


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       :		Optional[Any] ,   _UpperCAmelCase       :		UNetaDModel ,   _UpperCAmelCase       :		ScoreSdeVeScheduler						)		->  Union[str, Any]:




							"""simple docstring"""


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


					@torch.no_grad()
					def __call__(self       :		List[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						)





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       :		int						)		->  Tuple:




							"""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       :		Any						):
									# 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       :		List[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()

