


'''simple docstring'''


import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, 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 torch import nn

      from transformers import (
          MODEL_MAPPING,
          DataaVecVisionForImageClassification,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
      )
      from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor







class        __magic_name__  :

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase=100 ,    lowerCamelCase=13 ,    lowerCamelCase=30 ,    lowerCamelCase=2 ,    lowerCamelCase=3 ,    lowerCamelCase=True ,    lowerCamelCase=True ,    lowerCamelCase=32 ,    lowerCamelCase=4 ,    lowerCamelCase=4 ,    lowerCamelCase=37 ,    lowerCamelCase="gelu" ,    lowerCamelCase=0.1 ,    lowerCamelCase=0.1 ,    lowerCamelCase=10 ,    lowerCamelCase=0.02 ,    lowerCamelCase=3 ,    lowerCamelCase=None ,    lowerCamelCase=[0, 1, 2, 3] ,    ):


          '''simple docstring'''





          __A  :					Tuple						     =       parent
          __A  :					Optional[int]						     =       100
          __A  :					Dict						     =       batch_size
          __A  :					str						     =       image_size
          __A  :					Optional[Any]						     =       patch_size
          __A  :					Tuple						     =       num_channels
          __A  :					Optional[Any]						     =       is_training
          __A  :					Tuple						     =       use_labels
          __A  :					int						     =       hidden_size
          __A  :					Union[str, Any]						     =       num_hidden_layers
          __A  :					Optional[int]						     =       num_attention_heads
          __A  :					List[Any]						     =       intermediate_size
          __A  :					List[Any]						     =       hidden_act
          __A  :					str						     =       hidden_dropout_prob
          __A  :					Union[str, Any]						     =       attention_probs_dropout_prob
          __A  :					Any						     =       type_sequence_label_size
          __A  :					List[Any]						     =       initializer_range
          __A  :					str						     =       scope
          __A  :					List[Any]						     =       out_indices
          __A  :					int						     =       num_labels

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __A  :					int						     =       (image_size // patch_size) ** 2
          __A  :					Tuple						     =       num_patches + 1


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A  :					Tuple						     =       None
          __A  :					int						     =       None
          if self.use_labels:
               __A  :					Dict						     =       ids_tensor([self.batch_size] ,    self.type_sequence_label_size		)
               __A  :					Tuple						     =       ids_tensor([self.batch_size, self.image_size, self.image_size] ,    self.num_labels		)

          __A  :					List[Any]						     =       self.get_config()

          return config, pixel_values, labels, pixel_labels


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return DataaVecVisionConfig(
              vocab_size=self.vocab_size ,    image_size=self.image_size ,    patch_size=self.patch_size ,    num_channels=self.num_channels ,    hidden_size=self.hidden_size ,    num_hidden_layers=self.num_hidden_layers ,    num_attention_heads=self.num_attention_heads ,    intermediate_size=self.intermediate_size ,    hidden_act=self.hidden_act ,    hidden_dropout_prob=self.hidden_dropout_prob ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob ,    is_decoder=_lowerCamelCase ,    initializer_range=self.initializer_range ,    out_indices=self.out_indices ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[Any]						     =       DataaVecVisionModel(config=_lowerCamelCase		)
          model.to(_lowerCamelCase		)
          model.eval()
          __A  :					List[str]						     =       model(_lowerCamelCase		)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          __A  :					int						     =       (self.image_size // self.patch_size) ** 2
          self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, num_patches + 1, self.hidden_size)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       self.type_sequence_label_size
          __A  :					Optional[int]						     =       DataaVecVisionForImageClassification(_lowerCamelCase		)
          model.to(_lowerCamelCase		)
          model.eval()
          __A  :					Tuple						     =       model(_lowerCamelCase ,    labels=_lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.type_sequence_label_size)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Dict						     =       self.num_labels
          __A  :					Union[str, Any]						     =       DataaVecVisionForSemanticSegmentation(_lowerCamelCase		)
          model.to(_lowerCamelCase		)
          model.eval()
          __A  :					int						     =       model(_lowerCamelCase		)
          self.parent.assertEqual(
              result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
          __A  :					Any						     =       model(_lowerCamelCase ,    labels=_lowerCamelCase		)
          self.parent.assertEqual(
              result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.prepare_config_and_inputs()
          __A  :					Union[str, Any]						     =       config_and_inputs
          __A  :					Tuple						     =       {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        __magic_name__  (					lowerCAmelCase__						,		lowerCAmelCase__						,		unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							(
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     lowerCamelCase__						=							(
         {
             'feature-extraction': DataaVecVisionModel,
             'image-classification': DataaVecVisionForImageClassification,
             'image-segmentation': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowerCamelCase__						=							False
     lowerCamelCase__						=							False
     lowerCamelCase__						=							False


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       DataaVecVisionModelTester(self		)
          __A  :					Union[str, Any]						     =       ConfigTester(
              self ,    config_class=_lowerCamelCase ,    has_text_modality=_lowerCamelCase ,    hidden_size=37		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          pass


     @require_torch_multi_gpu
     @unittest.skip(
         reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"		)
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       self.model_tester.prepare_config_and_inputs_for_common()

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       self.model_tester.prepare_config_and_inputs_for_common()

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

               __A  :					Union[str, Any]						     =       ["""pixel_values"""]
               self.assertListEqual(arg_names[:1] ,    _lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*_lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          if not self.model_tester.is_training:
               return

          __A  :					List[Any]						     =       self.model_tester.prepare_config_and_inputs_for_common()
          __A  :					Any						     =       True

          for model_class in self.all_model_classes:
               if model_class in [*get_values(_lowerCamelCase		)]:
                    continue

               __A  :					List[Any]						     =       model_class(_lowerCamelCase		)
               model.to(_lowerCamelCase		)
               model.train()
               __A  :					Dict						     =       self._prepare_for_class(_lowerCamelCase ,    _lowerCamelCase ,    return_labels=_lowerCamelCase		)
               __A  :					List[Any]						     =       model(**_lowerCamelCase		).loss
               loss.backward()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
               return

          __A  :					Any						     =       False
          __A  :					Any						     =       True

          for model_class in self.all_model_classes:
               if model_class in [*get_values(_lowerCamelCase		)] or not model_class.supports_gradient_checkpointing:
                    continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                    __A  :					str						     =       inputs_dict["""pixel_values"""].shape
                    __A  :					List[Any]						     =       torch.zeros(
                        [self.model_tester.batch_size, height, width] ,    device=_lowerCamelCase		).long()
               __A  :					Optional[Any]						     =       model_class(_lowerCamelCase		)
               model.gradient_checkpointing_enable()
               model.to(_lowerCamelCase		)
               model.train()
               __A  :					str						     =       self._prepare_for_class(_lowerCamelCase ,    _lowerCamelCase ,    return_labels=_lowerCamelCase		)
               __A  :					Optional[int]						     =       model(**_lowerCamelCase		).loss
               loss.backward()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       self.model_tester.prepare_config_and_inputs_for_common()

          __A  :					Optional[int]						     =       _config_zero_init(_lowerCamelCase		)
          for model_class in self.all_model_classes:
               __A  :					List[str]						     =       model_class(config=_lowerCamelCase		)
               for name, param in model.named_parameters():
                    # we skip lambda parameters as these require special initial values
                    # determined by config.layer_scale_init_value
                    if "lambda" in name:
                         continue
                    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" ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=2E-4 ,    lowerCamelCase="outputs" ,    lowerCamelCase=None		):


          '''simple docstring'''





          super().check_pt_tf_outputs(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase		)


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A  :					str						     =       DataaVecVisionModel.from_pretrained(_lowerCamelCase		)
               self.assertIsNotNone(_lowerCamelCase		)


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Union[str, Any]						     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
     return image







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

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return (
              BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"		) if is_vision_available() else None
          )


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"		).to(
              _lowerCamelCase		)

          __A  :					int						     =       self.default_image_processor
          __A  :					Tuple						     =       prepare_img()
          __A  :					Union[str, Any]						     =       image_processor(images=_lowerCamelCase ,    return_tensors="pt"		).to(_lowerCamelCase		)

          # forward pass
          with torch.no_grad():
               __A  :					str						     =       model(**_lowerCamelCase		)
          __A  :					Union[str, Any]						     =       outputs.logits

          # verify the logits
          __A  :					Optional[int]						     =       torch.Size((1, 1000)		)
          self.assertEqual(logits.shape ,    _lowerCamelCase		)

          __A  :					List[str]						     =       torch.tensor([0.3277, -0.1395, 0.0911]		).to(_lowerCamelCase		)

          self.assertTrue(torch.allclose(logits[0, :3] ,    _lowerCamelCase ,    atol=1E-4		)		)

          __A  :					Dict						     =       [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
          self.assertEqual(logits[0].topk(2		).indices.cpu().tolist() ,    _lowerCamelCase		)






'''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          =     {
    """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 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       {node: {node} for node in graph}

     __A  :					List[str]						     =       {node: graph[node][:] for node in graph}

     while len(SCREAMING_SNAKE_CASE       ) > 2:
          # Choose a random edge.
          __A  :					Tuple						     =       random.choice(list(graph_copy.keys()       )       )
          __A  :					Any						     =       random.choice(graph_copy[u]       )

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

          __A  :					str						     =       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(SCREAMING_SNAKE_CASE       )
               if v in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )

    # Find cutset.
     __A  :					int						     =       [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))

