

'''simple docstring'''




import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import WhisperFeatureExtractor

if is_torch_available():
				import torch

lowerCAmelCase_							=	random.Random()
def       A__	(							A  :     Optional[int]							,       A  :     int=1.0							,       A  :     Any=None							,       A  :     Optional[Any]=None):


			'''simple docstring'''


			if rng is None:
						UpperCamelCase				:	Any    						=					global_rng

			UpperCamelCase				:	List[str]    						=					[]
			for batch_idx in range(shape[0]):
						values.append([])
						for _ in range(shape[1]):
									values[-1].append(rng.random() * scale)

			return values

@require_torch
@require_torchaudio
class     UpperCAmelCase_ (	unittest.TestCase    ):



	"""simple docstring"""





	def __init__(    self				,			lowerCamelCase				,			lowerCamelCase=7				,			lowerCamelCase=4_00				,			lowerCamelCase=20_00				,			lowerCamelCase=10				,			lowerCamelCase=1_60				,			lowerCamelCase=8				,			lowerCamelCase=0.0				,			lowerCamelCase=40_00				,			lowerCamelCase=False				,			lowerCamelCase=True				,			)							->     List[Any]:




				'''simple docstring'''






				UpperCamelCase				:	str    						=					parent
				UpperCamelCase				:	int    						=					batch_size
				UpperCamelCase				:	Tuple    						=					min_seq_length
				UpperCamelCase				:	Any    						=					max_seq_length
				UpperCamelCase				:	List[str]    						=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCamelCase				:	Any    						=					padding_value
				UpperCamelCase				:	List[str]    						=					sampling_rate
				UpperCamelCase				:	Union[str, Any]    						=					return_attention_mask
				UpperCamelCase				:	Optional[Any]    						=					do_normalize
				UpperCamelCase				:	List[str]    						=					feature_size
				UpperCamelCase				:	Optional[Any]    						=					chunk_length
				UpperCamelCase				:	str    						=					hop_length

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Optional[Any]:




				'''simple docstring'''






				return {
				    "feature_size": self.feature_size,
				    "hop_length": self.hop_length,
				    "chunk_length": self.chunk_length,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}



	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase=False				,			lowerCamelCase=False   )							->     List[Any]:




				'''simple docstring'''






				def _flatten(lowerCamelCase   ):
							return list(itertools.chain(*A_   )   )

				if equal_length:
							UpperCamelCase				:	List[str]    						=					[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
				else:
							# make sure that inputs increase in size
							UpperCamelCase				:	Union[str, Any]    						=					[
							    floats_list((x, self.feature_size)   )
							    for x in range(self.min_seq_length				,			self.max_seq_length				,			self.seq_length_diff   )
							]
				if numpify:
							UpperCamelCase				:	str    						=					[np.asarray(A_   ) for x in speech_inputs]
				return speech_inputs

@require_torch
@require_torchaudio
class     UpperCAmelCase_ (	SCREAMING_SNAKE_CASE__				,	unittest.TestCase    ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE					=       WhisperFeatureExtractor if is_speech_available() else None

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Dict:




				'''simple docstring'''






				UpperCamelCase				:	int    						=					WhisperFeatureExtractionTester(self   )

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Tuple:




				'''simple docstring'''






				UpperCamelCase				:	Union[str, Any]    						=					self.feature_extraction_class(**self.feat_extract_dict   )

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCamelCase				:	Any    						=					feat_extract_first.save_pretrained(A_   )[0]
							check_json_file_has_correct_format(A_   )
							UpperCamelCase				:	str    						=					self.feature_extraction_class.from_pretrained(A_   )

				UpperCamelCase				:	Dict    						=					feat_extract_first.to_dict()
				UpperCamelCase				:	List[str]    						=					feat_extract_second.to_dict()
				UpperCamelCase				:	List[str]    						=					feat_extract_first.mel_filters
				UpperCamelCase				:	str    						=					feat_extract_second.mel_filters
				self.assertTrue(np.allclose(A_				,			A_   )   )
				self.assertEqual(A_				,			A_   )

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Optional[int]:




				'''simple docstring'''






				UpperCamelCase				:	Dict    						=					self.feature_extraction_class(**self.feat_extract_dict   )

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCamelCase				:	Union[str, Any]    						=					os.path.join(A_				,			"feat_extract.json"   )
							feat_extract_first.to_json_file(A_   )
							UpperCamelCase				:	Tuple    						=					self.feature_extraction_class.from_json_file(A_   )

				UpperCamelCase				:	int    						=					feat_extract_first.to_dict()
				UpperCamelCase				:	Optional[int]    						=					feat_extract_second.to_dict()
				UpperCamelCase				:	Tuple    						=					feat_extract_first.mel_filters
				UpperCamelCase				:	Optional[int]    						=					feat_extract_second.mel_filters
				self.assertTrue(np.allclose(A_				,			A_   )   )
				self.assertEqual(A_				,			A_   )

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Tuple:




				'''simple docstring'''






				UpperCamelCase				:	Any    						=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
				# create three inputs of length 800, 1000, and 1200
				UpperCamelCase				:	Tuple    						=					[floats_list((1, x)   )[0] for x in range(8_00				,			14_00				,			2_00   )]
				UpperCamelCase				:	List[str]    						=					[np.asarray(A_   ) for speech_input in speech_inputs]

				# Test feature size
				UpperCamelCase				:	Dict    						=					feature_extractor(A_				,			padding="max_length"				,			return_tensors="np"   ).input_features
				self.assertTrue(input_features.ndim == 3   )
				self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
				self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

				# Test not batched input
				UpperCamelCase				:	Dict    						=					feature_extractor(speech_inputs[0]				,			return_tensors="np"   ).input_features
				UpperCamelCase				:	Dict    						=					feature_extractor(np_speech_inputs[0]				,			return_tensors="np"   ).input_features
				self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )

				# Test batched
				UpperCamelCase				:	Union[str, Any]    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				UpperCamelCase				:	List[Any]    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				for enc_seq_a, enc_seq_a in zip(A_				,			A_   ):
							self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )

				# Test 2-D numpy arrays are batched.
				UpperCamelCase				:	Optional[Any]    						=					[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
				UpperCamelCase				:	Any    						=					np.asarray(A_   )
				UpperCamelCase				:	Tuple    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				UpperCamelCase				:	Dict    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				for enc_seq_a, enc_seq_a in zip(A_				,			A_   ):
							self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )

				# Test truncation required
				UpperCamelCase				:	List[str]    						=					[floats_list((1, x)   )[0] for x in range(2_00				,			(feature_extractor.n_samples + 5_00)				,			2_00   )]
				UpperCamelCase				:	List[Any]    						=					[np.asarray(A_   ) for speech_input in speech_inputs]

				UpperCamelCase				:	Any    						=					[x[: feature_extractor.n_samples] for x in speech_inputs]
				UpperCamelCase				:	str    						=					[np.asarray(A_   ) for speech_input in speech_inputs_truncated]

				UpperCamelCase				:	Optional[Any]    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				UpperCamelCase				:	List[str]    						=					feature_extractor(A_				,			return_tensors="np"   ).input_features
				for enc_seq_a, enc_seq_a in zip(A_				,			A_   ):
							self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Union[str, Any]:




				'''simple docstring'''






				import torch

				UpperCamelCase				:	List[str]    						=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
				UpperCamelCase				:	List[Any]    						=					np.random.rand(1_00				,			32   ).astype(np.floataa   )
				UpperCamelCase				:	Tuple    						=					np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
							UpperCamelCase				:	Optional[Any]    						=					feature_extractor.pad([{"input_features": inputs}]				,			return_tensors="np"   )
							self.assertTrue(np_processed.input_features.dtype == np.floataa   )
							UpperCamelCase				:	int    						=					feature_extractor.pad([{"input_features": inputs}]				,			return_tensors="pt"   )
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase   )							->     List[Any]:




				'''simple docstring'''






				UpperCamelCase				:	Optional[Any]    						=					load_dataset("hf-internal-testing/librispeech_asr_dummy"				,			"clean"				,			split="validation"   )
				# automatic decoding with librispeech
				UpperCamelCase				:	List[str]    						=					ds.sort("id"   ).select(range(A_   )   )[:num_samples]['''audio''']

				return [x["array"] for x in speech_samples]

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Optional[int]:




				'''simple docstring'''






				UpperCamelCase				:	List[str]    						=					torch.tensor(
				    [
				        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
				        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
				        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
				        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
				    ]   )
				# fmt: on

				UpperCamelCase				:	Optional[Any]    						=					self._load_datasamples(1   )
				UpperCamelCase				:	Optional[int]    						=					WhisperFeatureExtractor()
				UpperCamelCase				:	Dict    						=					feature_extractor(A_				,			return_tensors="pt"   ).input_features
				self.assertEqual(input_features.shape				,			(1, 80, 30_00)   )
				self.assertTrue(torch.allclose(input_features[0, 0, :30]				,			A_				,			atol=1e-4   )   )



	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Union[str, Any]:




				'''simple docstring'''






				UpperCamelCase				:	Union[str, Any]    						=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
				UpperCamelCase				:	List[str]    						=					self._load_datasamples(1   )[0]
				UpperCamelCase				:	Union[str, Any]    						=					((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
				UpperCamelCase				:	Any    						=					feat_extract.zero_mean_unit_var_norm([audio]				,			attention_mask=A_   )[0]

				self.assertTrue(np.all(np.mean(A_   ) < 1e-3   )   )
				self.assertTrue(np.all(np.abs(np.var(A_   ) - 1   ) < 1e-3   )   )


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





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      Union[str, Any]   			=  batch_size
         _lowercase:      Tuple   			=  image_size
         _lowercase:      Tuple   			=  num_channels
         _lowercase:      Optional[int]   			=  num_stages
         _lowercase:      List[Any]   			=  hidden_sizes
         _lowercase:      Dict   			=  depths
         _lowercase:      int   			=  is_training
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      Any   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_act
         _lowercase:      str   			=  type_sequence_label_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  out_features
         _lowercase:      Any   			=  num_labels
         _lowercase:      Any   			=  scope
         _lowercase:      str   			=  num_stages
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)

         _lowercase:      Tuple   			=  self.get_config()

         return config, pixel_values, labels
       def 				lowercase_				(  self							)  ->						List[Any]:





         """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							)  ->						Dict:





         """simple docstring"""




         return UperNetConfig(
             backbone_config=self.get_backbone_config()			,					hidden_size=512			,					pool_scales=[1, 2, 3, 6]			,					use_auxiliary_head=A_			,					auxiliary_loss_weight=0.4			,					auxiliary_in_channels=40			,					auxiliary_channels=256			,					auxiliary_num_convs=1			,					auxiliary_concat_input=A_			,					loss_ignore_index=255			,					num_labels=self.num_labels			,					)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  UperNetForSemanticSegmentation(config=A_							)
         model.to(A_							)
         model.eval()
         _lowercase:      int   			=  model(A_							)
         self.parent.assertEqual(
             result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size, self.image_size)							)



       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.prepare_config_and_inputs()
         (
             (
             _lowercase
         )			,					(
             _lowercase
         )			,					(
             _lowercase
         )			,					
         ):      Tuple   			=  config_and_inputs
         _lowercase:      str   			=  {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  UperNetModelTester(self							)
         _lowercase:      Optional[Any]   			=  ConfigTester(self			,					config_class=A_			,					has_text_modality=A_			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """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							)  ->						Dict:





         """simple docstring"""




         return
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase			,					_lowercase:      Union[str, Any]   			=  self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase:      int   			=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A_							)
       @unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """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							)  ->						Optional[Any]:





         """simple docstring"""




         pass
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




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

           with torch.no_grad():
             _lowercase:      List[str]   			=  model(**self._prepare_for_class(A_			,					A_							)							)

           _lowercase:      Tuple   			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase:      Union[str, Any]   			=  self.model_tester.num_stages
           self.assertEqual(len(A_							)			,					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]			,					)

         _lowercase			,					_lowercase:      Dict   			=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase:      Optional[int]   			=  True
           check_hidden_states_output(A_			,					A_			,					A_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:      Union[str, Any]   			=  True

           check_hidden_states_output(A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:      int   			=  _config_zero_init(A_							)
         _lowercase:      Optional[Any]   			=  _config_zero_init(configs_no_init.backbone_config							)
         for model_class in self.all_model_classes:
           _lowercase:      Any   			=  model_class(config=A_							)
           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							)  ->						List[str]:





         """simple docstring"""




         pass



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      Union[str, Any]   			=  UperNetForSemanticSegmentation.from_pretrained(A_							)
           self.assertIsNotNone(A_							)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  hf_hub_download(
      repo_id='''hf-internal-testing/fixtures_ade20k'''			,					repo_type='''dataset'''			,					filename='''ADE_val_00000001.jpg'''    )
  _lowercase:      Optional[int]   			=  Image.open(_UpperCamelCase    ).convert('''RGB'''    )
  return image







@require_torch
@require_vision
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
         _lowercase:      Optional[Any]   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(A_							)

         _lowercase:      List[Any]   			=  prepare_img()
         _lowercase:      Any   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Union[str, Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Optional[Any]   			=  torch.tensor(
             [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
         _lowercase:      str   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(A_							)

         _lowercase:      Union[str, Any]   			=  prepare_img()
         _lowercase:      Optional[Any]   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Optional[Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Tuple   			=  torch.tensor(
             [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)
