
'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=13   ,       _lowercase=7   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=99   ,       _lowercase=32   ,       _lowercase=5   ,       _lowercase=4   ,       _lowercase=37   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=16   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=3   ,       _lowercase=4   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			seq_length
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_token_type_ids
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			type_sequence_label_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			num_choices
												_lowerCAmelCase  						=			scope
												_lowerCAmelCase  						=			self.vocab_size - 1




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size				)

												_lowerCAmelCase  						=			None
												if self.use_token_type_ids:
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size				)

												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												if self.use_labels:
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.type_sequence_label_size				)
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels				)
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.num_choices				)

												_lowerCAmelCase  						=			OpenAIGPTConfig(
												    vocab_size=self.vocab_size   ,       n_embd=self.hidden_size   ,       n_layer=self.num_hidden_layers   ,       n_head=self.num_attention_heads   ,       n_positions=self.max_position_embeddings   ,       pad_token_id=self.pad_token_id   ,       )

												_lowerCAmelCase  						=			ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,       2				)

												return (
												    config,
												    input_ids,
												    head_mask,
												    token_type_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       *_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OpenAIGPTModel(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()

												_lowerCAmelCase  						=			model(_lowercase   ,       token_type_ids=_lowercase   ,       head_mask=_lowercase				)
												_lowerCAmelCase  						=			model(_lowercase   ,       token_type_ids=_lowercase				)
												_lowerCAmelCase  						=			model(_lowercase				)

												self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       *_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OpenAIGPTLMHeadModel(_lowercase				)
												model.to(_lowercase				)
												model.eval()

												_lowerCAmelCase  						=			model(_lowercase   ,       token_type_ids=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.loss.shape   ,       ()				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       *_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OpenAIGPTDoubleHeadsModel(_lowercase				)
												model.to(_lowercase				)
												model.eval()

												_lowerCAmelCase  						=			model(_lowercase   ,       token_type_ids=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.loss.shape   ,       ()				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       *_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			OpenAIGPTForSequenceClassification(_lowercase				)
												model.to(_lowercase				)
												model.eval()

												_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.type_sequence_label_size				)
												_lowerCAmelCase  						=			model(_lowercase   ,       token_type_ids=_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												(
												    (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   
												)  						=			config_and_inputs
												_lowerCAmelCase  						=			{
												    """input_ids""": input_ids,
												    """token_type_ids""": token_type_ids,
												    """head_mask""": head_mask,
												}

												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       (
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						_lowercase		:				str									=       (
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						_lowercase		:				int									=       (
						    {
						        '''feature-extraction''': OpenAIGPTModel,
						        '''text-classification''': OpenAIGPTForSequenceClassification,
						        '''text-generation''': OpenAIGPTLMHeadModel,
						        '''zero-shot''': OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																		# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																		# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																		# tiny config could not be created.
																		return True

												return False




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase=False				):

												"""simple docstring"""



												_lowerCAmelCase  						=			super()._prepare_for_class(_lowercase   ,       _lowercase   ,       return_labels=_lowercase				)

												if return_labels:
																		if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																								_lowerCAmelCase  						=			torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)   ,       dtype=torch.long   ,       device=_lowercase   ,       )
																								_lowerCAmelCase  						=			inputs_dict["""labels"""]
																								_lowerCAmelCase  						=			inputs_dict["""labels"""]
																								_lowerCAmelCase  						=			torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.num_choices)   ,       dtype=torch.long   ,       device=_lowercase   ,       )
																								_lowerCAmelCase  						=			torch.zeros(
																								    self.model_tester.batch_size   ,       dtype=torch.long   ,       device=_lowercase				)
												return inputs_dict




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OpenAIGPTModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       n_embd=37				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_model(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_double_lm_head_model(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  						=			OpenAIGPTModel.from_pretrained(_lowercase				)
																		self.assertIsNotNone(_lowercase				)






@require_torch
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""				)
												model.to(_lowercase				)
												_lowerCAmelCase  						=			torch.tensor([[481, 4_735, 544]]   ,       dtype=torch.long   ,       device=_lowercase				)  # the president is
												_lowerCAmelCase  						=			[
												    481,
												    4_735,
												    544,
												    246,
												    963,
												    870,
												    762,
												    239,
												    244,
												    40_477,
												    244,
												    249,
												    719,
												    881,
												    487,
												    544,
												    240,
												    244,
												    603,
												    481,
												]  # the president is a very good man. " \n " i\'m sure he is, " said the

												_lowerCAmelCase  						=			model.generate(_lowercase   ,       do_sample=_lowercase				)
												self.assertListEqual(output_ids[0].tolist()   ,       _lowercase				)


'''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_					:






						'''simple docstring'''






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

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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

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

												_lowerCAmelCase  						=			self.get_config()

												return config, pixel_values, labels




						def  _lowercase     ( 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  _lowercase     ( self				):

												"""simple docstring"""



												return UperNetConfig(
												    backbone_config=self.get_backbone_config()   ,       hidden_size=512   ,       pool_scales=[1, 2, 3, 6]   ,       use_auxiliary_head=_lowercase   ,       auxiliary_loss_weight=0.4   ,       auxiliary_in_channels=40   ,       auxiliary_channels=256   ,       auxiliary_num_convs=1   ,       auxiliary_concat_input=_lowercase   ,       loss_ignore_index=255   ,       num_labels=self.num_labels   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												(
												    (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   
												)  						=			config_and_inputs
												_lowerCAmelCase  						=			{"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       (UperNetForSemanticSegmentation,) if is_torch_available() else ()
						_lowercase		:				Dict									=       {'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
						_lowercase		:				Dict									=       False
						_lowercase		:				Optional[Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Union[str, Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				List[Any]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UperNetModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       has_text_modality=_lowercase   ,       hidden_size=37				)




						def  _lowercase     ( 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  _lowercase     ( self				):

												"""simple docstring"""



												return




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																		_lowerCAmelCase  						=			["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase				)




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		model.to(_lowercase				)
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**self._prepare_for_class(_lowercase   ,       _lowercase				)				)

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

																		_lowerCAmelCase  						=			self.model_tester.num_stages
																		self.assertEqual(len(_lowercase				)   ,       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]   ,       )

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			True
																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)

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

																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												_lowerCAmelCase  						=			_config_zero_init(_lowercase				)
												_lowerCAmelCase  						=			_config_zero_init(configs_no_init.backbone_config				)
												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(config=_lowercase				)
																		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  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



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






def A				():
						_lowerCAmelCase  						=			hf_hub_download(
						    repo_id="""hf-internal-testing/fixtures_ade20k"""			,     repo_type="""dataset"""			,     filename="""ADE_val_00000001.jpg"""		)
						_lowerCAmelCase  						=			Image.open(__lowerCamelCase		).convert("""RGB"""		)
						return image





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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

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