
"""simple docstring"""







import torch

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


lowerCamelCase__   					=		CamembertTokenizer.from_pretrained("""camembert-base""")
lowerCamelCase__   					=		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

lowerCamelCase__   					=		"""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     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		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   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		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   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		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=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		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     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

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



