
from __future__ import annotations

from typing import Any





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			num_of_nodes
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			{}




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							self.m_edges.append([u_node, v_node, weight]  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:

							if self.m_component[u_node] == u_node:
										return u_node
							return self.find_component(self.m_component[u_node]  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:

							if self.m_component[u_node] != u_node:
										for k in self.m_component:
													lowerCAmelCase_      			=			self.find_component(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:

							if component_size[u_node] <= component_size[v_node]:
										lowerCAmelCase_      			=			v_node
										component_size[v_node] += component_size[u_node]
										self.set_component(_UpperCamelCase  )

							elif component_size[u_node] >= component_size[v_node]:
										lowerCAmelCase_      			=			self.find_component(_UpperCamelCase  )
										component_size[u_node] += component_size[v_node]
										self.set_component(_UpperCamelCase  )




				def   __a		(      self  )       ->   None:
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			0

							lowerCAmelCase_      			=			[-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes  ):
										self.m_component.update({node: node}  )
										component_size.append(1  )

							lowerCAmelCase_      			=			self.m_num_of_nodes

							while num_of_components > 1:
										for edge in self.m_edges:
													lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			edge

													lowerCAmelCase_      			=			self.m_component[u]
													lowerCAmelCase_      			=			self.m_component[v]

													if u_component != v_component:

																for component in (u_component, v_component):
																			if (
																			    minimum_weight_edge[component] == -1
																			    or minimum_weight_edge[component][2] > w
																			):
																						lowerCAmelCase_      			=			[u, v, w]

										for edge in minimum_weight_edge:
													if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
																lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			edge

																lowerCAmelCase_      			=			self.m_component[u]
																lowerCAmelCase_      			=			self.m_component[v]

																if u_component != v_component:
																			mst_weight += w
																			self.union(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )
																			print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""  )
																			num_of_components -= 1

										lowerCAmelCase_      			=			[-1] * self.m_num_of_nodes
							print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""  )






def      lowerCamelCase__       (       )     ->   Optional[int]:




			"""simple docstring"""






if __name__ == "__main__":
			import doctest

			doctest.testmod()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A     =  logging.get_logger(__name__)

_A     =  {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''transfo-xl'''
				_lowercase    	=['''mems''']
				_lowercase    	={
				    '''n_token''': '''vocab_size''',
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(      self       ,       _UpperCamelCase=267_735       ,       _UpperCamelCase=[20_000, 40_000, 200_000]       ,       _UpperCamelCase=1_024       ,       _UpperCamelCase=1_024       ,       _UpperCamelCase=16       ,       _UpperCamelCase=64       ,       _UpperCamelCase=4_096       ,       _UpperCamelCase=4       ,       _UpperCamelCase=False       ,       _UpperCamelCase=18       ,       _UpperCamelCase=1_600       ,       _UpperCamelCase=1_000       ,       _UpperCamelCase=True       ,       _UpperCamelCase=True       ,       _UpperCamelCase=0       ,       _UpperCamelCase=-1       ,       _UpperCamelCase=True       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=True       ,       _UpperCamelCase="normal"       ,       _UpperCamelCase=0.01       ,       _UpperCamelCase=0.01       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-5       ,       _UpperCamelCase=0       ,       **_UpperCamelCase       ,       )       ->   Dict:
							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			[]
							self.cutoffs.extend(_UpperCamelCase  )
							if proj_share_all_but_first:
										lowerCAmelCase_      			=			[False] + [True] * len(self.cutoffs  )
							else:
										lowerCAmelCase_      			=			[False] + [False] * len(self.cutoffs  )
							lowerCAmelCase_      			=			d_model
							lowerCAmelCase_      			=			d_embed
							lowerCAmelCase_      			=			d_head
							lowerCAmelCase_      			=			d_inner
							lowerCAmelCase_      			=			div_val
							lowerCAmelCase_      			=			pre_lnorm
							lowerCAmelCase_      			=			n_layer
							lowerCAmelCase_      			=			n_head
							lowerCAmelCase_      			=			mem_len
							lowerCAmelCase_      			=			same_length
							lowerCAmelCase_      			=			attn_type
							lowerCAmelCase_      			=			clamp_len
							lowerCAmelCase_      			=			sample_softmax
							lowerCAmelCase_      			=			adaptive
							lowerCAmelCase_      			=			dropout
							lowerCAmelCase_      			=			dropatt
							lowerCAmelCase_      			=			untie_r
							lowerCAmelCase_      			=			init
							lowerCAmelCase_      			=			init_range
							lowerCAmelCase_      			=			proj_init_std
							lowerCAmelCase_      			=			init_std
							lowerCAmelCase_      			=			layer_norm_epsilon
							super().__init__(eos_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )




				@property
				def   __a		(      self  )       ->   List[Any]:
							# Message copied from Transformer-XL documentation
							logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )
							return -1




				@max_position_embeddings.setter
				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



