


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@slow
					@require_torch
					def   lowerCamelCase__					(self   :						List[str]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				, """prajjwal1/bert-tiny"""		)
							lowercase__					      =		BertTokenizer.from_pretrained("""bert-base-uncased"""		)

							lowercase__					      =		bertabert.config.encoder.vocab_size
							lowercase__					      =		tokenizer.sep_token_id
							lowercase__					      =		tokenizer.cls_token_id
							lowercase__					      =		128

							lowercase__					      =		datasets.load_dataset("""cnn_dailymail"""				, """3.0.0"""				, split="""train[:1%]"""		)
							lowercase__					      =		datasets.load_dataset("""cnn_dailymail"""				, """3.0.0"""				, split="""validation[:1%]"""		)

							lowercase__					      =		train_dataset.select(range(32		)		)
							lowercase__					      =		val_dataset.select(range(16		)		)

							lowercase__					      =		4

							def _map_to_encoder_decoder_inputs(_UpperCAmelCase   :						str		):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__					      =		tokenizer(batch["""article"""]				, padding="""max_length"""				, truncation=_UpperCAmelCase				, max_length=512		)
									lowercase__					      =		tokenizer(batch["""highlights"""]				, padding="""max_length"""				, truncation=_UpperCAmelCase				, max_length=128		)
									lowercase__					      =		inputs.input_ids
									lowercase__					      =		inputs.attention_mask

									lowercase__					      =		outputs.input_ids
									lowercase__					      =		outputs.input_ids.copy()
									lowercase__					      =		[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__					      =		outputs.attention_mask

									assert all(len(_UpperCAmelCase		) == 512 for x in inputs.input_ids		)
									assert all(len(_UpperCAmelCase		) == 128 for x in outputs.input_ids		)

									return batch

							def _compute_metrics(_UpperCAmelCase   :						Union[str, Any]		):
									lowercase__					      =		pred.label_ids
									lowercase__					      =		pred.predictions

									# all unnecessary tokens are removed
									lowercase__					      =		tokenizer.batch_decode(_UpperCAmelCase				, skip_special_tokens=_UpperCAmelCase		)
									lowercase__					      =		tokenizer.batch_decode(_UpperCAmelCase				, skip_special_tokens=_UpperCAmelCase		)

									lowercase__					      =		sum([int(pred_str[i] == label_str[i]		) for i in range(len(_UpperCAmelCase		)		)]		) / len(_UpperCAmelCase		)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__					      =		train_dataset.map(
							    _map_to_encoder_decoder_inputs				, batched=_UpperCAmelCase				, batch_size=_UpperCAmelCase				, remove_columns=["""article""", """highlights"""]				, )
							train_dataset.set_format(
							    type="""torch"""				, columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				, )

							# same for validation dataset
							lowercase__					      =		val_dataset.map(
							    _map_to_encoder_decoder_inputs				, batched=_UpperCAmelCase				, batch_size=_UpperCAmelCase				, remove_columns=["""article""", """highlights"""]				, )
							val_dataset.set_format(
							    type="""torch"""				, columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				, )

							lowercase__					      =		self.get_auto_remove_tmp_dir()

							lowercase__					      =		SeqaSeqTrainingArguments(
							    output_dir=_UpperCAmelCase				, per_device_train_batch_size=_UpperCAmelCase				, per_device_eval_batch_size=_UpperCAmelCase				, predict_with_generate=_UpperCAmelCase				, evaluation_strategy="""steps"""				, do_train=_UpperCAmelCase				, do_eval=_UpperCAmelCase				, warmup_steps=0				, eval_steps=2				, logging_steps=2				, )

							# instantiate trainer
							lowercase__					      =		SeqaSeqTrainer(
							    model=_UpperCAmelCase				, args=_UpperCAmelCase				, compute_metrics=_compute_metrics				, train_dataset=_UpperCAmelCase				, eval_dataset=_UpperCAmelCase				, tokenizer=_UpperCAmelCase				, )

							# start training
							trainer.train()





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

