
'''simple docstring'''







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

import torch

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


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								super().__init__()
								self.register_modules(vqvae=lowerCAmelCase_					,     unet=lowerCAmelCase_					,     scheduler=lowerCAmelCase_						)




							@torch.no_grad()
							def __call__( self					,     lowerCAmelCase_ = 1					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = 0.0					,     lowerCAmelCase_ = 50					,     lowerCAmelCase_ = "pil"					,     lowerCAmelCase_ = True					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,     generator=lowerCAmelCase_					,     )
								_snake_case     		=	latents.to(self.device						)

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

								self.scheduler.set_timesteps(lowerCAmelCase_						)

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								_snake_case     		=	'eta' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

								_snake_case     		=	{}
								if accepts_eta:
									_snake_case     		=	eta

								for t in self.progress_bar(self.scheduler.timesteps						):
									_snake_case     		=	self.scheduler.scale_model_input(lowerCAmelCase_					,     lowerCAmelCase_						)
									# predict the noise residual
									_snake_case     		=	self.unet(lowerCAmelCase_					,     lowerCAmelCase_						).sample
									# compute the previous noisy sample x_t -> x_t-1
									_snake_case     		=	self.scheduler.step(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     **lowerCAmelCase_						).prev_sample

								# decode the image latents with the VAE
								_snake_case     		=	self.vqvae.decode(lowerCAmelCase_						).sample

								_snake_case     		=	(image / 2 + 0.5).clamp(0					,     1						)
								_snake_case     		=	image.cpu().permute(0					,     2					,     3					,     1						).numpy()
								if output_type == "pil":
									_snake_case     		=	self.numpy_to_pil(lowerCAmelCase_						)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_						)



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

