


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase:						List[str]   				=       {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						int   				=       ['RobertaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Any   				=       [
	    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RobertaForCausalLM',
	    'RobertaForMaskedLM',
	    'RobertaForMultipleChoice',
	    'RobertaForQuestionAnswering',
	    'RobertaForSequenceClassification',
	    'RobertaForTokenClassification',
	    'RobertaModel',
	    'RobertaPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Optional[int]   				=       [
	    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFRobertaForCausalLM',
	    'TFRobertaForMaskedLM',
	    'TFRobertaForMultipleChoice',
	    'TFRobertaForQuestionAnswering',
	    'TFRobertaForSequenceClassification',
	    'TFRobertaForTokenClassification',
	    'TFRobertaMainLayer',
	    'TFRobertaModel',
	    'TFRobertaPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Union[str, Any]   				=       [
	    'FlaxRobertaForCausalLM',
	    'FlaxRobertaForMaskedLM',
	    'FlaxRobertaForMultipleChoice',
	    'FlaxRobertaForQuestionAnswering',
	    'FlaxRobertaForSequenceClassification',
	    'FlaxRobertaForTokenClassification',
	    'FlaxRobertaModel',
	    'FlaxRobertaPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						Dict   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from __future__ import annotations

from typing import Any




class       lowercase_					:





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





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





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

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





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-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)

											a__      =self.m_num_of_nodes

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

																									a__      =self.m_component[u]
																									a__      =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
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

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

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

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







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

