



"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__a      =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

__a      =				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__a      =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def    A_  (			_lowercase	):







						'''simple docstring'''


						with open(_lowercase,					"""rb"""	) as f:
												snake_case_ :str						=			Image.open(_lowercase	)
												return im.convert("""RGB"""	)
@dataclass
class 				lowerCamelCase  :






					'''simple docstring'''





					_A		:			Optional[str]						=     field(
					    default=_lowerCAmelCase    ,						metadata={
					        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
					    }    ,						)
					_A		:			Optional[str]						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}				)
					_A		:			Optional[str]						=     field(default=_lowerCAmelCase    ,						metadata={"""help""": """A folder containing the training data."""}				)
					_A		:			Optional[str]						=     field(default=_lowerCAmelCase    ,						metadata={"""help""": """A folder containing the validation data."""}				)
					_A		:			Optional[float]						=     field(
					    default=0.15    ,						metadata={"""help""": """Percent to split off of train for validation."""}				)
					_A		:			Optional[int]						=     field(
					    default=_lowerCAmelCase    ,						metadata={
					        """help""": (
					            """For debugging purposes or quicker training, truncate the number of training examples to this """
					            """value if set."""
					        )
					    }    ,						)
					_A		:			Optional[int]						=     field(
					    default=_lowerCAmelCase    ,						metadata={
					        """help""": (
					            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
					            """value if set."""
					        )
					    }    ,						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Any:
											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																	raise ValueError(
																	    """You must specify either a dataset name from the hub or a train and/or validation directory."""						)

@dataclass
class 				lowerCamelCase  :






					'''simple docstring'''





					_A		:			str						=     field(
					    default="""google/vit-base-patch16-224-in21k"""    ,						metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    ,						)
					_A		:			Optional[str]						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_lowerCAmelCase				)}    ,						)
					_A		:			Optional[str]						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
					_A		:			Optional[str]						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}				)
					_A		:			str						=     field(
					    default="""main"""    ,						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,						)
					_A		:			str						=     field(default=_lowerCAmelCase    ,						metadata={"""help""": """Name or path of preprocessor config."""}				)
					_A		:			bool						=     field(
					    default=_lowerCAmelCase    ,						metadata={
					        """help""": (
					            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
					            """with private models)."""
					        )
					    }    ,						)
					_A		:			bool						=     field(
					    default=_lowerCAmelCase    ,						metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}    ,						)

def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			torch.stack([example["""pixel_values"""] for example in examples]	)
						snake_case_ :Union[str, Any]						=			torch.tensor([example["""labels"""] for example in examples]	)
						return {"pixel_values": pixel_values, "labels": labels}
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Any						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
						if len(sys.argv	) == 2 and sys.argv[1].endswith(""".json"""	):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												snake_case_,       snake_case_,       snake_case_ :int						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												snake_case_,       snake_case_,       snake_case_ :Tuple						=			parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry("""run_image_classification""",					_lowercase,					_lowercase	)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",					datefmt="""%m/%d/%Y %H:%M:%S""",					handlers=[logging.StreamHandler(sys.stdout	)],					)

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						snake_case_ :Optional[int]						=			training_args.get_process_log_level()
						logger.setLevel(_lowercase	)
						transformers.utils.logging.set_verbosity(_lowercase	)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
						    + f"""distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}"""	)
						logger.info(f"""Training/evaluation parameters {training_args}"""	)

						# Detecting last checkpoint.
						snake_case_ :List[Any]						=			None
						if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
												snake_case_ :int						=			get_last_checkpoint(training_args.output_dir	)
												if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
																		raise ValueError(
																		    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																		    """Use --overwrite_output_dir to overcome."""	)
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																		    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""	)

    # Set seed before initializing model.
						set_seed(training_args.seed	)

						# Initialize our dataset and prepare it for the 'image-classification' task.
						if data_args.dataset_name is not None:
												snake_case_ :Optional[Any]						=			load_dataset(
												    data_args.dataset_name,					data_args.dataset_config_name,					cache_dir=model_args.cache_dir,					task="""image-classification""",					use_auth_token=True if model_args.use_auth_token else None,					)
						else:
												snake_case_ :Union[str, Any]						=			{}
												if data_args.train_dir is not None:
																		snake_case_ :Tuple						=			os.path.join(data_args.train_dir,					"""**"""	)
												if data_args.validation_dir is not None:
																		snake_case_ :List[Any]						=			os.path.join(data_args.validation_dir,					"""**"""	)
												snake_case_ :Dict						=			load_dataset(
												    """imagefolder""",					data_files=_lowercase,					cache_dir=model_args.cache_dir,					task="""image-classification""",					)

						# If we don't have a validation split, split off a percentage of train as validation.
						snake_case_ :List[Any]						=			None if """validation""" in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split,					_lowercase	) and data_args.train_val_split > 0.0:
												snake_case_ :Dict						=			dataset["""train"""].train_test_split(data_args.train_val_split	)
												snake_case_ :Optional[int]						=			split["""train"""]
												snake_case_ :int						=			split["""test"""]

						# Prepare label mappings.
						# We'll include these in the model's config to get human readable labels in the Inference API.
						snake_case_ :Optional[Any]						=			dataset["""train"""].features["""labels"""].names
						snake_case_,       snake_case_ :Optional[Any]						=			{}, {}
						for i, label in enumerate(_lowercase	):
												snake_case_ :Union[str, Any]						=			str(_lowercase	)
												snake_case_ :Union[str, Any]						=			label

						# Load the accuracy metric from the datasets package
						snake_case_ :Any						=			evaluate.load("""accuracy"""	)

						# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
						# predictions and label_ids field) and has to return a dictionary string to float.
						def compute_metrics(_lowercase	):
												return metric.compute(predictions=np.argmax(p.predictions,					axis=1	),					references=p.label_ids	)

						snake_case_ :Tuple						=			AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path,					num_labels=len(_lowercase	),					labelaid=_lowercase,					idalabel=_lowercase,					finetuning_task="""image-classification""",					cache_dir=model_args.cache_dir,					revision=model_args.model_revision,					use_auth_token=True if model_args.use_auth_token else None,					)
						snake_case_ :Optional[int]						=			AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path	),					config=_lowercase,					cache_dir=model_args.cache_dir,					revision=model_args.model_revision,					use_auth_token=True if model_args.use_auth_token else None,					ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,					)
						snake_case_ :Union[str, Any]						=			AutoImageProcessor.from_pretrained(
						    model_args.image_processor_name or model_args.model_name_or_path,					cache_dir=model_args.cache_dir,					revision=model_args.model_revision,					use_auth_token=True if model_args.use_auth_token else None,					)

						# Define torchvision transforms to be applied to each image.
						if "shortest_edge" in image_processor.size:
												snake_case_ :Union[str, Any]						=			image_processor.size["""shortest_edge"""]
						else:
												snake_case_ :Optional[Any]						=			(image_processor.size["""height"""], image_processor.size["""width"""])
						snake_case_ :List[Any]						=			Normalize(mean=image_processor.image_mean,					std=image_processor.image_std	)
						snake_case_ :Optional[Any]						=			Compose(
						    [
						        RandomResizedCrop(_lowercase	),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]	)
						snake_case_ :Any						=			Compose(
						    [
						        Resize(_lowercase	),
						        CenterCrop(_lowercase	),
						        ToTensor(),
						        normalize,
						    ]	)

						def train_transforms(_lowercase	):
												snake_case_ :Optional[Any]						=			[
												    _train_transforms(pil_img.convert("""RGB"""	)	) for pil_img in example_batch["""image"""]
												]
												return example_batch

						def val_transforms(_lowercase	):
												snake_case_ :List[str]						=			[_val_transforms(pil_img.convert("""RGB"""	)	) for pil_img in example_batch["""image"""]]
												return example_batch

						if training_args.do_train:
												if "train" not in dataset:
																		raise ValueError("""--do_train requires a train dataset"""	)
												if data_args.max_train_samples is not None:
																		snake_case_ :Any						=			(
																		    dataset["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
																		)
												# Set the training transforms
												dataset["train"].set_transform(_lowercase	)

						if training_args.do_eval:
												if "validation" not in dataset:
																		raise ValueError("""--do_eval requires a validation dataset"""	)
												if data_args.max_eval_samples is not None:
																		snake_case_ :Optional[Any]						=			(
																		    dataset["""validation"""].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
																		)
												# Set the validation transforms
												dataset["validation"].set_transform(_lowercase	)

						# Initalize our trainer
						snake_case_ :str						=			Trainer(
						    model=_lowercase,					args=_lowercase,					train_dataset=dataset["""train"""] if training_args.do_train else None,					eval_dataset=dataset["""validation"""] if training_args.do_eval else None,					compute_metrics=_lowercase,					tokenizer=_lowercase,					data_collator=_lowercase,					)

						# Training
						if training_args.do_train:
												snake_case_ :Optional[Any]						=			None
												if training_args.resume_from_checkpoint is not None:
																		snake_case_ :Optional[int]						=			training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		snake_case_ :Optional[Any]						=			last_checkpoint
												snake_case_ :Tuple						=			trainer.train(resume_from_checkpoint=_lowercase	)
												trainer.save_model()
												trainer.log_metrics("""train""",					train_result.metrics	)
												trainer.save_metrics("""train""",					train_result.metrics	)
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												snake_case_ :Optional[int]						=			trainer.evaluate()
												trainer.log_metrics("""eval""",					_lowercase	)
												trainer.save_metrics("""eval""",					_lowercase	)

						# Write model card and (optionally) push to hub
						snake_case_ :Union[str, Any]						=			{
						    """finetuned_from""": model_args.model_name_or_path,
						    """tasks""": """image-classification""",
						    """dataset""": data_args.dataset_name,
						    """tags""": ["""image-classification""", """vision"""],
						}
						if training_args.push_to_hub:
												trainer.push_to_hub(**_lowercase	)
						else:
												trainer.create_model_card(**_lowercase	)


if __name__ == "__main__":
						main()

"""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						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=32						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=[10, 20, 30, 40]						,__UpperCAmelCase=[2, 2, 3, 2]						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=10						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=["stage2", "stage3", "stage4"]						,__UpperCAmelCase=3						,__UpperCAmelCase=None						,)    -> Optional[int]:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							image_size
													A__		   =							num_channels
													A__		   =							num_stages
													A__		   =							hidden_sizes
													A__		   =							depths
													A__		   =							is_training
													A__		   =							use_labels
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							out_features
													A__		   =							num_labels
													A__		   =							scope
													A__		   =							num_stages





						def  snake_case__		(				self   )    -> List[Any]:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

													A__		   =							self.get_config()

													return config, pixel_values, labels





						def  snake_case__		(				self   )    -> str:
													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  snake_case__		(				self   )    -> Tuple:
													return UperNetConfig(
													    backbone_config=self.get_backbone_config()						,hidden_size=5_12						,pool_scales=[1, 2, 3, 6]						,use_auxiliary_head=__UpperCAmelCase						,auxiliary_loss_weight=0.4						,auxiliary_in_channels=40						,auxiliary_channels=2_56						,auxiliary_num_convs=1						,auxiliary_concat_input=__UpperCAmelCase						,loss_ignore_index=2_55						,num_labels=self.num_labels						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							UperNetForSemanticSegmentation(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(
													    result.logits.shape						,(self.batch_size, self.num_labels, self.image_size, self.image_size)   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.prepare_config_and_inputs()
													(
													    (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     
													)		   =							config_and_inputs
													A__		   =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  int  							=						(UperNetForSemanticSegmentation,) if is_torch_available() else ()
						lowerCAmelCase__     :  int  							=						{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
						lowerCAmelCase__     :  Optional[int]  							=						False
						lowerCAmelCase__     :  List[Any]  							=						False
						lowerCAmelCase__     :  Tuple  							=						False
						lowerCAmelCase__     :  Optional[Any]  							=						False
						lowerCAmelCase__     :  Union[str, Any]  							=						False
						lowerCAmelCase__     :  Dict  							=						False





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							UperNetModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> List[Any]:
													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  snake_case__		(				self   )    -> int:
													return





						def  snake_case__		(				self   )    -> List[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase   )





						@unittest.skip(reason='UperNet does not use inputs_embeds'   )
						def  snake_case__		(				self   )    -> Optional[int]:
													pass





						@unittest.skip(reason='UperNet does not support input and output embeddings'   )
						def  snake_case__		(				self   )    -> Tuple:
													pass





						@unittest.skip(reason='UperNet does not have a base model'   )
						def  snake_case__		(				self   )    -> List[Any]:
													pass





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





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





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





						def  snake_case__		(				self   )    -> Optional[int]:
													def check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							model_class(__UpperCAmelCase   )
																				model.to(__UpperCAmelCase   )
																				model.eval()

																				with torch.no_grad():
																											A__		   =							model(**self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )   )

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

																				A__		   =							self.model_tester.num_stages
																				self.assertEqual(len(__UpperCAmelCase   )						,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]						,)

													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							True
																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )

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

																				check_hidden_states_output(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													A__		   =							_config_zero_init(__UpperCAmelCase   )
													A__		   =							_config_zero_init(configs_no_init.backbone_config   )
													for model_class in self.all_model_classes:
																				A__		   =							model_class(config=__UpperCAmelCase   )
																				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  snake_case__		(				self   )    -> str:
													pass





						@slow
						def  snake_case__		(				self   )    -> List[str]:
													for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__		   =							UperNetForSemanticSegmentation.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							hf_hub_download(
							    repo_id='hf-internal-testing/fixtures_ade20k'				,			repo_type='dataset'				,			filename='ADE_val_00000001.jpg' )
							A__		   =							Image.open(UpperCamelCase__ ).convert('RGB' )
							return image


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





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

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

													A__		   =							torch.tensor(
													    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'   )
													A__		   =							UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'   ).to(__UpperCAmelCase   )

													A__		   =							prepare_img()
													A__		   =							processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase   )

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

													A__		   =							torch.tensor(
													    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]   ).to(__UpperCAmelCase   )
													self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,__UpperCAmelCase						,atol=1e-4   )   )





