



"""simple docstring"""




import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, 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 NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 	a__		:


   def __init__(				self   :				Dict,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				str=13,  lowerCAmelCase   :				Any=64,  lowerCAmelCase   :				Any=4,  lowerCAmelCase   :				str=3,  lowerCAmelCase   :				Tuple=16,  lowerCAmelCase   :				int=[1, 2, 1],  lowerCAmelCase   :				Tuple=[2, 4, 8],  lowerCAmelCase   :				str=3,  lowerCAmelCase   :				List[Any]=2.0,  lowerCAmelCase   :				Tuple=True,  lowerCAmelCase   :				Dict=0.0,  lowerCAmelCase   :				Tuple=0.0,  lowerCAmelCase   :				Dict=0.1,  lowerCAmelCase   :				Optional[Any]="gelu",  lowerCAmelCase   :				Union[str, Any]=True,  lowerCAmelCase   :				List[Any]=0.02,  lowerCAmelCase   :				int=1e-5,  lowerCAmelCase   :				int=True,  lowerCAmelCase   :				int=None,  lowerCAmelCase   :				List[Any]=True,  lowerCAmelCase   :				Optional[int]=10,  lowerCAmelCase   :				Dict=["stage1", "stage2"],  lowerCAmelCase   :				Tuple=[1, 2],  )     ->							List[str]:
         lowercase	:   Optional[int]											= parent
         lowercase	:   List[str]											= batch_size
         lowercase	:   int											= image_size
         lowercase	:   Union[str, Any]											= patch_size
         lowercase	:   List[Any]											= num_channels
         lowercase	:   Tuple											= embed_dim
         lowercase	:   Tuple											= depths
         lowercase	:   Optional[Any]											= num_heads
         lowercase	:   Union[str, Any]											= kernel_size
         lowercase	:   Optional[Any]											= mlp_ratio
         lowercase	:   Optional[int]											= qkv_bias
         lowercase	:   List[Any]											= hidden_dropout_prob
         lowercase	:   Optional[int]											= attention_probs_dropout_prob
         lowercase	:   List[str]											= drop_path_rate
         lowercase	:   Optional[Any]											= hidden_act
         lowercase	:   Any											= patch_norm
         lowercase	:   Any											= layer_norm_eps
         lowercase	:   Union[str, Any]											= initializer_range
         lowercase	:   List[Any]											= is_training
         lowercase	:   List[Any]											= scope
         lowercase	:   Dict											= use_labels
         lowercase	:   Tuple											= num_labels
         lowercase	:   Union[str, Any]											= out_features
         lowercase	:   Dict											= out_indices


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

         lowercase	:   Union[str, Any]											= None
         if self.use_labels:
               lowercase	:   int											= ids_tensor([self.batch_size],  self.num_labels      )

         lowercase	:   Union[str, Any]											= self.get_config()

         return config, pixel_values, labels


   def 							lowercase					(				self   :				List[Any]      )     ->							Optional[int]:
         return NatConfig(
             num_labels=self.num_labels,  image_size=self.image_size,  patch_size=self.patch_size,  num_channels=self.num_channels,  embed_dim=self.embed_dim,  depths=self.depths,  num_heads=self.num_heads,  kernel_size=self.kernel_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,  patch_norm=self.patch_norm,  layer_norm_eps=self.layer_norm_eps,  initializer_range=self.initializer_range,  out_features=self.out_features,  out_indices=self.out_indices,  )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Optional[int]      )     ->							str:
         lowercase	:   int											= NatModel(config=lowercase__      )
         model.to(lowercase__      )
         model.eval()
         lowercase	:   Optional[Any]											= model(lowercase__      )

         lowercase	:   Optional[int]											= (config.image_size // config.patch_size) // (2 ** (len(config.depths      ) - 1))
         lowercase	:   str											= int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

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


   def 							lowercase					(				self   :				Any,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				str      )     ->							Tuple:
         lowercase	:   int											= NatForImageClassification(lowercase__      )
         model.to(lowercase__      )
         model.eval()
         lowercase	:   List[str]											= model(lowercase__,  labels=lowercase__      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)      )

         # test greyscale images
         lowercase	:   str											= 1
         lowercase	:   Dict											= NatForImageClassification(lowercase__      )
         model.to(lowercase__      )
         model.eval()

         lowercase	:   int											= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         lowercase	:   Optional[Any]											= model(lowercase__      )
         self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)      )


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				int,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   int											= NatBackbone(config=lowercase__      )
         model.to(lowercase__      )
         model.eval()
         lowercase	:   Any											= model(lowercase__      )

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps      ),  len(config.out_features      )      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      ),  [self.batch_size, model.channels[0], 16, 16]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      ),  len(config.out_features      )      )

         # verify backbone works with out_features=None
         lowercase	:   Dict											= None
         lowercase	:   Any											= NatBackbone(config=lowercase__      )
         model.to(lowercase__      )
         model.eval()
         lowercase	:   str											= model(lowercase__      )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps      ),  1      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      ),  [self.batch_size, model.channels[-1], 4, 4]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      ),  1      )






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




@require_natten
@require_torch
class 	a__		(    a__,	a__,	unittest.TestCase  ):
   _lowerCamelCase       				=					(
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _lowerCamelCase       				=					(
       {"""feature-extraction""": NatModel, """image-classification""": NatForImageClassification}
       if is_torch_available()
       else {}
   )
   _lowerCamelCase       				=					False

   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				List[Any]      )     ->							List[Any]:
         lowercase	:   List[str]											= NatModelTester(self      )
         lowercase	:   List[str]											= ConfigTester(self,  config_class=lowercase__,  embed_dim=37      )


   def 							lowercase					(				self   :				Dict      )     ->							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 							lowercase					(				self   :				Optional[Any]      )     ->							int:
         return


   def 							lowercase					(				self   :				Dict      )     ->							Optional[int]:
         lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__      )


   def 							lowercase					(				self   :				Dict      )     ->							Dict:
         lowercase	:   Optional[int]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase__      )


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Any:
         lowercase	:   List[Any]											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowercase__      )


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


   @unittest.skip(reason='Nat does not use feedforward chunking'      )
   def 							lowercase					(				self   :				Union[str, Any]      )     ->							List[Any]:
         pass


   def 							lowercase					(				self   :				int      )     ->							str:
         lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase	:   List[Any]											= model_class(lowercase__      )
               self.assertIsInstance(model.get_input_embeddings(),  (nn.Module)      )
               lowercase	:   str											= model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowercase__,  nn.Linear      )      )


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

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

               lowercase	:   str											= ['''pixel_values''']
               self.assertListEqual(arg_names[:1],  lowercase__      )


   def 							lowercase					(				self   :				List[str]      )     ->							List[Any]:
         self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'      )


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				List[str],  lowerCAmelCase   :				Tuple      )     ->							Any:
         lowercase	:   str											= model_class(lowercase__      )
         model.to(lowercase__      )
         model.eval()

         with torch.no_grad():
               lowercase	:   Optional[Any]											= model(**self._prepare_for_class(lowercase__,  lowercase__      )      )

         lowercase	:   Any											= outputs.hidden_states

         lowercase	:   Optional[Any]											= getattr(
             self.model_tester,  'expected_num_hidden_layers',  len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(lowercase__      ),  lowercase__      )

         # Nat has a different seq_length
         lowercase	:   Tuple											= (
             config.patch_size
             if isinstance(config.patch_size,  collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         lowercase	:   int											= image_size[0] // patch_size[0]
         lowercase	:   Optional[Any]											= image_size[1] // patch_size[1]

         self.assertListEqual(
             list(hidden_states[0].shape[-3:]      ),  [height, width, self.model_tester.embed_dim],  )

         if model_class.__name__ != "NatBackbone":
               lowercase	:   Dict											= outputs.reshaped_hidden_states
               self.assertEqual(len(lowercase__      ),  lowercase__      )

               lowercase	:   Any											= reshaped_hidden_states[0].shape
               lowercase	:   str											= (
                   reshaped_hidden_states[0].view(lowercase__,  lowercase__,  lowercase__,  lowercase__      ).permute(0,  2,  3,  1      )
               )
               self.assertListEqual(
                   list(reshaped_hidden_states.shape[-3:]      ),  [height, width, self.model_tester.embed_dim],  )


   def 							lowercase					(				self   :				int      )     ->							Union[str, Any]:
         lowercase	:   str											= self.model_tester.prepare_config_and_inputs_for_common()

         lowercase	:   Dict											= (
             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:
               lowercase	:   Optional[Any]											= True
               self.check_hidden_states_output(lowercase__,  lowercase__,  lowercase__,  lowercase__      )

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

               self.check_hidden_states_output(lowercase__,  lowercase__,  lowercase__,  lowercase__      )


   @slow
   def 							lowercase					(				self   :				Tuple      )     ->							Dict:





         for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase	:   Optional[int]											= NatModel.from_pretrained(lowercase__      )
               self.assertIsNotNone(lowercase__      )






   def 							lowercase					(				self   :				Tuple      )     ->							Dict:
         lowercase	:   Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

         lowercase	:   str											= _config_zero_init(lowercase__      )
         for model_class in self.all_model_classes:
               lowercase	:   int											= model_class(config=lowercase__      )
               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_natten
@require_vision
@require_torch
class 	a__		(    unittest.TestCase  ):


   @cached_property
   def 							lowercase					(				self   :				Union[str, Any]      )     ->							int:
         return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'      ) if is_vision_available() else None






   @slow
   def 							lowercase					(				self   :				Tuple      )     ->							Dict:
         lowercase	:   Tuple											= NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'      ).to(lowercase__      )
         lowercase	:   Optional[int]											= self.default_image_processor

         lowercase	:   List[str]											= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         lowercase	:   List[Any]											= image_processor(images=lowercase__,  return_tensors='pt'      ).to(lowercase__      )

         # forward pass
         with torch.no_grad():
               lowercase	:   Tuple											= model(**lowercase__      )

         # verify the logits
         lowercase	:   List[str]											= torch.Size((1, 1000)      )
         self.assertEqual(outputs.logits.shape,  lowercase__      )
         lowercase	:   List[str]											= torch.tensor([0.3805, -0.8676, -0.3912]      ).to(lowercase__      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowercase__,  atol=1e-4      )      )




@require_torch
@require_natten
class 	a__		(    unittest.TestCase,	a__  ):
   _lowerCamelCase       				=					(NatBackbone,) if is_torch_available() else ()
   _lowerCamelCase       				=					NatConfig


   def 							lowercase					(				self   :				List[Any]      )     ->							Dict:
         lowercase	:   Any											= NatModelTester(self      )







'''simple docstring'''

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
UpperCAmelCase       :		List[Any]		=				{
    '1': ['2', '3', '4', '5'],
    '2': ['1', '3', '4', '5'],
    '3': ['1', '2', '4', '5', '10'],
    '4': ['1', '2', '3', '5', '6'],
    '5': ['1', '2', '3', '4', '7'],
    '6': ['7', '8', '9', '10', '4'],
    '7': ['6', '8', '9', '10', '5'],
    '8': ['6', '7', '9', '10'],
    '9': ['6', '7', '8', '10'],
    '10': ['6', '7', '8', '9', '3'],
}




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Dict         =  {node: {node} for node in graph}

      _snake_case		:						str         =  {node: graph[node][:] for node in graph}

      while len(lowerCAmelCase_						) > 2:
            # Choose a random edge.
            _snake_case		:						Optional[Any]         =  random.choice(list(graph_copy.keys()						)						)
            _snake_case		:						Union[str, Any]         =  random.choice(graph_copy[u]						)

            # Contract edge (u, v) to new node uv
            _snake_case		:						List[str]         =  u + v
            _snake_case		:						List[str]         =  list(set(graph_copy[u] + graph_copy[v]						)						)
            uv_neighbors.remove(lowerCAmelCase_						)
            uv_neighbors.remove(lowerCAmelCase_						)
            _snake_case		:						Tuple         =  uv_neighbors
            for neighbor in uv_neighbors:
                  graph_copy[neighbor].append(lowerCAmelCase_						)

            _snake_case		:						Tuple         =  set(contracted_nodes[u].union(contracted_nodes[v]						)						)

            # Remove nodes u and v.
            del graph_copy[u]
            del graph_copy[v]
            for neighbor in uv_neighbors:
                  if u in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)
                  if v in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)

    # Find cutset.
      _snake_case		:						Any         =  [contracted_nodes[node] for node in graph_copy]
      return {
          (node, neighbor)
          for node in groups[0]
          for neighbor in graph[node]
          if neighbor in groups[1]
      }


if __name__ == "__main__":
   print(partition_graph(TEST_GRAPH))


