



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=1_3    ,				A=3_2    ,				A=3    ,				A=4    ,				A=[1_0, 2_0, 3_0, 4_0]    ,				A=[2, 2, 3, 2]    ,				A=True    ,				A=True    ,				A=3_7    ,				A="gelu"    ,				A=1_0    ,				A=0.02    ,				A=["stage2", "stage3", "stage4"]    ,				A=3    ,				A=None    ,				)		->    Optional[Any]:
         snake_case				:    List[str]           =	parent
         snake_case				:    Union[str, Any]           =	batch_size
         snake_case				:    Tuple           =	image_size
         snake_case				:    Optional[int]           =	num_channels
         snake_case				:    List[str]           =	num_stages
         snake_case				:    List[Any]           =	hidden_sizes
         snake_case				:    Optional[Any]           =	depths
         snake_case				:    List[str]           =	is_training
         snake_case				:    str           =	use_labels
         snake_case				:    Any           =	intermediate_size
         snake_case				:    Dict           =	hidden_act
         snake_case				:    List[Any]           =	type_sequence_label_size
         snake_case				:    List[str]           =	initializer_range
         snake_case				:    Tuple           =	out_features
         snake_case				:    Dict           =	num_labels
         snake_case				:    List[str]           =	scope
         snake_case				:    Optional[Any]           =	num_stages

      def      UpperCAmelCase				(				self     )		->    int:
         snake_case				:    int           =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

         snake_case				:    Optional[int]           =	self.get_config()

         return config, pixel_values, labels

      def      UpperCAmelCase				(				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      UpperCAmelCase				(				self     )		->    Dict:
         return UperNetConfig(
             backbone_config=self.get_backbone_config()    ,				hidden_size=5_1_2    ,				pool_scales=[1, 2, 3, 6]    ,				use_auxiliary_head=_UpperCamelCase    ,				auxiliary_loss_weight=0.4    ,				auxiliary_in_channels=4_0    ,				auxiliary_channels=2_5_6    ,				auxiliary_num_convs=1    ,				auxiliary_concat_input=_UpperCamelCase    ,				loss_ignore_index=2_5_5    ,				num_labels=self.num_labels    ,				)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    List[Any]:
         snake_case				:    Union[str, Any]           =	UperNetForSemanticSegmentation(config=_UpperCamelCase     )
         model.to(_UpperCamelCase     )
         model.eval()
         snake_case				:    List[Any]           =	model(_UpperCamelCase     )
         self.parent.assertEqual(
             result.logits.shape    ,				(self.batch_size, self.num_labels, self.image_size, self.image_size)     )

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    List[str]           =	self.prepare_config_and_inputs()
         (
             snake_case 
         )				:    Union[str, Any]           =	config_and_inputs
         snake_case				:    List[Any]           =	{"""pixel_values""": pixel_values}
         return config, inputs_dict




@require_torch
class 						__lowercase		(lowercase__					,		lowercase__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						(UperNetForSemanticSegmentation,) if is_torch_available() else ()
      _snake_case												=						{'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
      _snake_case												=						False
      _snake_case												=						False
      _snake_case												=						False
      _snake_case												=						False
      _snake_case												=						False
      _snake_case												=						False

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         snake_case				:    Tuple           =	UperNetModelTester(self     )
         snake_case				:    List[Any]           =	ConfigTester(self    ,				config_class=_UpperCamelCase    ,				has_text_modality=_UpperCamelCase    ,				hidden_size=3_7     )

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

      def      UpperCAmelCase				(				self     )		->    str:
         snake_case				:    List[Any]           =	self.model_tester.prepare_config_and_inputs_for_common()

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

            snake_case				:    Optional[Any]           =	["""pixel_values"""]
            self.assertListEqual(arg_names[:1]    ,				_UpperCamelCase     )

      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    int           =	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      UpperCAmelCase				(				self     )		->    int:
         pass

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

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

      @unittest.skip(reason="""UperNet does not have a base model"""     )
      def      UpperCAmelCase				(				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      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         pass

      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         pass

      def      UpperCAmelCase				(				self     )		->    int:
         def check_hidden_states_output(A    ,				A    ,				A     ):
            snake_case				:    Any           =	model_class(_UpperCamelCase     )
            model.to(_UpperCamelCase     )
            model.eval()

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

            snake_case				:    Optional[Any]           =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            snake_case				:    Any           =	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]    ,				)

         snake_case				:    int           =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            snake_case				:    List[Any]           =	True
            check_hidden_states_output(_UpperCamelCase    ,				_UpperCamelCase    ,				_UpperCamelCase     )

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

            check_hidden_states_output(_UpperCamelCase    ,				_UpperCamelCase    ,				_UpperCamelCase     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    int           =	self.model_tester.prepare_config_and_inputs_for_common()

         snake_case				:    str           =	_config_zero_init(_UpperCamelCase     )
         snake_case				:    str           =	_config_zero_init(configs_no_init.backbone_config     )
         for model_class in self.all_model_classes:
            snake_case				:    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      UpperCAmelCase				(				self     )		->    int:
         pass

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




def 						SCREAMING_SNAKE_CASE__ (   )  ->       int:
   snake_case				:    Tuple           =	hf_hub_download(
       repo_id="""hf-internal-testing/fixtures_ade20k"""						,repo_type="""dataset"""						,filename="""ADE_val_00000001.jpg"""					)
   snake_case				:    Optional[Any]           =	Image.open(lowerCamelCase_					).convert("""RGB"""					)
   return image




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




      """simple docstring"""





      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    Union[str, Any]           =	AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""     )
         snake_case				:    Optional[Any]           =	UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""     ).to(_UpperCamelCase     )

         snake_case				:    Optional[int]           =	prepare_img()
         snake_case				:    int           =	processor(images=_UpperCamelCase    ,				return_tensors="""pt"""     ).to(_UpperCamelCase     )

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

         snake_case				:    List[str]           =	torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)     )
         self.assertEqual(outputs.logits.shape    ,				_UpperCamelCase     )

         snake_case				:    Dict           =	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(_UpperCamelCase     )
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,				_UpperCamelCase    ,				atol=1e-4     )     )

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    int           =	AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""     )
         snake_case				:    List[Any]           =	UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""     ).to(_UpperCamelCase     )

         snake_case				:    Optional[int]           =	prepare_img()
         snake_case				:    Tuple           =	processor(images=_UpperCamelCase    ,				return_tensors="""pt"""     ).to(_UpperCamelCase     )

         with torch.no_grad():
            snake_case				:    Optional[Any]           =	model(**_UpperCamelCase     )

         snake_case				:    Tuple           =	torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)     )
         self.assertEqual(outputs.logits.shape    ,				_UpperCamelCase     )

         snake_case				:    Optional[int]           =	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(_UpperCamelCase     )
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,				_UpperCamelCase    ,				atol=1e-4     )     )











# using dfs for finding eulerian path traversal




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=None					)  ->       Any:
   snake_case				:    Union[str, Any]           =	(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         snake_case     , snake_case				:    int           =	True, True
         snake_case				:    List[Any]           =	dfs(lowercase						,lowercase						,lowercase						,lowercase					)
   return path




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	0
   snake_case				:    Union[str, Any]           =	-1
   for i in range(lowercase					):
      if i not in graph.keys():
         continue
      if len(graph[i]					) % 2 == 1:
         odd_degree_nodes += 1
         snake_case				:    str           =	i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    Tuple           =	[[False for _ in range(max_node + 1					)] for _ in range(max_node + 1					)]
   snake_case     , snake_case				:    Any           =	check_circuit_or_path(lowercase						,lowercase					)
   if check == 3:
      print("""graph is not Eulerian"""					)
      print("""no path"""					)
      return
   snake_case				:    str           =	1
   if check == 2:
      snake_case				:    Optional[int]           =	odd_node
      print("""graph has a Euler path"""					)
   if check == 1:
      print("""graph has a Euler cycle"""					)
   snake_case				:    Dict           =	dfs(lowercase						,lowercase						,lowercase					)
   print(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Optional[Any]:
   snake_case				:    Union[str, Any]           =	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   snake_case				:    Optional[int]           =	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   snake_case				:    List[Any]           =	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
   snake_case				:    Tuple           =	{
       1: [],
       2: []
       # all degree is zero
   }
   snake_case				:    Tuple           =	10
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)
   check_euler(lowercase						,lowercase					)


if __name__ == "__main__":
       main()





