      ,							device=torch.device(__magic_name__					)      ,							),
							]
							snake_case_		:					Any			    =						floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__magic_name__					)					).to(__magic_name__					)
							snake_case_		:					List[str]			    =						init_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]

							snake_case_		:					Union[str, Any]			    =						Image.fromarray(np.uinta(__magic_name__					)					).convert('''RGB'''					).resize((64, 64)					)
							snake_case_		:					List[str]			    =						Image.fromarray(np.uinta(init_image + 4					)					).convert('''RGB'''					).resize((64, 64)					)

							snake_case_		:					Dict			    =						{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''mask_image''': mask_image,
							    '''control_image''': control_image,
							}

							return inputs






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.get_dummy_components()
							snake_case_		:					List[Any]			    =						self.pipeline_class(**__magic_name__					)
							pipe.to(__magic_name__					)

							snake_case_		:					Optional[int]			    =						10.0
							snake_case_		:					Dict			    =						4

							snake_case_		:					Optional[int]			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Any			    =						steps
							snake_case_		:					Union[str, Any]			    =						scale
							snake_case_		:					List[Any]			    =						pipe(**__magic_name__					)[0]

							snake_case_		:					Optional[int]			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Any			    =						steps
							snake_case_		:					int			    =						scale
							snake_case_		:					List[Any]			    =						pipe(**__magic_name__      ,							control_guidance_start=0.1      ,							control_guidance_end=0.2					)[0]

							snake_case_		:					Any			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Optional[int]			    =						steps
							snake_case_		:					Tuple			    =						scale
							snake_case_		:					List[str]			    =						pipe(**__magic_name__      ,							control_guidance_start=[0.1, 0.3]      ,							control_guidance_end=[0.2, 0.7]					)[0]

							snake_case_		:					str			    =						self.get_dummy_inputs(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						steps
							snake_case_		:					Dict			    =						scale
							snake_case_		:					str			    =						pipe(**__magic_name__      ,							control_guidance_start=0.4      ,							control_guidance_end=[0.5, 0.8]					)[0]

							# make sure that all outputs are different
							assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
							assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
							assert np.sum(np.abs(output_a - output_a					)					) > 1e-3






			def     lowerCamelCase						(self					)  ->     Optional[int]:


							'''simple docstring'''



							return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()      ,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,							)
			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							self._test_inference_batch_single_identical(expected_max_diff=2e-3					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						self.get_dummy_components()
							snake_case_		:					Any			    =						self.pipeline_class(**__magic_name__					)
							pipe.to(__magic_name__					)
							pipe.set_progress_bar_config(disable=__magic_name__					)
							with tempfile.TemporaryDirectory() as tmpdir:
											try:
															# save_pretrained is not implemented for Multi-ControlNet
															pipe.save_pretrained(__magic_name__					)
											except NotImplementedError:
															pass




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






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



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






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''					)

							snake_case_		:					Tuple			    =						StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-inpainting'''      ,							safety_checker=__magic_name__      ,							controlnet=__magic_name__					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__magic_name__					)

							snake_case_		:					Optional[int]			    =						torch.Generator(device='''cpu'''					).manual_seed(0					)
							snake_case_		:					Optional[Any]			    =						load_image(
							    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''					).resize((512, 512)					)

							snake_case_		:					List[Any]			    =						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
							    '''/stable_diffusion_inpaint/input_bench_mask.png'''					).resize((512, 512)					)

							snake_case_		:					List[str]			    =						'''pitch black hole'''

							snake_case_		:					List[str]			    =						load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					).resize((512, 512)					)

							snake_case_		:					Dict			    =						pipe(
							    __magic_name__      ,							image=__magic_name__      ,							mask_image=__magic_name__      ,							control_image=__magic_name__      ,							generator=__magic_name__      ,							output_type='''np'''      ,							num_inference_steps=3      ,							)

							snake_case_		:					List[str]			    =						output.images[0]

							assert image.shape == (512, 512, 3)

							snake_case_		:					Optional[int]			    =						load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''					)

							assert np.abs(expected_image - image					).max() < 9e-2






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					List[str]			    =						ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''					)

							snake_case_		:					List[Any]			    =						StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''      ,							safety_checker=__magic_name__      ,							controlnet=__magic_name__					)
							snake_case_		:					Any			    =						DDIMScheduler.from_config(pipe.scheduler.config					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__magic_name__					)

							snake_case_		:					List[Any]			    =						torch.Generator(device='''cpu'''					).manual_seed(33					)

							snake_case_		:					str			    =						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''					)
							snake_case_		:					List[Any]			    =						init_image.resize((512, 512)					)

							snake_case_		:					Dict			    =						load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''					)
							snake_case_		:					List[Any]			    =						mask_image.resize((512, 512)					)

							snake_case_		:					int			    =						'''a handsome man with ray-ban sunglasses'''

							def make_inpaint_condition(__magic_name__      ,							__magic_name__					):
											snake_case_		:					int			    =						np.array(image.convert('''RGB'''					)					).astype(np.floataa					) / 255.0
											snake_case_		:					Optional[int]			    =						np.array(image_mask.convert('''L'''					)					).astype(np.floataa					) / 255.0

											assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
											snake_case_		:					Optional[int]			    =						-1.0  # set as masked pixel
											snake_case_		:					Dict			    =						np.expand_dims(__magic_name__      ,							0					).transpose(0      ,							3      ,							1      ,							2					)
											snake_case_		:					Optional[Any]			    =						torch.from_numpy(__magic_name__					)
											return image

							snake_case_		:					str			    =						make_inpaint_condition(__magic_name__      ,							__magic_name__					)

							snake_case_		:					Optional[Any]			    =						pipe(
							    __magic_name__      ,							image=__magic_name__      ,							mask_image=__magic_name__      ,							control_image=__magic_name__      ,							guidance_scale=9.0      ,							eta=1.0      ,							generator=__magic_name__      ,							num_inference_steps=20      ,							output_type='''np'''      ,							)
							snake_case_		:					List[str]			    =						output.images[0]

							assert image.shape == (512, 512, 3)

							snake_case_		:					List[Any]			    =						load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''					)

							assert np.abs(expected_image - image					).max() < 9e-2
