






"""simple docstring"""

import heapq as hq
import math
from collections.abc import Iterator

class 						A_							:







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					Optional[int]:
										__magic_name__            =     str(id_	)
										__magic_name__            =     None
										__magic_name__            =     None
										__magic_name__            =     []
										__magic_name__            =     {}  # {vertex:distance}







				def __lt__(							self							:						str						,    __lowerCamelCase							:						Union[str, Any]	)				->					Any:
										return self.key < other.key







				def __repr__(							self							:						Dict	)				->					str:
										return self.id







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						int	)				->					Union[str, Any]:
										self.neighbors.append(UpperCAmelCase__	)







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						List[str]	)				->					str:
										__magic_name__            =     weight





def _lowerCAmelCase	(  __lowerCamelCase:Any ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						graph[a - 1].add_neighbor(graph[b - 1]   )
						graph[b - 1].add_neighbor(graph[a - 1]   )
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1] ,						__lowerCamelCase   )
						graph[b - 1].add_edge(graph[a - 1] ,						__lowerCamelCase   )

def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     []
						for u in graph:
												__magic_name__            =     math.inf
												__magic_name__            =     None
						__magic_name__            =     0
						__magic_name__            =     graph[:]
						while q:
												__magic_name__            =     min(__lowerCamelCase   )
												q.remove(__lowerCamelCase   )
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								__magic_name__            =     u
																								__magic_name__            =     u.edges[v.id]
						for i in range(1 ,						len(__lowerCamelCase   )   ):
												a.append((int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)   )
						return a

def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






						for u in graph:
												__magic_name__            =     math.inf
												__magic_name__            =     None
						__magic_name__            =     0

						__magic_name__            =     list(__lowerCamelCase   )
						hq.heapify(__lowerCamelCase   )

						while h:
												__magic_name__            =     hq.heappop(__lowerCamelCase   )
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								__magic_name__            =     u
																								__magic_name__            =     u.edges[v.id]
																								hq.heapify(__lowerCamelCase   )

						for i in range(1 ,						len(__lowerCamelCase   )   ):
												yield (int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)

def _lowerCAmelCase	(  ):

						'''simple docstring'''














if __name__ == "__main__":
			import doctest

			doctest.testmod()











"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowercase    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



