

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__=5	):





	'''simple docstring'''
	assert masked_input.count('<mask>'	) == 1
	snake_case_      		= torch.tensor(tokenizer.encode(lowercase__			, add_special_tokens=lowercase__	)	).unsqueeze(0	)  # Batch size 1
	snake_case_      		= model(lowercase__	)[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=lowercase__			, dim=0	)
	snake_case_      		= ' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(lowercase__	)	)]	)
	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(lowercase__	) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(lowercase__	)			, lowercase__	),
			        values[index].item(),
			        predicted_token,
			    )	)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(lowercase__			, lowercase__	),
			        values[index].item(),
			        predicted_token,
			    )	)
	return topk_filled_outputs


A											=     CamembertTokenizer.from_pretrained('camembert-base')
A											=     CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

A											=     'Le camembert est <mask> :)'






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



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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