
'''simple docstring'''







import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import UperNetForSemanticSegmentation
					from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=32					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=[10, 20, 30, 40]					,     lowerCAmelCase_=[2, 2, 3, 2]					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=10					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=["stage2", "stage3", "stage4"]					,     lowerCAmelCase_=3					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	image_size
								_snake_case     		=	num_channels
								_snake_case     		=	num_stages
								_snake_case     		=	hidden_sizes
								_snake_case     		=	depths
								_snake_case     		=	is_training
								_snake_case     		=	use_labels
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	out_features
								_snake_case     		=	num_labels
								_snake_case     		=	scope
								_snake_case     		=	num_stages


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								_snake_case     		=	None
								if self.use_labels:
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)

								_snake_case     		=	self.get_config()

								return config, pixel_values, labels


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return ConvNextConfig(
								    num_channels=self.num_channels					,     num_stages=self.num_stages					,     hidden_sizes=self.hidden_sizes					,     depths=self.depths					,     is_training=self.is_training					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     out_features=self.out_features					,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return UperNetConfig(
								    backbone_config=self.get_backbone_config()					,     hidden_size=5_12					,     pool_scales=[1, 2, 3, 6]					,     use_auxiliary_head=lowerCAmelCase_					,     auxiliary_loss_weight=0.4					,     auxiliary_in_channels=40					,     auxiliary_channels=2_56					,     auxiliary_num_convs=1					,     auxiliary_concat_input=lowerCAmelCase_					,     loss_ignore_index=2_55					,     num_labels=self.num_labels					,     )


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	UperNetForSemanticSegmentation(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()
								_snake_case     		=	model(lowerCAmelCase_						)
								self.parent.assertEqual(
								    result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size, self.image_size)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				
								)     		=	config_and_inputs
								_snake_case     		=	{'pixel_values': pixel_values}
								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (UperNetForSemanticSegmentation,) if is_torch_available() else ()
							__lowercase   	= {"""image-segmentation""": UperNetForSemanticSegmentation} if is_torch_available() else {}
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	UperNetModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     has_text_modality=lowerCAmelCase_					,     hidden_size=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_snake_case     		=	model_class(lowerCAmelCase_						)
									_snake_case     		=	inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_snake_case     		=	[*signature.parameters.keys()]

									_snake_case     		=	['pixel_values']
									self.assertListEqual(arg_names[:1]					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_						)


							@unittest.skip(reason='UperNet does not use inputs_embeds'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@unittest.skip(reason='UperNet does not support input and output embeddings'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@unittest.skip(reason='UperNet does not have a base model'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@unittest.skip(reason='UperNet does not have a base model'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@require_torch_multi_gpu
							@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								def check_hidden_states_output(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):
									_snake_case     		=	model_class(lowerCAmelCase_						)
									model.to(lowerCAmelCase_						)
									model.eval()

									with torch.no_grad():
										_snake_case     		=	model(**self._prepare_for_class(lowerCAmelCase_					,     lowerCAmelCase_						)						)

									_snake_case     		=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									_snake_case     		=	self.model_tester.num_stages
									self.assertEqual(len(lowerCAmelCase_						)					,     expected_num_stages + 1						)

									# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]						)					,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,     )

								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_snake_case     		=	True
									check_hidden_states_output(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									_snake_case     		=	True

									check_hidden_states_output(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								_snake_case     		=	_config_zero_init(lowerCAmelCase_						)
								_snake_case     		=	_config_zero_init(configs_no_init.backbone_config						)
								for model_class in self.all_model_classes:
									_snake_case     		=	model_class(config=lowerCAmelCase_						)
									for name, param in model.named_parameters():
										if param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()					,     [0.0, 1.0]					,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,     )


							@unittest.skip(reason='UperNet does not have tied weights'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			Tuple:
	_snake_case     		=	hf_hub_download(
	    repo_id='hf-internal-testing/fixtures_ade20k'    ,       repo_type='dataset'    ,       filename='ADE_val_00000001.jpg'			)
	_snake_case     		=	Image.open(__A			).convert('RGB'			)
	return image





@require_torch
@require_vision
@slow
class 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'						)
								_snake_case     		=	UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'						).to(lowerCAmelCase_						)

								_snake_case     		=	prepare_img()
								_snake_case     		=	processor(images=lowerCAmelCase_					,     return_tensors='pt'						).to(lowerCAmelCase_						)

								with torch.no_grad():
									_snake_case     		=	model(**lowerCAmelCase_						)

								_snake_case     		=	torch.Size((1, model.config.num_labels, 5_12, 5_12)						)
								self.assertEqual(outputs.logits.shape					,     lowerCAmelCase_						)

								_snake_case     		=	torch.tensor(
								    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]						).to(lowerCAmelCase_						)
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,     lowerCAmelCase_					,     atol=1E-4						)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'						)
								_snake_case     		=	UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'						).to(lowerCAmelCase_						)

								_snake_case     		=	prepare_img()
								_snake_case     		=	processor(images=lowerCAmelCase_					,     return_tensors='pt'						).to(lowerCAmelCase_						)

								with torch.no_grad():
									_snake_case     		=	model(**lowerCAmelCase_						)

								_snake_case     		=	torch.Size((1, model.config.num_labels, 5_12, 5_12)						)
								self.assertEqual(outputs.logits.shape					,     lowerCAmelCase_						)

								_snake_case     		=	torch.tensor(
								    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]						).to(lowerCAmelCase_						)
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,     lowerCAmelCase_					,     atol=1E-4						)						)



'''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))

