



'''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 							lowercase       :


 """simple docstring"""


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







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						Union[str, Any]			     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

        _UpperCAmelCase  :						Tuple			     = self.get_config()

        return config, pixel_values, labels







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







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   int:
        _UpperCAmelCase  :						Tuple			     = UperNetForSemanticSegmentation(config=_UpperCamelCase							)
        model.to(_UpperCamelCase							)
        model.eval()
        _UpperCAmelCase  :						Dict			     = model(_UpperCamelCase							)
        self.parent.assertEqual(
            result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size, self.image_size)							)







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						Tuple			     = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase    
        )     ,(
            _UpperCAmelCase    
        )     ,(
            _UpperCAmelCase    
        )     ,    
        )  :						Optional[int]			     = config_and_inputs
        _UpperCAmelCase  :						Optional[Any]			     = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 							lowercase       (  __A					,					__A					,					unittest.TestCase				):


 """simple docstring"""


 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 _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						List[str]			     = UperNetModelTester(self							)
        _UpperCAmelCase  :						Union[str, Any]			     = ConfigTester(self  ,config_class=_UpperCamelCase  ,has_text_modality=_UpperCamelCase  ,hidden_size=37							)







 def _snake_case (							self							)		->   List[str]:
        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							)		->   Dict:
        return







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCAmelCase  :						Optional[Any]			     = model_class(_UpperCamelCase							)
               _UpperCAmelCase  :						Union[str, Any]			     = inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase  :						int			     = [*signature.parameters.keys()]

               _UpperCAmelCase  :						Dict			     = ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]  ,_UpperCamelCase							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Dict			     = 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							)		->   Tuple:
        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							)		->   Optional[int]:
        pass







 @unittest.skip(reason="""UperNet does not have a base model"""							)
 def _snake_case (							self							)		->   str:
        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							)		->   Tuple:
        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							)		->   Any:
        def check_hidden_states_output(a_  ,a_  ,a_							):
               _UpperCAmelCase  :						Optional[int]			     = model_class(_UpperCamelCase							)
               model.to(_UpperCamelCase							)
               model.eval()

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

               _UpperCAmelCase  :						List[str]			     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _UpperCAmelCase  :						int			     = 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]  ,)

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

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

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase  :						str			     = True

               check_hidden_states_output(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase  :						Union[str, Any]			     = _config_zero_init(_UpperCamelCase							)
        _UpperCAmelCase  :						Tuple			     = _config_zero_init(configs_no_init.backbone_config							)
        for model_class in self.all_model_classes:
               _UpperCAmelCase  :						List[str]			     = 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							)		->   Tuple:
        pass







 @slow
 def _snake_case (							self							)		->   List[Any]:
        for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase  :						List[Any]			     = UperNetForSemanticSegmentation.from_pretrained(_UpperCamelCase							)
               self.assertIsNotNone(_UpperCamelCase							)







def     snake_case_					(     )->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = hf_hub_download(
           repo_id="""hf-internal-testing/fixtures_ade20k"""       ,   repo_type="""dataset"""       ,   filename="""ADE_val_00000001.jpg"""		)
       _UpperCAmelCase  :						str			     = Image.open(SCREAMING_SNAKE_CASE__		).convert("""RGB"""		)
       return image



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


 """simple docstring"""


 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Tuple			     = AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""							)
        _UpperCAmelCase  :						List[Any]			     = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""							).to(_UpperCamelCase							)

        _UpperCAmelCase  :						Union[str, Any]			     = prepare_img()
        _UpperCAmelCase  :						List[str]			     = processor(images=_UpperCamelCase  ,return_tensors="""pt"""							).to(_UpperCamelCase							)

        with torch.no_grad():
               _UpperCAmelCase  :						Tuple			     = model(**_UpperCamelCase							)

        _UpperCAmelCase  :						Optional[int]			     = torch.Size((1, model.config.num_labels, 512, 512)							)
        self.assertEqual(outputs.logits.shape  ,_UpperCamelCase							)

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







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""							)
        _UpperCAmelCase  :						int			     = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""							).to(_UpperCamelCase							)

        _UpperCAmelCase  :						Dict			     = prepare_img()
        _UpperCAmelCase  :						List[str]			     = processor(images=_UpperCamelCase  ,return_tensors="""pt"""							).to(_UpperCamelCase							)

        with torch.no_grad():
               _UpperCAmelCase  :						str			     = model(**_UpperCamelCase							)

        _UpperCAmelCase  :						int			     = torch.Size((1, model.config.num_labels, 512, 512)							)
        self.assertEqual(outputs.logits.shape  ,_UpperCamelCase							)

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






from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()