






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class        lowercase__       (     _UpperCAmelCase		):
	a_             =["""image_processor""", """tokenizer"""]
	a_             ="""AutoImageProcessor"""
	a_             ="""AutoTokenizer"""

	def __init__(							self						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."						,      __UpperCAmelCase						,      )
													lowerCAmelCase__	 =  kwargs.pop("feature_extractor"    )

							lowerCAmelCase__	 =  image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
													raise ValueError("You have to specify an image_processor."    )
							if tokenizer is None:
													raise ValueError("You have to specify a tokenizer."    )

							super().__init__(__UpperCAmelCase						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  self.image_processor

	def __call__(							self						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Optional[Any]:







							'''simple docstring'''





							if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."    )

							if text is not None:
													lowerCAmelCase__	 =  self.tokenizer(__UpperCAmelCase						,      return_tensors=__UpperCAmelCase						,      **__UpperCAmelCase    )

							if images is not None:
													lowerCAmelCase__	 =  self.image_processor(__UpperCAmelCase						,      return_tensors=__UpperCAmelCase						,      **__UpperCAmelCase    )

							if text is not None and images is not None:
													lowerCAmelCase__	 =  image_features.pixel_values
													return encoding
							elif text is not None:
													return encoding
							else:
													return BatchEncoding(data=dict(**__UpperCAmelCase    )						,      tensor_type=__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   str:







							'''simple docstring'''





							return self.tokenizer.batch_decode(*__UpperCAmelCase						,      **__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Optional[Any]:







							'''simple docstring'''





							return self.tokenizer.decode(*__UpperCAmelCase						,      **__UpperCAmelCase    )

	@property
	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.tokenizer.model_input_names
							lowerCAmelCase__	 =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

	@property
	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,      __UpperCAmelCase						,      )
							return self.image_processor_class


	@property
	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,      __UpperCAmelCase						,      )
							return self.image_processor














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
a_							=      {
    '''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					(       UpperCamelCase_   :			dict[str, list[str]]				)	->				set[tuple[str, str]]:
						"""simple docstring"""







						lowerCAmelCase__	 =  {node: {node} for node in graph}

						lowerCAmelCase__	 =  {node: graph[node][:] for node in graph}

						while len(UpperCamelCase_				) > 2:
												# Choose a random edge.
												lowerCAmelCase__	 =  random.choice(list(graph_copy.keys()				)				)
												lowerCAmelCase__	 =  random.choice(graph_copy[u]				)

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

												lowerCAmelCase__	 =  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__	 =  [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))






