
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(__a					,     'do_resize'						)						)
								self.assertTrue(hasattr(__a					,     'size_divisor'						)						)
								self.assertTrue(hasattr(__a					,     'resample'						)						)
								self.assertTrue(hasattr(__a					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=__a						)
								for image in image_inputs:
									self.assertIsInstance(__a					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=__a					,     numpify=__a						)
								for image in image_inputs:
									self.assertIsInstance(__a					,     np.ndarray						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=__a					,     torchify=__a						)
								for image in image_inputs:
									self.assertIsInstance(__a					,     torch.Tensor						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)



'''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))

