



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 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=[10, 20, 30, 40],     SCREAMING_SNAKE_CASE_=[2, 2, 3, 2],     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=37,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"],     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=None,     )	->						Optional[int]:
          UpperCamelCase       :    Optional[int]		      =      parent
          UpperCamelCase       :    Optional[Any]		      =      batch_size
          UpperCamelCase       :    Union[str, Any]		      =      image_size
          UpperCamelCase       :    Optional[int]		      =      num_channels
          UpperCamelCase       :    int		      =      num_stages
          UpperCamelCase       :    Any		      =      hidden_sizes
          UpperCamelCase       :    Tuple		      =      depths
          UpperCamelCase       :    Tuple		      =      is_training
          UpperCamelCase       :    Optional[Any]		      =      use_labels
          UpperCamelCase       :    Optional[int]		      =      intermediate_size
          UpperCamelCase       :    Any		      =      hidden_act
          UpperCamelCase       :    Dict		      =      type_sequence_label_size
          UpperCamelCase       :    int		      =      initializer_range
          UpperCamelCase       :    List[str]		      =      out_features
          UpperCamelCase       :    str		      =      num_labels
          UpperCamelCase       :    Optional[int]		      =      scope
          UpperCamelCase       :    Tuple		      =      num_stages
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

          UpperCamelCase       :    str		      =      None
          if self.use_labels:
             UpperCamelCase       :    List[str]		      =      ids_tensor([self.batch_size],     self.type_sequence_label_size			)

          UpperCamelCase       :    Optional[Any]		      =      self.get_config()

          return config, pixel_values, labels
       def    snake_case_			(     self			)	->						Optional[int]:
          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=SCREAMING_SNAKE_CASE_,     auxiliary_loss_weight=0.4,     auxiliary_in_channels=40,     auxiliary_channels=256,     auxiliary_num_convs=1,     auxiliary_concat_input=SCREAMING_SNAKE_CASE_,     loss_ignore_index=255,     num_labels=self.num_labels,     )
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Dict:
          UpperCamelCase       :    int		      =      UperNetForSemanticSegmentation(config=SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.eval()
          UpperCamelCase       :    Tuple		      =      model(SCREAMING_SNAKE_CASE_			)
          self.parent.assertEqual(
              result.logits.shape,     (self.batch_size, self.num_labels, self.image_size, self.image_size)			)






       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Optional[Any]		      =      self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase
          )      ,	(
              UpperCamelCase
          )      ,	(
              UpperCamelCase
          )      ,	
          )       :    str		      =      config_and_inputs
          UpperCamelCase       :    List[Any]		      =      {'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 							lowerCAmelCase_						(    a__			,       a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   List[str]  	=    (UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCAmelCase__							:   Union[str, Any]  	=    {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCAmelCase__							:   Any  	=    False
       UpperCAmelCase__							:   str  	=    False
       UpperCAmelCase__							:   Optional[int]  	=    False
       UpperCAmelCase__							:   Any  	=    False
       UpperCAmelCase__							:   str  	=    False
       UpperCAmelCase__							:   Tuple  	=    False
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Dict		      =      UperNetModelTester(self			)
          UpperCamelCase       :    int		      =      ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     has_text_modality=SCREAMING_SNAKE_CASE_,     hidden_size=37			)
       def    snake_case_			(     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    snake_case_			(     self			)	->						Dict:
          return
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase       :    int		      =      ['pixel_values']
             self.assertListEqual(arg_names[:1],     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    int		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_			)
       @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			)	->						str:
          pass
       @unittest.skip(reason='UperNet does not have a base model'			)
       def    snake_case_			(     self			)	->						List[str]:
          pass
       @unittest.skip(reason='UperNet does not have a base model'			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          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			)	->						Optional[int]:
          pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
       def    snake_case_			(     self			)	->						List[str]:
          pass
       def    snake_case_			(     self			)	->						Optional[Any]:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Dict		      =      model_class(SCREAMING_SNAKE_CASE_			)
             model.to(SCREAMING_SNAKE_CASE_			)
             model.eval()

             with torch.no_grad():
                UpperCamelCase       :    Union[str, Any]		      =      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)			)

             UpperCamelCase       :    List[Any]		      =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase       :    Dict		      =      self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     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       :    str		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Tuple		      =      True
             check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

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

             check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase      ,	UpperCamelCase       :    int		      =      self.model_tester.prepare_config_and_inputs_for_common()

          UpperCamelCase       :    str		      =      _config_zero_init(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Any		      =      _config_zero_init(configs_no_init.backbone_config			)
          for model_class in self.all_model_classes:
             UpperCamelCase       :    Dict		      =      model_class(config=SCREAMING_SNAKE_CASE_			)
             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			)	->						List[Any]:
          pass






       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    str		      =      UperNetForSemanticSegmentation.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






def 						UpperCamelCase      (  )      ->	Optional[Any]:
   UpperCamelCase       :    Dict		      =      hf_hub_download(
       repo_id='hf-internal-testing/fixtures_ade20k'			,	repo_type='dataset'			,	filename='ADE_val_00000001.jpg'      )
   UpperCamelCase       :    List[Any]		      =      Image.open(snake_case__      ).convert('RGB'      )
   return image



@require_torch
@require_vision
@slow
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Any		      =      AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'			)
          UpperCamelCase       :    int		      =      UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      prepare_img()
          UpperCamelCase       :    Union[str, Any]		      =      processor(images=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)

          with torch.no_grad():
             UpperCamelCase       :    str		      =      model(**SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    Tuple		      =      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(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=1e-4			)			)






       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    List[Any]		      =      AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'			)
          UpperCamelCase       :    Dict		      =      UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'			).to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    str		      =      prepare_img()
          UpperCamelCase       :    List[Any]		      =      processor(images=SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)

          with torch.no_grad():
             UpperCamelCase       :    Tuple		      =      model(**SCREAMING_SNAKE_CASE_			)

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

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






"""simple docstring"""


class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self    )    ->      Union[str, Any]:
          A				  =		{}




    def UpperCamelCase__		( self    )    ->      None:
          print(self.vertex    )
          for i in self.vertex:
                print(lowerCamelCase_ ,""" -> """ ,""" -> """.join([str(lowerCamelCase_    ) for j in self.vertex[i]]    )    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # check if vertex is already present,
          if from_vertex in self.vertex:
                self.vertex[from_vertex].append(lowerCamelCase_    )
          else:
                # else make a new vertex
                A				  =		[to_vertex]




    def UpperCamelCase__		( self    )    ->      None:
          # visited array for storing already visited nodes
          A				  =		[False] * len(self.vertex    )

          # call the recursive helper function
          for i in range(len(self.vertex    )    ):
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # mark start vertex as visited
          A				  =		True

          print(lowerCamelCase_ ,end=""" """    )

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase													=Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("DFS:")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3




