code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  
  from transformers import BlipTextConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_torch_available():
  					import torch
  
  					from transformers import BlipTextModel
  					from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			Dict		,    a_							:			Optional[int]=12		,    a_							:			Dict=7		,    a_							:			Union[str, Any]=True		,    a_							:			List[Any]=True		,    a_							:			Any=True		,    a_							:			int=99		,    a_							:			List[str]=32		,    a_							:			Tuple=32		,    a_							:			Optional[int]=5		,    a_							:			Tuple=4		,    a_							:			List[Any]=37		,    a_							:			Optional[Any]=0.1		,    a_							:			str=0.1		,    a_							:			List[Any]=5_12		,    a_							:			Optional[int]=0.02		,    a_							:			List[Any]=0		,    a_							:			Optional[Any]=None		,    ):
  				lowerCAmelCase_     :							Tuple             =    parent
  				lowerCAmelCase_     :							Optional[Any]             =    batch_size
  				lowerCAmelCase_     :							List[Any]             =    seq_length
  				lowerCAmelCase_     :							Optional[Any]             =    is_training
  				lowerCAmelCase_     :							Optional[Any]             =    use_input_mask
  				lowerCAmelCase_     :							int             =    use_labels
  				lowerCAmelCase_     :							Optional[Any]             =    vocab_size
  				lowerCAmelCase_     :							Dict             =    hidden_size
  				lowerCAmelCase_     :							Union[str, Any]             =    projection_dim
  				lowerCAmelCase_     :							Any             =    num_hidden_layers
  				lowerCAmelCase_     :							Any             =    num_attention_heads
  				lowerCAmelCase_     :							int             =    intermediate_size
  				lowerCAmelCase_     :							Dict             =    dropout
  				lowerCAmelCase_     :							List[str]             =    attention_dropout
  				lowerCAmelCase_     :							Dict             =    max_position_embeddings
  				lowerCAmelCase_     :							Union[str, Any]             =    initializer_range
  				lowerCAmelCase_     :							int             =    scope
  				lowerCAmelCase_     :							Optional[int]             =    bos_token_id
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_     :							int             =    ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    None
  				if self.use_input_mask:
  						lowerCAmelCase_     :							int             =    random_attention_mask([self.batch_size, self.seq_length]			)
  
  				if input_mask is not None:
  						lowerCAmelCase_     :							Union[str, Any]             =    input_mask.shape
  						lowerCAmelCase_     :							int             =    np.random.randint(1		,    seq_length - 1		,    size=(batch_size,)			)
  						for batch_idx, start_index in enumerate(a_			):
  								lowerCAmelCase_     :							List[Any]             =    1
  								lowerCAmelCase_     :							Optional[Any]             =    0
  
  				lowerCAmelCase_     :							Dict             =    self.get_config()
  
  				return config, input_ids, input_mask
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				return BlipTextConfig(
  				    vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    projection_dim=self.projection_dim		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    dropout=self.dropout		,    attention_dropout=self.attention_dropout		,    max_position_embeddings=self.max_position_embeddings		,    initializer_range=self.initializer_range		,    bos_token_id=self.bos_token_id		,    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			Tuple		,    a_							:			Tuple		,    a_							:			Tuple			):
  				lowerCAmelCase_     :							List[Any]             =    BlipTextModel(config=a_			)
  				model.to(a_			)
  				model.eval()
  				with torch.no_grad():
  						lowerCAmelCase_     :							Optional[Any]             =    model(a_		,    attention_mask=a_			)
  						lowerCAmelCase_     :							Any             =    model(a_			)
  				self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)
  				self.parent.assertEqual(result.pooler_output.shape		,    (self.batch_size, self.hidden_size)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				lowerCAmelCase_     :							Dict             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_     :							Union[str, Any]             =    config_and_inputs
  				lowerCAmelCase_     :							Dict             =    {"input_ids": input_ids, "attention_mask": input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Dict      =							(BlipTextModel,) if is_torch_available() else ()
  		a_			:       Tuple      =							False
  		a_			:       Optional[int]      =							False
  		a_			:       List[Any]      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							str             =    BlipTextModelTester(self			)
  				lowerCAmelCase_     :							Any             =    ConfigTester(self		,    config_class=a_		,    hidden_size=37			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				self.config_tester.run_common_tests()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="Blip does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
  		def      lowerCamelCase (     self							:			str			):
  				pass
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Any			):
  				for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						lowerCAmelCase_     :							str             =    BlipTextModel.from_pretrained(a_			)
  						self.assertIsNotNone(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				super().test_pt_tf_model_equivalence(allow_missing_keys=a_			)
  
style_context: 
  
  
  
  
  
  
  """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
  lowercase__      =				{
      """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 	__lowerCamelCase			(						__UpperCamelCase )		->					set[tuple[str, str]]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Union[str, Any]             =    {node: {node} for node in graph}
  
  		lowerCAmelCase_     :							List[str]             =    {node: graph[node][:] for node in graph}
  
  		while len(__UpperCamelCase ) > 2:
  				# Choose a random edge.
  				lowerCAmelCase_     :							int             =    random.choice(list(graph_copy.keys() ) )
  				lowerCAmelCase_     :							Any             =    random.choice(graph_copy[u] )
  
  				# Contract edge (u, v) to new node uv
  				lowerCAmelCase_     :							Union[str, Any]             =    u + v
  				lowerCAmelCase_     :							List[Any]             =    list(set(graph_copy[u] + graph_copy[v] ) )
  				uv_neighbors.remove(__UpperCamelCase )
  				uv_neighbors.remove(__UpperCamelCase )
  				lowerCAmelCase_     :							str             =    uv_neighbors
  				for neighbor in uv_neighbors:
  						graph_copy[neighbor].append(__UpperCamelCase )
  
  				lowerCAmelCase_     :							Optional[int]             =    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(__UpperCamelCase )
  						if v in graph_copy[neighbor]:
  								graph_copy[neighbor].remove(__UpperCamelCase )
  
      # Find cutset.
  		lowerCAmelCase_     :							Union[str, 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))
  