"""simple docstring"""
from queue import PriorityQueue
from typing import Any

import numpy as np





def 				lowerCAmelCase__      (     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			set	,     _UpperCamelCase    :			set	,     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			PriorityQueue	,     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			float | int	,     )   ->			float | int:





						"""simple docstring"""






						for nxt, d in graph[v]:
												if nxt in visited_forward:
																		continue
												snake_case        =     cst_fwd.get(_lowerCAmelCase	,     np.inf   )
												snake_case        =     cst_fwd[v] + d
												if new_cost_f < old_cost_f:
																		queue.put((new_cost_f, nxt)   )
																		snake_case        =     new_cost_f
																		snake_case        =     v
												if nxt in visited_backward:
																		if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																								snake_case        =     cst_fwd[v] + d + cst_bwd[nxt]
						return shortest_distance





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			dict   )   ->			int:





						"""simple docstring"""






						snake_case        =     -1

						snake_case        =     set()
						snake_case        =     set()
						snake_case        =     {source: 0}
						snake_case        =     {destination: 0}
						snake_case        =     {source: None}
						snake_case        =     {destination: None}
						snake_case        =     PriorityQueue()
						snake_case        =     PriorityQueue()

						snake_case        =     np.inf

						queue_forward.put((0, source)   )
						queue_backward.put((0, destination)   )

						if source == destination:
												return 0

						while not queue_forward.empty() and not queue_backward.empty():
												snake_case        =     queue_forward.get()
												visited_forward.add(_lowerCAmelCase   )

												snake_case        =     queue_backward.get()
												visited_backward.add(_lowerCAmelCase   )

												snake_case        =     pass_and_relaxation(
												    _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     )

												snake_case        =     pass_and_relaxation(
												    _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase	,     )

												if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																		break

						if shortest_distance != np.inf:
												snake_case        =     shortest_distance
						return shortest_path_distance


SCREAMING_SNAKE_CASE__   =					{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
SCREAMING_SNAKE_CASE__   =					{
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


