



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






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					Optional[Any]		   =							["image_processor", "tokenizer"]
	a__      :					Dict		   =							"EfficientNetImageProcessor"
	a__      :					Any		   =							("BertTokenizer", "BertTokenizerFast")




	def __init__(	self	:						List[str]						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						str     )			-> Dict:
							super().__init__(__UpperCAmelCase						,  __UpperCAmelCase     )




	def __call__(	self	:						List[str]						,  __UpperCAmelCase	:						List[str]=None						,  __UpperCAmelCase	:						List[Any]=None						,  __UpperCAmelCase	:						Tuple="max_length"						,  __UpperCAmelCase	:						Optional[Any]=64						,  __UpperCAmelCase	:						Tuple=None						,  **__UpperCAmelCase	:						Dict     )			-> Union[str, Any]:
							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:
													UpperCAmelCase_=   self.tokenizer(
													    __UpperCAmelCase						,  padding=__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  **__UpperCAmelCase     )

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

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]						,  *__UpperCAmelCase	:						Union[str, Any]						,  **__UpperCAmelCase	:						Optional[int]     )			-> Any:
							return self.tokenizer.batch_decode(*__UpperCAmelCase						,  **__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						int						,  *__UpperCAmelCase	:						int						,  **__UpperCAmelCase	:						str     )			-> Dict:
							return self.tokenizer.decode(*__UpperCAmelCase						,  **__UpperCAmelCase     )




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Tuple:
							UpperCAmelCase_=   self.tokenizer.model_input_names
							UpperCAmelCase_=   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )









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     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



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

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

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

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

												UpperCAmelCase_=   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.
						UpperCAmelCase_=   [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))




