




import sys
from collections import defaultdict

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: str				):
          __UpperCamelCase  		=						[]







      def       snake_case	(     self					: Tuple ,       snake_case					: Any				):
          return self.node_position[vertex]







      def       snake_case	(     self					: Any ,       snake_case					: Optional[Any] ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						pos







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Dict ,       snake_case					: int ,       snake_case					: Dict ,       snake_case					: Dict				):
          if start > size // 2 - 1:
              return
          else:
              if 2 * start + 2 >= size:
                  __UpperCamelCase  		=						2 * start + 1
              else:
                  if heap[2 * start + 1] < heap[2 * start + 2]:
                      __UpperCamelCase  		=						2 * start + 1
                  else:
                      __UpperCamelCase  		=						2 * start + 2
              if heap[smallest_child] < heap[start]:
                  __UpperCamelCase		,      __UpperCamelCase  		=						heap[smallest_child], positions[smallest_child]
                  __UpperCamelCase		,      __UpperCamelCase  		=						(
                      heap[start],
                      positions[start],
                  )
                  __UpperCamelCase		,      __UpperCamelCase  		=						temp, tempa

                  __UpperCamelCase  		=						self.get_position(positions[smallest_child]				)
                  self.set_position(
                      positions[smallest_child] ,       self.get_position(positions[start]				)				)
                  self.set_position(positions[start] ,       snake_case				)

                  self.top_to_bottom(snake_case ,       snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: str				):
          __UpperCamelCase  		=						position[index]

          while index != 0:
              __UpperCamelCase  		=						int((index - 2) / 2				) if index % 2 == 0 else int((index - 1) / 2				)

              if val < heap[parent]:
                  __UpperCamelCase  		=						heap[parent]
                  __UpperCamelCase  		=						position[parent]
                  self.set_position(position[parent] ,       snake_case				)
              else:
                  __UpperCamelCase  		=						val
                  __UpperCamelCase  		=						temp
                  self.set_position(snake_case ,       snake_case				)
                  break
              __UpperCamelCase  		=						parent
          else:
              __UpperCamelCase  		=						val
              __UpperCamelCase  		=						temp
              self.set_position(snake_case ,       0				)







      def       snake_case	(     self					: Any ,       snake_case					: Optional[int] ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						len(snake_case				) // 2 - 1
          for i in range(snake_case ,       -1 ,       -1				):
              self.top_to_bottom(snake_case ,       snake_case ,       len(snake_case				) ,       snake_case				)







      def       snake_case	(     self					: Tuple ,       snake_case					: Union[str, Any] ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						positions[0]
          __UpperCamelCase  		=						sys.maxsize
          self.top_to_bottom(snake_case ,       0 ,       len(snake_case				) ,       snake_case				)
          return temp







def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						Heap()

    __UpperCamelCase  		=						[0] * len(lowercase_      )
    __UpperCamelCase  		=						[-1] * len(lowercase_      )  # Neighboring Tree Vertex of selected vertex
    # Minimum Distance of explored vertex with neighboring vertex of partial tree
    # formed in graph
    __UpperCamelCase  		=						[]  # Heap of Distance of vertices from their neighboring vertex
    __UpperCamelCase  		=						[]

    for vertex in range(len(lowercase_      )      ):
        distance_tv.append(sys.maxsize      )
        positions.append(lowercase_      )
        heap.node_position.append(lowercase_      )

    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						1
    __UpperCamelCase  		=						sys.maxsize
    for neighbor, distance in adjacency_list[0]:
        __UpperCamelCase  		=						0
        __UpperCamelCase  		=						distance
    heap.heapify(lowercase_					,     lowercase_      )

    for _ in range(1					,     len(lowercase_      )      ):
        __UpperCamelCase  		=						heap.delete_minimum(lowercase_					,     lowercase_      )
        if visited[vertex] == 0:
            tree_edges.append((nbr_tv[vertex], vertex)      )
            __UpperCamelCase  		=						1
            for neighbor, distance in adjacency_list[vertex]:
                if (
                    visited[neighbor] == 0
                    and distance < distance_tv[heap.get_position(lowercase_      )]
                ):
                    __UpperCamelCase  		=						distance
                    heap.bottom_to_top(
                        lowercase_					,     heap.get_position(lowercase_      )					,     lowercase_					,     lowercase_      )
                    __UpperCamelCase  		=						vertex
    return tree_edges


if __name__ == "__main__":  # pragma: no cover
 # < --------- Prims Algorithm --------- >
 a_   		=     int(input("Enter number of edges: ").strip())
 a_   		=     defaultdict(list)
 for _ in range(edges_number):
  a_   		=     [int(x) for x in input().strip().split()]
  adjacency_list[edge[0]].append([edge[1], edge[2]])
  adjacency_list[edge[1]].append([edge[0], edge[2]])
 print(prisms_algorithm(adjacency_list))












import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Optional[Any] ,       snake_case					: Tuple=13 ,       snake_case					: List[Any]=32 ,       snake_case					: Optional[Any]=2 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=16 ,       snake_case					: Optional[int]=[32, 64, 128] ,       snake_case					: int=[1, 2, 1] ,       snake_case					: Any=[2, 2, 4] ,       snake_case					: Optional[int]=2 ,       snake_case					: List[str]=2.0 ,       snake_case					: Any=True ,       snake_case					: Optional[Any]=0.0 ,       snake_case					: str=0.0 ,       snake_case					: List[Any]=0.1 ,       snake_case					: List[str]="gelu" ,       snake_case					: Any=False ,       snake_case					: Optional[int]=True ,       snake_case					: List[str]=0.02 ,       snake_case					: List[Any]=1E-5 ,       snake_case					: str=True ,       snake_case					: Any=None ,       snake_case					: int=True ,       snake_case					: Optional[Any]=10 ,       snake_case					: List[str]=8 ,       snake_case					: List[str]=["stage1", "stage2"] ,       snake_case					: Optional[Any]=[1, 2] ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						image_size
          __UpperCamelCase  		=						patch_size
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						embed_dim
          __UpperCamelCase  		=						hidden_sizes
          __UpperCamelCase  		=						depths
          __UpperCamelCase  		=						num_heads
          __UpperCamelCase  		=						window_size
          __UpperCamelCase  		=						mlp_ratio
          __UpperCamelCase  		=						qkv_bias
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						drop_path_rate
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						use_absolute_embeddings
          __UpperCamelCase  		=						patch_norm
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						scope
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						encoder_stride
          __UpperCamelCase  		=						out_features
          __UpperCamelCase  		=						out_indices







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

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

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels







      def       snake_case	(     self					: List[str]				):
          return FocalNetConfig(
              image_size=self.image_size ,       patch_size=self.patch_size ,       num_channels=self.num_channels ,       embed_dim=self.embed_dim ,       hidden_sizes=self.hidden_sizes ,       depths=self.depths ,       num_heads=self.num_heads ,       window_size=self.window_size ,       mlp_ratio=self.mlp_ratio ,       qkv_bias=self.qkv_bias ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       drop_path_rate=self.drop_path_rate ,       hidden_act=self.hidden_act ,       use_absolute_embeddings=self.use_absolute_embeddings ,       path_norm=self.patch_norm ,       layer_norm_eps=self.layer_norm_eps ,       initializer_range=self.initializer_range ,       encoder_stride=self.encoder_stride ,       out_features=self.out_features ,       out_indices=self.out_indices ,       )







      def       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						FocalNetModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          __UpperCamelCase  		=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
          __UpperCamelCase  		=						int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

          self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, expected_seq_len, expected_dim)				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: Any ,       snake_case					: Dict ,       snake_case					: int				):
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				) ,       len(config.out_features				)				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,       [self.batch_size, self.image_size, 8, 8]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				) ,       len(config.out_features				)				)
          self.parent.assertListEqual(model.channels ,       config.hidden_sizes[:-1]				)

          # verify backbone works with out_features=None
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						FocalNetBackbone(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps				) ,       1				)
          self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,       [self.batch_size, self.image_size * 2, 4, 4]				)

          # verify channels
          self.parent.assertEqual(len(model.channels				) ,       1				)
          self.parent.assertListEqual(model.channels ,       [config.hidden_sizes[-1]]				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(
              result.reconstruction.shape ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForMaskedImageModeling(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.reconstruction.shape ,       (self.batch_size, 1, self.image_size, self.image_size)				)







      def       snake_case	(     self					: str ,       snake_case					: Tuple ,       snake_case					: List[str] ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						self.type_sequence_label_size
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)

          # test greyscale images
          __UpperCamelCase  		=						1
          __UpperCamelCase  		=						FocalNetForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()

          __UpperCamelCase  		=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          __UpperCamelCase  		=						model(snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.type_sequence_label_size)				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: Optional[Any]    =					(
          {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__			: List[str]    =					False

      lowerCAmelCase__			: Any    =					False
      lowerCAmelCase__			: Tuple    =					False
      lowerCAmelCase__			: List[Any]    =					False
      lowerCAmelCase__			: Optional[Any]    =					False







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       embed_dim=37 ,       has_text_modality=snake_case				)







      def       snake_case	(     self					: Union[str, 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					: List[Any]				):
          return







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      @unittest.skip(reason='''FocalNet does not use inputs_embeds'''				)
      def       snake_case	(     self					: Optional[Any]				):
          pass







      @unittest.skip(reason='''FocalNet does not use feedforward chunking'''				)
      def       snake_case	(     self					: int				):
          pass







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						model_class(snake_case				)
              self.assertIsInstance(model.get_input_embeddings() ,       (nn.Module)				)
              __UpperCamelCase  		=						model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case ,       nn.Linear				)				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						model_class(snake_case				)
              __UpperCamelCase  		=						inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __UpperCamelCase  		=						[*signature.parameters.keys()]

              __UpperCamelCase  		=						['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,       snake_case				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Union[str, Any] ,       snake_case					: int ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						model_class(snake_case				)
          model.to(snake_case				)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase  		=						model(**self._prepare_for_class(snake_case ,       snake_case				)				)

          __UpperCamelCase  		=						outputs.hidden_states

          __UpperCamelCase  		=						getattr(
              self.model_tester ,       '''expected_num_hidden_layers''' ,       len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(snake_case				) ,       snake_case				)

          # FocalNet has a different seq_length
          __UpperCamelCase  		=						(
              config.patch_size
              if isinstance(config.patch_size ,       collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase  		=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]				) ,       [num_patches, self.model_tester.embed_dim] ,       )

          __UpperCamelCase  		=						outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case				) ,       snake_case				)

          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						reshaped_hidden_states[0].shape
          __UpperCamelCase  		=						(
              reshaped_hidden_states[0].view(snake_case ,       snake_case ,       height * width				).permute(0 ,       2 ,       1				)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]				) ,       [num_patches, self.model_tester.embed_dim] ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size ,       collections.abc.Iterable				)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)

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

              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase  		=						3

          __UpperCamelCase  		=						(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size ,       collections.abc.Iterable				)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          __UpperCamelCase  		=						(
              config.patch_size
              if isinstance(config.patch_size ,       collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase  		=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          __UpperCamelCase  		=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase  		=						True
              self.check_hidden_states_output(snake_case ,       snake_case ,       snake_case ,       (padded_height, padded_width)				)







      @slow
      def       snake_case	(     self					: Union[str, Any]				):
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						FocalNetModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase  		=						_config_zero_init(snake_case				)
          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						model_class(config=snake_case				)
              for name, param in model.named_parameters():
                  if "embeddings" not in name and 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" ,       )







@require_vision
@require_torch
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: str				):
          # TODO update organization
          return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''				).to(snake_case				)
          __UpperCamelCase  		=						self.default_image_processor

          __UpperCamelCase  		=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       snake_case				)
          __UpperCamelCase  		=						torch.tensor([0.21_66, -0.43_68, 0.21_91]				).to(snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3] ,       snake_case ,       atol=1E-4				)				)
          self.assertTrue(outputs.logits.argmax(dim=-1				).item() ,       281				)







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					(FocalNetBackbone,) if is_torch_available() else ()
      lowerCAmelCase__			: Dict    =					FocalNetConfig

      lowerCAmelCase__			: List[str]    =					False







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						FocalNetModelTester(self				)






