
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


__SCREAMING_SNAKE_CASE			:   Tuple         =     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__SCREAMING_SNAKE_CASE			:   Dict         =     {
    '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
__SCREAMING_SNAKE_CASE			:   Any         =     {
    '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
}
__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     sorted(arg_to_scheduler.keys())
__SCREAMING_SNAKE_CASE			:   Any         =     '{' + ', '.join(arg_to_scheduler_choices) + '}'


class 		A   (						pl.LightningModule    ):





		def __init__(							self    :	int						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	Any=None						,  lowerCamelCase__    :	Dict="base"						,  lowerCamelCase__    :	List[Any]=None						,  lowerCamelCase__    :	str=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  **lowerCamelCase__    :	str						,  ):
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(lowercase_				)
							lowerCAmelCase		:					str  	=     0
							lowerCAmelCase		:					Union[str, Any]  	=     Path(self.hparams.output_dir				)
							lowerCAmelCase		:					Dict  	=     self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
												lowerCAmelCase		:					Optional[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=lowercase_						,  **lowercase_						,  )
							else:
												lowerCAmelCase		:					PretrainedConfig  	=     config

							lowerCAmelCase		:					Optional[int]  	=     ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
							for p in extra_model_params:
												if getattr(self.hparams						,  lowercase_						,  lowercase_				):
																	assert hasattr(self.config						,  lowercase_				), f'''model config doesn\'t have a `{p}` attribute'''
																	setattr(self.config						,  lowercase_						,  getattr(self.hparams						,  lowercase_				)				)

							if tokenizer is None:
												lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,  cache_dir=lowercase_						,  )
							else:
												lowerCAmelCase		:					PreTrainedTokenizer  	=     tokenizer
							lowerCAmelCase		:					Optional[Any]  	=     MODEL_MODES[mode]
							if model is None:
												lowerCAmelCase		:					int  	=     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=lowercase_						,  )
							else:
												lowerCAmelCase		:					Tuple  	=     model





		def       _A							(							self    :	int						,  *lowerCamelCase__    :	Union[str, Any]						,  **lowerCamelCase__    :	Optional[int]				):
							lowerCAmelCase		:					Optional[int]  	=     self.model_type.from_pretrained(*lowercase_						,  **lowercase_				)





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Tuple  	=     arg_to_scheduler[self.hparams.lr_scheduler]
							lowerCAmelCase		:					Tuple  	=     get_schedule_func(
							    self.opt						,  num_warmup_steps=self.hparams.warmup_steps						,  num_training_steps=self.total_steps()				)
							lowerCAmelCase		:					Optional[Any]  	=     {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
							return scheduler





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					Tuple  	=     self.model
							lowerCAmelCase		:					str  	=     ['''bias''', '''LayerNorm.weight''']
							lowerCAmelCase		:					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:
												lowerCAmelCase		:					List[Any]  	=     Adafactor(
												    lowercase_						,  lr=self.hparams.learning_rate						,  scale_parameter=lowercase_						,  relative_step=lowercase_				)

							else:
												lowerCAmelCase		:					Optional[int]  	=     AdamW(
												    lowercase_						,  lr=self.hparams.learning_rate						,  eps=self.hparams.adam_epsilon				)
							lowerCAmelCase		:					str  	=     optimizer

							lowerCAmelCase		:					int  	=     self.get_lr_scheduler()

							return [optimizer], [scheduler]





		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	Union[str, Any]				):
							return self.validation_step(lowercase_						,  lowercase_				)





		def       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	List[Any]				):
							return self.validation_end(lowercase_				)





		def       _A							(							self    :	int				):
							lowerCAmelCase		:					Union[str, Any]  	=     max(1						,  self.hparams.gpus				)  # TODO: consider num_tpu_cores
							lowerCAmelCase		:					Optional[int]  	=     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





		def       _A							(							self    :	Optional[int]						,  lowerCamelCase__    :	Optional[int]				):

							if mode == "test":
												lowerCAmelCase		:					Optional[int]  	=     len(self.test_dataloader().dataset				)
							else:
												lowerCAmelCase		:					List[str]  	=     self.get_dataloader('''train'''						,  self.hparams.train_batch_size						,  shuffle=lowercase_				)
												lowerCAmelCase		:					Dict  	=     len(self.train_dataloader().dataset				)





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	Any						,  lowerCamelCase__    :	List[Any] = False				):
							raise NotImplementedError('''You must implement this for your task'''				)





		def       _A							(							self    :	List[Any]				):
							return self.train_loader





		def       _A							(							self    :	List[Any]				):
							return self.get_dataloader('''dev'''						,  self.hparams.eval_batch_size						,  shuffle=lowercase_				)





		def       _A							(							self    :	int				):
							return self.get_dataloader('''test'''						,  self.hparams.eval_batch_size						,  shuffle=lowercase_				)





		def       _A							(							self    :	str						,  lowerCamelCase__    :	Dict				):
							return os.path.join(
							    self.hparams.data_dir						,  '''cached_{}_{}_{}'''.format(
							        lowercase_						,  list(filter(lowercase_						,  self.hparams.model_name_or_path.split('''/'''				)				)				).pop()						,  str(self.hparams.max_seq_length				)						,  )						,  )





		@pl.utilities.rank_zero_only
		def       _A							(							self    :	Tuple						,  lowerCamelCase__    :	int				):
							lowerCAmelCase		:					List[str]  	=     self.output_dir.joinpath('''best_tfmr'''				)
							lowerCAmelCase		:					Optional[int]  	=     self.step_count
							self.model.save_pretrained(lowercase_				)
							self.tokenizer.save_pretrained(lowercase_				)





		@staticmethod
		def       _A							(							lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	Any				):
							parser.add_argument(
							    '''--model_name_or_path'''						,  default=lowercase_						,  type=lowercase_						,  required=lowercase_						,  help='''Path to pretrained model or model identifier from huggingface.co/models'''						,  )
							parser.add_argument(
							    '''--config_name'''						,  default=''''''						,  type=lowercase_						,  help='''Pretrained config name or path if not the same as model_name'''				)
							parser.add_argument(
							    '''--tokenizer_name'''						,  default=lowercase_						,  type=lowercase_						,  help='''Pretrained tokenizer name or path if not the same as model_name'''						,  )
							parser.add_argument(
							    '''--cache_dir'''						,  default=''''''						,  type=lowercase_						,  help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''						,  )
							parser.add_argument(
							    '''--encoder_layerdrop'''						,  type=lowercase_						,  help='''Encoder layer dropout probability (Optional). Goes into model.config'''						,  )
							parser.add_argument(
							    '''--decoder_layerdrop'''						,  type=lowercase_						,  help='''Decoder layer dropout probability (Optional). Goes into model.config'''						,  )
							parser.add_argument(
							    '''--dropout'''						,  type=lowercase_						,  help='''Dropout probability (Optional). Goes into model.config'''						,  )
							parser.add_argument(
							    '''--attention_dropout'''						,  type=lowercase_						,  help='''Attention dropout probability (Optional). Goes into model.config'''						,  )
							parser.add_argument('''--learning_rate'''						,  default=5E-5						,  type=lowercase_						,  help='''The initial learning rate for Adam.'''				)
							parser.add_argument(
							    '''--lr_scheduler'''						,  default='''linear'''						,  choices=lowercase_						,  metavar=lowercase_						,  type=lowercase_						,  help='''Learning rate scheduler'''						,  )
							parser.add_argument('''--weight_decay'''						,  default=0.0						,  type=lowercase_						,  help='''Weight decay if we apply some.'''				)
							parser.add_argument('''--adam_epsilon'''						,  default=1E-8						,  type=lowercase_						,  help='''Epsilon for Adam optimizer.'''				)
							parser.add_argument('''--warmup_steps'''						,  default=0						,  type=lowercase_						,  help='''Linear warmup over warmup_steps.'''				)
							parser.add_argument('''--num_workers'''						,  default=4						,  type=lowercase_						,  help='''kwarg passed to DataLoader'''				)
							parser.add_argument('''--num_train_epochs'''						,  dest='''max_epochs'''						,  default=3						,  type=lowercase_				)
							parser.add_argument('''--train_batch_size'''						,  default=3_2						,  type=lowercase_				)
							parser.add_argument('''--eval_batch_size'''						,  default=3_2						,  type=lowercase_				)
							parser.add_argument('''--adafactor'''						,  action='''store_true'''				)



class 		A   (						pl.Callback    ):





		def       _A							(							self    :	str						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Tuple  	=     trainer.lr_schedulers[0]['''scheduler''']
							lowerCAmelCase		:					Optional[Any]  	=     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
							pl_module.logger.log_metrics(lowercase_				)





		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	List[Any]				):
							rank_zero_info('''***** Validation results *****'''				)
							lowerCAmelCase		:					Dict  	=     trainer.callback_metrics
							# Log results
							for key in sorted(lowercase_				):
												if key not in ["log", "progress_bar"]:
																	rank_zero_info('''{} = {}\n'''.format(lowercase_						,  str(metrics[key]				)				)				)





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



def     UpperCAmelCase__							(       __magic_name__						:     List[Any]							,  __magic_name__						:     int  ):


					'''simple docstring'''

					parser.add_argument(
					    '''--output_dir'''							,  default=lowercase_							,  type=lowercase_							,  required=lowercase_							,  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=lowercase_							,  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=lowercase_  )
					parser.add_argument('''--max_grad_norm'''							,  dest='''gradient_clip_val'''							,  default=1.0							,  type=lowercase_							,  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=lowercase_							,  default=1							,  help='''Number of updates steps to accumulate before performing a backward/update pass.'''							,  )
					parser.add_argument('''--seed'''							,  type=lowercase_							,  default=42							,  help='''random seed for initialization'''  )
					parser.add_argument(
					    '''--data_dir'''							,  default=lowercase_							,  type=lowercase_							,  required=lowercase_							,  help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''							,  )
def     UpperCAmelCase__							(       __magic_name__						:     List[str]							,  __magic_name__						:     Optional[int]							,  __magic_name__						:     Union[str, Any]=None							,  __magic_name__						:     str=True							,  __magic_name__						:     Optional[Any]=[]							,  __magic_name__						:     int=None							,  __magic_name__						:     List[str]=None							,  **__magic_name__						:     Any							,  ):


					'''simple docstring'''

					pl.seed_everything(args.seed  )

					# init model
					lowerCAmelCase		:					Optional[Any]  	=     Path(model.hparams.output_dir  )
					odir.mkdir(exist_ok=lowercase_  )

					# add custom checkpoints
					if checkpoint_callback is None:
										lowerCAmelCase		:					List[str]  	=     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(lowercase_  )
					if logging_callback is None:
										lowerCAmelCase		:					Optional[Any]  	=     LoggingCallback()

					lowerCAmelCase		:					Dict  	=     {}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										lowerCAmelCase		:					str  	=     16
										lowerCAmelCase		:					Any  	=     args.fpaa_opt_level

					if args.gpus > 1:
										lowerCAmelCase		:					str  	=     '''ddp'''

					lowerCAmelCase		:					Any  	=     args.accumulate_grad_batches
					lowerCAmelCase		:					Any  	=     extra_train_kwargs.get('''accelerator'''							,  lowercase_  )
					lowerCAmelCase		:					Optional[int]  	=     extra_train_kwargs.get('''profiler'''							,  lowercase_  )

					lowerCAmelCase		:					Any  	=     pl.Trainer.from_argparse_args(
					    lowercase_							,  weights_summary=lowercase_							,  callbacks=[logging_callback] + extra_callbacks							,  logger=lowercase_							,  checkpoint_callback=lowercase_							,  **lowercase_							,  )

					if args.do_train:
										trainer.fit(lowercase_  )

					return trainer


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	PaintByExamplePipeline
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase		=	frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





		def       _A							(							self    :	Dict				):
							torch.manual_seed(0				)
							lowerCAmelCase		:					Optional[int]  	=     UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)						,  layers_per_block=2						,  sample_size=3_2						,  in_channels=9						,  out_channels=4						,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,  cross_attention_dim=3_2						,  )
							lowerCAmelCase		:					List[str]  	=     PNDMScheduler(skip_prk_steps=lowerCamelCase__				)
							torch.manual_seed(0				)
							lowerCAmelCase		:					List[str]  	=     AutoencoderKL(
							    block_out_channels=[3_2, 6_4]						,  in_channels=3						,  out_channels=3						,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,  latent_channels=4						,  )
							torch.manual_seed(0				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPVisionConfig(
							    hidden_size=3_2						,  projection_dim=3_2						,  intermediate_size=3_7						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  image_size=3_2						,  patch_size=4						,  )
							lowerCAmelCase		:					List[Any]  	=     PaintByExampleImageEncoder(lowerCamelCase__						,  proj_size=3_2				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPImageProcessor(crop_size=3_2						,  size=3_2				)

							lowerCAmelCase		:					str  	=     {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''image_encoder''': image_encoder,
							    '''safety_checker''': None,
							    '''feature_extractor''': feature_extractor,
							}
							return components





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     np.array(image.convert('''RGB'''				)				)
							lowerCAmelCase		:					int  	=     image[None].transpose(0						,  3						,  1						,  2				)
							lowerCAmelCase		:					Any  	=     torch.from_numpy(lowerCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
							return image





		def       _A							(							self    :	str						,  lowerCamelCase__    :	Union[str, Any]="cpu"						,  lowerCamelCase__    :	Any=0				):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							lowerCAmelCase		:					str  	=     floats_tensor((1, 3, 3_2, 3_2)						,  rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
							lowerCAmelCase		:					Any  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					Dict  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					Optional[int]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((3_2, 3_2)				)

							if str(lowerCamelCase__				).startswith('''mps'''				):
												lowerCAmelCase		:					Dict  	=     torch.manual_seed(lowerCamelCase__				)
							else:
												lowerCAmelCase		:					str  	=     torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''example_image''': example_image,
							    '''image''': init_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs





		def       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline(**lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     pipe.to('''cpu'''				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     self.get_dummy_inputs()
							lowerCAmelCase		:					List[str]  	=     pipe(**lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     output.images

							lowerCAmelCase		:					Optional[int]  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase		:					List[Any]  	=     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]				)

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





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[str]  	=     '''cpu'''
							lowerCAmelCase		:					Union[str, Any]  	=     self.get_dummy_inputs()
							inputs.pop('''mask_image'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.convert_to_pt(inputs.pop('''image'''				)				)
							lowerCAmelCase		:					Dict  	=     image.clamp(0						,  1				) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					str  	=     PaintByExamplePipeline(**self.get_dummy_components()				)
							lowerCAmelCase		:					Tuple  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     pipe(image=lowerCamelCase__						,  mask_image=mask_image[:, 0]						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     output.images

							lowerCAmelCase		:					int  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					int  	=     mask_image.cpu().permute(0						,  2						,  3						,  1				)[0]

							lowerCAmelCase		:					List[str]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)

							lowerCAmelCase		:					List[Any]  	=     pipe(**self.get_dummy_inputs()				)
							lowerCAmelCase		:					Any  	=     output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()				).max() < 5E-2





		def       _A							(							self    :	Optional[Any]				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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





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





		def       _A							(							self    :	Optional[int]				):
							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/dog_in_bucket.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/mask.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/panda.jpg'''				)

							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''				)
							lowerCAmelCase		:					str  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					int  	=     torch.manual_seed(3_2_1				)
							lowerCAmelCase		:					List[str]  	=     pipe(
							    image=lowerCamelCase__						,  mask_image=lowerCamelCase__						,  example_image=lowerCamelCase__						,  generator=lowerCamelCase__						,  guidance_scale=5.0						,  num_inference_steps=5_0						,  output_type='''np'''						,  )

							lowerCAmelCase		:					Any  	=     output.images

							lowerCAmelCase		:					Tuple  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:					Dict  	=     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]				)

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