




from __future__ import annotations

from queue import Queue




class     a__ :







					def __init__(			self				)						->					None:



										'''simple docstring'''



										a											=   {}







					def     lowerCAmelCase_       (			self				)						->					None:



										'''simple docstring'''



										for i in self.vertices:
															print(A						,	" : "						,	" -> ".join([str(A				) for j in self.vertices[i]]				)				)







					def     lowerCAmelCase_       (			self						,	A						,	A				)						->					None:



										'''simple docstring'''



										if from_vertex in self.vertices:
															self.vertices[from_vertex].append(A				)
										else:
															a											=   [to_vertex]







					def     lowerCAmelCase_       (			self						,	A				)						->					set[int]:



										'''simple docstring'''



										a											=   set()

										# create a first in first out queue to store all the vertices for BFS
										a											=   Queue()

										# mark the source node as visited and enqueue it
										visited.add(A				)
										queue.put(A				)

										while not queue.empty():
															a											=   queue.get()

															# loop through all adjacent vertex and enqueue it if not yet visited
															for adjacent_vertex in self.vertices[vertex]:
																				if adjacent_vertex not in visited:
																									queue.put(A				)
																									visited.add(A				)
										return visited


if __name__ == "__main__":
			from doctest import testmod

			testmod(verbose=True)

			lowercase__   :    Optional[Any] 					=      Graph()
			g.add_edge(0, 1)
			g.add_edge(0, 2)
			g.add_edge(1, 2)
			g.add_edge(2, 0)
			g.add_edge(2, 3)
			g.add_edge(3, 3)

			g.print_graph()
			# 0  :  1 -> 2
			# 1  :  2
			# 2  :  0 -> 3
			# 3  :  3

			assert sorted(g.bfs(2)) == [0, 1, 2, 3]







from collections import OrderedDict
from typing import Mapping

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


lowercase__   :    str 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class     a__ (      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     a	:    Optional[int]	      =			"""mobilebert"""







     def __init__(			self						,	A=30522						,	A=512						,	A=24						,	A=4						,	A=512						,	A="relu"						,	A=0.0						,	A=0.1						,	A=512						,	A=2						,	A=0.0_2						,	A=1e-12						,	A=0						,	A=128						,	A=True						,	A=True						,	A=128						,	A=False						,	A=True						,	A=4						,	A="no_norm"						,	A=True						,	A=None						,	**A						,	)						->					str:



          '''simple docstring'''



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

          a											=   vocab_size
          a											=   hidden_size
          a											=   num_hidden_layers
          a											=   num_attention_heads
          a											=   hidden_act
          a											=   intermediate_size
          a											=   hidden_dropout_prob
          a											=   attention_probs_dropout_prob
          a											=   max_position_embeddings
          a											=   type_vocab_size
          a											=   initializer_range
          a											=   layer_norm_eps
          a											=   embedding_size
          a											=   trigram_input
          a											=   use_bottleneck
          a											=   intra_bottleneck_size
          a											=   use_bottleneck_attention
          a											=   key_query_shared_bottleneck
          a											=   num_feedforward_networks
          a											=   normalization_type
          a											=   classifier_activation

          if self.use_bottleneck:
               a											=   intra_bottleneck_size
          else:
               a											=   hidden_size

          a											=   classifier_dropout







class     a__ (      UpperCamelCase__   ):







     @property
     def     lowerCAmelCase_       (			self				)						->					Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



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

