






"""simple docstring"""



import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


A__	:    Tuple			 = logging.getLogger(__name__)
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Any             =			"summarization"
			_UpperCAmelCase		:List[Any]             =			["loss"]
			_UpperCAmelCase		:str             =			ROUGE_KEYS
			_UpperCAmelCase		:Optional[int]             =			"rouge2"


			def __init__(				self					:  Tuple       ,	snake_case__					:  Dict       ,	**snake_case__					:  Optional[Any] ):
										if hparams.sortish_sampler and hparams.gpus > 1:
																	lowerCamelCase_	:							Union[str, Any]									=False
										elif hparams.max_tokens_per_batch is not None:
																	if hparams.gpus > 1:
																								raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training" )
																	if hparams.sortish_sampler:
																								raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously" )

										super().__init__(snake_case__       ,	num_labels=snake_case__       ,	mode=self.mode       ,	**snake_case__ )
										use_task_specific_params(self.model       ,	"summarization" )
										save_git_info(self.hparams.output_dir )
										lowerCamelCase_	:							Optional[int]									=Path(self.output_dir ) / "metrics.json"
										lowerCamelCase_	:							List[str]									=Path(self.output_dir ) / "hparams.pkl"
										pickle_save(self.hparams       ,	self.hparams_save_path )
										lowerCamelCase_	:							Optional[int]									=0
										lowerCamelCase_	:							Any									=defaultdict(snake_case__ )
										lowerCamelCase_	:							Dict									=self.config.model_type
										lowerCamelCase_	:							List[str]									=self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

										lowerCamelCase_	:							dict									={
										    "data_dir": self.hparams.data_dir,
										    "max_source_length": self.hparams.max_source_length,
										    "prefix": self.model.config.prefix or "",
										}
										lowerCamelCase_	:							int									={
										    "train": self.hparams.n_train,
										    "val": self.hparams.n_val,
										    "test": self.hparams.n_test,
										}
										lowerCamelCase_	:							str									={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

										lowerCamelCase_	:							Any									={
										    "train": self.hparams.max_target_length,
										    "val": self.hparams.val_max_target_length,
										    "test": self.hparams.test_max_target_length,
										}
										assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
										assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
										if self.hparams.freeze_embeds:
																	freeze_embeds(self.model )
										if self.hparams.freeze_encoder:
																	freeze_params(self.model.get_encoder() )
																	assert_all_frozen(self.model.get_encoder() )

										lowerCamelCase_	:							Optional[int]									=get_git_info()["repo_sha"]
										lowerCamelCase_	:							Any									=hparams.num_workers
										lowerCamelCase_	:							Any									=None  # default to config
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,	snake_case__ ):
																	lowerCamelCase_	:							Tuple									=self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	lowerCamelCase_	:							str									=self.decoder_start_token_id
										lowerCamelCase_	:							str									=(
										    SeqaSeqDataset if hasattr(self.tokenizer       ,	"prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
										)
										lowerCamelCase_	:							List[Any]									=False
										lowerCamelCase_	:							int									=self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
										if self.hparams.eval_max_gen_length is not None:
																	lowerCamelCase_	:							Tuple									=self.hparams.eval_max_gen_length
										else:
																	lowerCamelCase_	:							Any									=self.model.config.max_length
										lowerCamelCase_	:							str									=self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict[str, torch.Tensor] ):
										lowerCamelCase_	:							Any									={
										    k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
										}
										save_json(snake_case__       ,	Path(self.output_dir ) / "text_batch.json" )
										save_json({k: v.tolist() for k, v in batch.items()}       ,	Path(self.output_dir ) / "tok_batch.json" )

										lowerCamelCase_	:							str									=True
										return readable_batch


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Union[str, Any]       ,	**snake_case__					:  Optional[Any] ):
										return self.model(snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  List[int] ):
										lowerCamelCase_	:							Optional[int]									=self.tokenizer.batch_decode(
										    snake_case__       ,	skip_special_tokens=snake_case__       ,	clean_up_tokenization_spaces=snake_case__ )
										return lmap(str.strip       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  dict ):
										lowerCamelCase_	:							int									=self.tokenizer.pad_token_id
										lowerCamelCase_	:							List[str]									=batch["input_ids"], batch["attention_mask"]
										lowerCamelCase_	:							Any									=batch["labels"]
										if isinstance(self.model       ,	snake_case__ ):
																	lowerCamelCase_	:							List[Any]									=self.model._shift_right(snake_case__ )
										else:
																	lowerCamelCase_	:							Optional[Any]									=shift_tokens_right(snake_case__       ,	snake_case__ )
										if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	lowerCamelCase_	:							Any									=decoder_input_ids
																	self.save_readable_batch(snake_case__ )

										lowerCamelCase_	:							List[str]									=self(snake_case__       ,	attention_mask=snake_case__       ,	decoder_input_ids=snake_case__       ,	use_cache=snake_case__ )
										lowerCamelCase_	:							str									=outputs["logits"]
										if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	lowerCamelCase_	:							Tuple									=nn.CrossEntropyLoss(ignore_index=snake_case__ )

																	assert lm_logits.shape[-1] == self.vocab_size
																	lowerCamelCase_	:							List[str]									=ce_loss_fct(lm_logits.view(-1       ,	lm_logits.shape[-1] )       ,	tgt_ids.view(-1 ) )
										else:
																	lowerCamelCase_	:							Tuple									=nn.functional.log_softmax(snake_case__       ,	dim=-1 )
																	lowerCamelCase_	:							Dict									=label_smoothed_nll_loss(
																	    snake_case__       ,	snake_case__       ,	self.hparams.label_smoothing       ,	ignore_index=snake_case__ )
										return (loss,)


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return self.tokenizer.pad_token_id


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  int ):
										lowerCamelCase_	:							List[Any]									=self._step(snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=dict(zip(self.loss_names       ,	snake_case__ ) )
										# tokens per batch
										lowerCamelCase_	:							Union[str, Any]									=batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
										lowerCamelCase_	:							List[Any]									=batch["input_ids"].shape[0]
										lowerCamelCase_	:							List[str]									=batch["input_ids"].eq(self.pad ).sum()
										lowerCamelCase_	:							int									=batch["input_ids"].eq(self.pad ).float().mean()
										# TODO(SS): make a wandb summary metric for this
										return {"loss": loss_tensors[0], "log": logs}


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  List[str]       ,	snake_case__					:  int ):
										return self._generative_step(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[Any]="val" ):
										self.step_count += 1
										lowerCamelCase_	:							Union[str, Any]									={k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
										lowerCamelCase_	:							Any									=losses["loss"]
										lowerCamelCase_	:							int									={
										    k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
										}
										lowerCamelCase_	:							Optional[Any]									=(
										    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
										)
										lowerCamelCase_	:							torch.FloatTensor									=torch.tensor(snake_case__ ).type_as(snake_case__ )
										generative_metrics.update({k: v.item() for k, v in losses.items()} )
										losses.update(snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									={F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
										lowerCamelCase_	:							Optional[Any]									=self.step_count
										self.metrics[prefix].append(snake_case__ )  # callback writes this to self.metrics_save_path
										lowerCamelCase_	:							str									=flatten_list([x["preds"] for x in outputs] )
										return {
										    "log": all_metrics,
										    "preds": preds,
										    F"""{prefix}_loss""": loss,
										    F"""{prefix}_{self.val_metric}""": metric_tensor,
										}


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, Any] ):
										return calculate_rouge(snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  dict ):
										lowerCamelCase_	:							List[Any]									=time.time()

										# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
										lowerCamelCase_	:							Optional[int]									=self.model.generate(
										    batch["input_ids"]       ,	attention_mask=batch["attention_mask"]       ,	use_cache=snake_case__       ,	decoder_start_token_id=self.decoder_start_token_id       ,	num_beams=self.eval_beams       ,	max_length=self.eval_max_length       ,	)
										lowerCamelCase_	:							Optional[Any]									=(time.time() - ta) / batch["input_ids"].shape[0]
										lowerCamelCase_	:							List[str]									=self.ids_to_clean_text(snake_case__ )
										lowerCamelCase_	:							List[str]									=self.ids_to_clean_text(batch["labels"] )
										lowerCamelCase_	:							Optional[int]									=self._step(snake_case__ )
										lowerCamelCase_	:							List[Any]									=dict(zip(self.loss_names       ,	snake_case__ ) )
										lowerCamelCase_	:							Dict									=self.calc_generative_metrics(snake_case__       ,	snake_case__ )
										lowerCamelCase_	:							str									=np.mean(lmap(snake_case__       ,	snake_case__ ) )
										base_metrics.update(gen_time=snake_case__       ,	gen_len=snake_case__       ,	preds=snake_case__       ,	target=snake_case__       ,	**snake_case__ )
										return base_metrics


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  List[Any]       ,	snake_case__					:  str ):
										return self._generative_step(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Optional[Any] ):
										return self.validation_epoch_end(snake_case__       ,	prefix="test" )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  str ):
										lowerCamelCase_	:							str									=self.n_obs[type_path]
										lowerCamelCase_	:							Any									=self.target_lens[type_path]
										lowerCamelCase_	:							int									=self.dataset_class(
										    self.tokenizer       ,	type_path=snake_case__       ,	n_obs=snake_case__       ,	max_target_length=snake_case__       ,	**self.dataset_kwargs       ,	)
										return dataset


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str       ,	snake_case__					:  int       ,	snake_case__					:  bool = False ):
										lowerCamelCase_	:							Tuple									=self.get_dataset(snake_case__ )

										if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																	lowerCamelCase_	:							List[str]									=dataset.make_sortish_sampler(snake_case__       ,	distributed=self.hparams.gpus > 1 )
																	return DataLoader(
																	    snake_case__       ,	batch_size=snake_case__       ,	collate_fn=dataset.collate_fn       ,	shuffle=snake_case__       ,	num_workers=self.num_workers       ,	sampler=snake_case__       ,	)

										elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
																	lowerCamelCase_	:							str									=dataset.make_dynamic_sampler(
																	    self.hparams.max_tokens_per_batch       ,	distributed=self.hparams.gpus > 1 )
																	return DataLoader(
																	    snake_case__       ,	batch_sampler=snake_case__       ,	collate_fn=dataset.collate_fn       ,	num_workers=self.num_workers       ,	)
										else:
																	return DataLoader(
																	    snake_case__       ,	batch_size=snake_case__       ,	collate_fn=dataset.collate_fn       ,	shuffle=snake_case__       ,	num_workers=self.num_workers       ,	sampler=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Dict									=self.get_dataloader("train"       ,	batch_size=self.hparams.train_batch_size       ,	shuffle=snake_case__ )
										return dataloader


			def      UpperCAmelCase__						(				self					:  Tuple ):
										return self.get_dataloader("val"       ,	batch_size=self.hparams.eval_batch_size )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return self.get_dataloader("test"       ,	batch_size=self.hparams.eval_batch_size )


			@staticmethod
			def      UpperCAmelCase__						(				snake_case__					:  str       ,	snake_case__					:  List[str] ):
										BaseTransformer.add_model_specific_args(snake_case__       ,	snake_case__ )
										add_generic_args(snake_case__       ,	snake_case__ )
										parser.add_argument(
										    "--max_source_length"       ,	default=1024       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--max_target_length"       ,	default=56       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--val_max_target_length"       ,	default=142       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--test_max_target_length"       ,	default=142       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument("--freeze_encoder"       ,	action="store_true" )
										parser.add_argument("--freeze_embeds"       ,	action="store_true" )
										parser.add_argument("--sortish_sampler"       ,	action="store_true"       ,	default=snake_case__ )
										parser.add_argument("--overwrite_output_dir"       ,	action="store_true"       ,	default=snake_case__ )
										parser.add_argument("--max_tokens_per_batch"       ,	type=snake_case__       ,	default=snake_case__ )
										parser.add_argument("--logger_name"       ,	type=snake_case__       ,	choices=["default", "wandb", "wandb_shared"]       ,	default="default" )
										parser.add_argument("--n_train"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--n_val"       ,	type=snake_case__       ,	default=500       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--n_test"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument(
										    "--task"       ,	type=snake_case__       ,	default="summarization"       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--label_smoothing"       ,	type=snake_case__       ,	default=0.0       ,	required=snake_case__ )
										parser.add_argument("--src_lang"       ,	type=snake_case__       ,	default=""       ,	required=snake_case__ )
										parser.add_argument("--tgt_lang"       ,	type=snake_case__       ,	default=""       ,	required=snake_case__ )
										parser.add_argument("--eval_beams"       ,	type=snake_case__       ,	default=snake_case__       ,	required=snake_case__ )
										parser.add_argument(
										    "--val_metric"       ,	type=snake_case__       ,	default=snake_case__       ,	required=snake_case__       ,	choices=["bleu", "rouge2", "loss", None] )
										parser.add_argument("--eval_max_gen_length"       ,	type=snake_case__       ,	default=snake_case__       ,	help="never generate more than n tokens" )
										parser.add_argument("--save_top_k"       ,	type=snake_case__       ,	default=1       ,	required=snake_case__       ,	help="How many checkpoints to save" )
										parser.add_argument(
										    "--early_stopping_patience"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help=(
										        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
										        " val_check_interval will effect it."
										    )       ,	)
										return parser


class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[Any]             =			"translation"
			_UpperCAmelCase		:str             =			["loss"]
			_UpperCAmelCase		:Any             =			["bleu"]
			_UpperCAmelCase		:int             =			"bleu"


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  int       ,	**snake_case__					:  List[str] ):
										super().__init__(snake_case__       ,	**snake_case__ )
										lowerCamelCase_	:							Any									=hparams.src_lang
										lowerCamelCase_	:							Dict									=hparams.tgt_lang


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  int ):
										return calculate_bleu(snake_case__       ,	snake_case__ )


def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   int=None  )    ->     SummarizationModule:
							Path(args.output_dir  ).mkdir(exist_ok=lowerCamelCase__  )
							check_output_dir(lowerCamelCase__    ,						expected_items=3  )

							if model is None:
														if "summarization" in args.task:
																					lowerCamelCase_	:							SummarizationModule									=SummarizationModule(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							SummarizationModule									=TranslationModule(lowerCamelCase__  )
							lowerCamelCase_	:							Tuple									=Path(args.data_dir  ).name
							if (
							    args.logger_name == "default"
							    or args.fast_dev_run
							    or str(args.output_dir  ).startswith("/tmp"  )
							    or str(args.output_dir  ).startswith("/var"  )
							):
														lowerCamelCase_	:							Optional[Any]									=True  # don't pollute wandb logs unnecessarily
							elif args.logger_name == "wandb":
														from pytorch_lightning.loggers import WandbLogger

														lowerCamelCase_	:							List[str]									=os.environ.get("WANDB_PROJECT"    ,						lowerCamelCase__  )
														lowerCamelCase_	:							str									=WandbLogger(name=model.output_dir.name    ,						project=lowerCamelCase__  )

							elif args.logger_name == "wandb_shared":
														from pytorch_lightning.loggers import WandbLogger

														lowerCamelCase_	:							Union[str, Any]									=WandbLogger(name=model.output_dir.name    ,						project=F"""hf_{dataset}"""  )

							if args.early_stopping_patience >= 0:
														lowerCamelCase_	:							int									=get_early_stopping_callback(model.val_metric    ,						args.early_stopping_patience  )
							else:
														lowerCamelCase_	:							Optional[int]									=False

							lowerCamelCase_	:							int									=args.val_metric == "loss"
							lowerCamelCase_	:							pl.Trainer									=generic_train(
							    lowerCamelCase__    ,						lowerCamelCase__    ,						logging_callback=SeqaSeqLoggingCallback()    ,						checkpoint_callback=get_checkpoint_callback(
							        args.output_dir    ,						model.val_metric    ,						args.save_top_k    ,						lowerCamelCase__  )    ,						early_stopping_callback=lowerCamelCase__    ,						logger=lowerCamelCase__    ,						)
							pickle_save(model.hparams    ,						model.output_dir / "hparams.pkl"  )
							if not args.do_predict:
														return model

							lowerCamelCase_	:							List[str]									=""
							lowerCamelCase_	:							str									=sorted(glob.glob(os.path.join(args.output_dir    ,						"*.ckpt"  )    ,						recursive=lowerCamelCase__  )  )
							if checkpoints:
														lowerCamelCase_	:							List[Any]									=checkpoints[-1]
														lowerCamelCase_	:							Union[str, Any]									=checkpoints[-1]
							trainer.logger.log_hyperparams(model.hparams  )

							# test() without a model tests using the best checkpoint automatically
							trainer.test()
							return model


if __name__ == "__main__":
						A__	:    str			 = argparse.ArgumentParser()
						A__	:    Tuple			 = pl.Trainer.add_argparse_args(parser)
						A__	:    str			 = SummarizationModule.add_model_specific_args(parser, os.getcwd())

						A__	:    Any			 = parser.parse_args()

						main(args)











"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        lowercase__      (							unittest.TestCase       ):


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


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=1
										lowerCamelCase_	:							Union[str, Any]									=3
										lowerCamelCase_	:							Dict									=(32, 32)

										lowerCamelCase_	:							List[Any]									=floats_tensor((batch_size, num_channels) + sizes       ,	rng=random.Random(0 ) ).to(snake_case__ )
										return image


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Dict									=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       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Union[str, 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       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Any									=RobertaSeriesConfig(
										    hidden_size=32       ,	project_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=5006       ,	)
										return RobertaSeriesModelWithTransformation(snake_case__ )


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										def extract(*snake_case__					:  Dict       ,	**snake_case__					:  int ):







																	class        lowercase__      :


																				def __init__(				self					:  Optional[Any] ):
																											lowerCamelCase_	:							Union[str, Any]									=torch.ones([0] )


																				def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Any ):
																											self.pixel_values.to(snake_case__ )
																											return self


																	return Out()

										return extract


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Dict									="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							List[str]									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							List[Any]									=self.dummy_vae
										lowerCamelCase_	:							Any									=self.dummy_text_encoder
										lowerCamelCase_	:							List[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							List[str]									=77

										lowerCamelCase_	:							Optional[int]									=self.dummy_image.to(snake_case__ )
										lowerCamelCase_	:							Any									=init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							List[Any]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							int									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							Optional[int]									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Any									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Union[str, Any]									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							str									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	)

										lowerCamelCase_	:							List[Any]									=output.images

										lowerCamelCase_	:							str									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	return_dict=snake_case__       ,	)[0]

										lowerCamelCase_	:							Tuple									=image[0, -3:, -3:, -1]
										lowerCamelCase_	:							Union[str, Any]									=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_	:							List[str]									=np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							Any									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							Tuple									=self.dummy_vae
										lowerCamelCase_	:							Union[str, Any]									=self.dummy_text_encoder
										lowerCamelCase_	:							Optional[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							Tuple									=77

										lowerCamelCase_	:							str									=self.dummy_image.to(snake_case__ )

										# put models in fp16
										lowerCamelCase_	:							Optional[Any]									=unet.half()
										lowerCamelCase_	:							Dict									=vae.half()
										lowerCamelCase_	:							str									=bert.half()

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							Optional[int]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							List[Any]									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							int									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Tuple									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Tuple									=torch.manual_seed(0 )
										lowerCamelCase_	:							List[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	).images

										assert image.shape == (1, 32, 32, 3)


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							int									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowerCamelCase_	:							Any									=init_image.resize((760, 504) )

										lowerCamelCase_	:							List[str]									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[int]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							List[Any]									=torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[int]									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							Optional[int]									=output.images[0]

										lowerCamelCase_	:							Tuple									=image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										lowerCamelCase_	:							Optional[int]									=np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] )

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







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


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


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										lowerCamelCase_	:							Any									=init_image.resize((768, 512) )
										lowerCamelCase_	:							Optional[int]									=load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )

										lowerCamelCase_	:							Dict									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[Any]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							Dict									=torch.manual_seed(0 )
										lowerCamelCase_	:							int									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							int									=output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image ).max() < 1E-2



