



'''simple docstring'''

from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer

class      __magic_name__						:







       def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Dict		):
         raise NotImplementedError()







       def 							__lowercase      (							self :	Optional[int]		):
         raise NotImplementedError()

class      __magic_name__						(   _UpperCamelCase       ):







       def __init__(							self :	Optional[int]     ,_UpperCAmelCase :	"AutoTokenizer"     ,_UpperCAmelCase :	bool = False     ,**_UpperCAmelCase :	int		):
         _a					:				int				=							tokenizer
         _a					:				Tuple				=							skip_prompt
         _a					:				Optional[int]				=							decode_kwargs

         # variables used in the streaming process
         _a					:				Union[str, Any]				=							[]
         _a					:				Tuple				=							0
         _a					:				Union[str, Any]				=							True







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Any		):
         if len(value.shape		) > 1 and value.shape[0] > 1:
           raise ValueError('TextStreamer only supports batch size 1'		)
         elif len(value.shape		) > 1:
           _a					:				int				=							value[0]

         if self.skip_prompt and self.next_tokens_are_prompt:
           _a					:				Tuple				=							False
           return

         # Add the new token to the cache and decodes the entire thing.
         self.token_cache.extend(value.tolist()		)
         _a					:				List[Any]				=							self.tokenizer.decode(self.token_cache     ,**self.decode_kwargs		)

         # After the symbol for a new line, we flush the cache.
         if text.endswith('\n'		):
           _a					:				Union[str, Any]				=							text[self.print_len :]
           _a					:				List[Any]				=							[]
           _a					:				int				=							0
         # If the last token is a CJK character, we print the characters.
         elif len(_UpperCAmelCase		) > 0 and self._is_chinese_char(ord(text[-1]		)		):
           _a					:				Dict				=							text[self.print_len :]
           self.print_len += len(_UpperCAmelCase		)
         # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
         # which may change with the subsequent token -- there are probably smarter ways to do this!)
         else:
           _a					:				List[Any]				=							text[self.print_len : text.rfind(' '		) + 1]
           self.print_len += len(_UpperCAmelCase		)

         self.on_finalized_text(_UpperCAmelCase		)







       def 							__lowercase      (							self :	Dict		):
         # Flush the cache, if it exists
         if len(self.token_cache		) > 0:
           _a					:				int				=							self.tokenizer.decode(self.token_cache     ,**self.decode_kwargs		)
           _a					:				str				=							text[self.print_len :]
           _a					:				Dict				=							[]
           _a					:				List[str]				=							0
         else:
           _a					:				Optional[int]				=							''

         _a					:				List[str]				=							True
         self.on_finalized_text(_UpperCAmelCase     ,stream_end=_UpperCAmelCase		)







       def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	bool = False		):
         print(_UpperCAmelCase     ,flush=_UpperCAmelCase     ,end='' if not stream_end else None		)







       def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Any		):
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0X4_E00 and cp <= 0X9_FFF)
             or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
             or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
             or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
             or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
             or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
             or (cp >= 0XF_900 and cp <= 0XF_AFF)
             or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
         ):  #
           return True

         return False

class      __magic_name__						(   _UpperCamelCase       ):







       def __init__(							self :	Any     ,_UpperCAmelCase :	"AutoTokenizer"     ,_UpperCAmelCase :	bool = False     ,_UpperCAmelCase :	Optional[float] = None     ,**_UpperCAmelCase :	Tuple		):
         super().__init__(_UpperCAmelCase     ,_UpperCAmelCase     ,**_UpperCAmelCase		)
         _a					:				Dict				=							Queue()
         _a					:				Optional[Any]				=							None
         _a					:				Optional[int]				=							timeout







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	bool = False		):
         self.text_queue.put(_UpperCAmelCase     ,timeout=self.timeout		)
         if stream_end:
           self.text_queue.put(self.stop_signal     ,timeout=self.timeout		)







       def __iter__(							self :	Optional[int]		):
         return self







       def 							__lowercase      (							self :	List[Any]		):
         _a					:				Union[str, Any]				=							self.text_queue.get(timeout=self.timeout		)
         if value == self.stop_signal:
           raise StopIteration()
         else:
           return value









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




