

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





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





	'''simple docstring'''
	# 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(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 transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
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


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= VQModel(
				    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPTextConfig(
				    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
				return CLIPTextModel(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= DDIMScheduler()
				snake_case_      		= self.dummy_vq_model

				snake_case_      		= LDMPipeline(unet=__UpperCamelCase ,				vqvae=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

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

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

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

				assert image.shape == (1, 64, 64, 3)
				snake_case_      		= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







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






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=5 ,				output_type='numpy'							).images

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

				assert image.shape == (1, 2_56, 2_56, 3)
				snake_case_      		= np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
