




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					List[str]          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[Any]          =  {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """roformer"""

						def __init__(		self :str     ,			__snake_case :Tuple=5_00_00     ,			__snake_case :Optional[Any]=None     ,			__snake_case :Union[str, Any]=7_68     ,			__snake_case :List[Any]=12     ,			__snake_case :int=12     ,			__snake_case :str=30_72     ,			__snake_case :Union[str, Any]="gelu"     ,			__snake_case :List[str]=0.1     ,			__snake_case :List[str]=0.1     ,			__snake_case :Union[str, Any]=15_36     ,			__snake_case :str=2     ,			__snake_case :Any=0.02     ,			__snake_case :Tuple=1E-12     ,			__snake_case :Dict=0     ,			__snake_case :Optional[int]=False     ,			__snake_case :Optional[int]=True     ,			**__snake_case :Optional[Any]     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			**__snake_case			)

										__magic_name__						:						Dict										=vocab_size
										__magic_name__						:						int										=hidden_size if embedding_size is None else embedding_size
										__magic_name__						:						str										=hidden_size
										__magic_name__						:						Any										=num_hidden_layers
										__magic_name__						:						int										=num_attention_heads
										__magic_name__						:						List[Any]										=hidden_act
										__magic_name__						:						Dict										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						List[str]										=attention_probs_dropout_prob
										__magic_name__						:						List[Any]										=max_position_embeddings
										__magic_name__						:						Optional[int]										=type_vocab_size
										__magic_name__						:						int										=initializer_range
										__magic_name__						:						int										=layer_norm_eps
										__magic_name__						:						Optional[Any]										=rotary_value
										__magic_name__						:						Optional[int]										=use_cache




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						Optional[int]										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						str										={0: """batch""", 1: """sequence"""}
										__magic_name__						:						List[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]			)










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						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
				__magic_name__						:						Tuple										=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
								__magic_name__						:						Tuple										=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]:
																__magic_name__						:						Tuple										=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()

							UpperCAmelCase_		:					Optional[int]          =  {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)}""")




