






'''simple docstring'''







import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def     A__      (					__lowerCAmelCase		:      Dict    ):
						lowerCamelCase__							,      lowerCamelCase__								=	image.size
						lowerCamelCase__							,      lowerCamelCase__								=	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						lowerCamelCase__								=	image.resize((w, h) ,     resample=PIL_INTERPOLATION["""lanczos"""]    )
						lowerCamelCase__								=	np.array(__lowerCAmelCase    ).astype(np.floataa    ) / 255.0
						lowerCamelCase__								=	image[None].transpose(0 ,     3 ,     1 ,     2    )
						lowerCamelCase__								=	torch.from_numpy(__lowerCAmelCase    )
						return 2.0 * image - 1.0





class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								super().__init__()
								self.register_modules(vqvae=_lowerCAmelCase ,unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase							)






		@torch.no_grad()
		def __call__(   self ,_lowerCAmelCase = None ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 1_00 ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,):
								if isinstance(_lowerCAmelCase ,PIL.Image.Image							):
														lowerCamelCase__								=	1
								elif isinstance(_lowerCAmelCase ,torch.Tensor							):
														lowerCamelCase__								=	image.shape[0]
								else:
														raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_lowerCAmelCase							)}'''							)

								if isinstance(_lowerCAmelCase ,PIL.Image.Image							):
														lowerCamelCase__								=	preprocess(_lowerCAmelCase							)

								lowerCamelCase__							,      lowerCamelCase__								=	image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								lowerCamelCase__								=	(batch_size, self.unet.config.in_channels // 2, height, width)
								lowerCamelCase__								=	next(self.unet.parameters()							).dtype

								lowerCamelCase__								=	randn_tensor(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=self.device ,dtype=_lowerCAmelCase							)

								lowerCamelCase__								=	image.to(device=self.device ,dtype=_lowerCAmelCase							)

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(_lowerCAmelCase ,device=self.device							)
								lowerCamelCase__								=	self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCamelCase__								=	latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								lowerCamelCase__								=	"""eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
								lowerCamelCase__								=	{}
								if accepts_eta:
														lowerCamelCase__								=	eta

								for t in self.progress_bar(_lowerCAmelCase							):
														# concat latents and low resolution image in the channel dimension.
														lowerCamelCase__								=	torch.cat([latents, image] ,dim=1							)
														lowerCamelCase__								=	self.scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase							)
														# predict the noise residual
														lowerCamelCase__								=	self.unet(_lowerCAmelCase ,_lowerCAmelCase							).sample
														# compute the previous noisy sample x_t -> x_t-1
														lowerCamelCase__								=	self.scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample

								# decode the image latents with the VQVAE
								lowerCamelCase__								=	self.vqvae.decode(_lowerCAmelCase							).sample
								lowerCamelCase__								=	torch.clamp(_lowerCAmelCase ,-1.0 ,1.0							)
								lowerCamelCase__								=	image / 2 + 0.5
								lowerCamelCase__								=	image.cpu().permute(0 ,2 ,3 ,1							).numpy()

								if output_type == "pil":
														lowerCamelCase__								=	self.numpy_to_pil(_lowerCAmelCase							)

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=_lowerCAmelCase							)





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()

