
"""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,
)


UpperCAmelCase__      :    List[str]    							= {
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    Dict    							= ['AlbertTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    List[Any]    							= ['AlbertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    Any    							= [
					    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AlbertForMaskedLM',
					    'AlbertForMultipleChoice',
					    'AlbertForPreTraining',
					    'AlbertForQuestionAnswering',
					    'AlbertForSequenceClassification',
					    'AlbertForTokenClassification',
					    'AlbertModel',
					    'AlbertPreTrainedModel',
					    'load_tf_weights_in_albert',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    int    							= [
					    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAlbertForMaskedLM',
					    'TFAlbertForMultipleChoice',
					    'TFAlbertForPreTraining',
					    'TFAlbertForQuestionAnswering',
					    'TFAlbertForSequenceClassification',
					    'TFAlbertForTokenClassification',
					    'TFAlbertMainLayer',
					    'TFAlbertModel',
					    'TFAlbertPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    Dict    							= [
					    'FlaxAlbertForMaskedLM',
					    'FlaxAlbertForMultipleChoice',
					    'FlaxAlbertForPreTraining',
					    'FlaxAlbertForQuestionAnswering',
					    'FlaxAlbertForSequenceClassification',
					    'FlaxAlbertForTokenClassification',
					    'FlaxAlbertModel',
					    'FlaxAlbertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

					UpperCAmelCase__      :    List[str]    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

