






"""simple docstring"""



from typing import TYPE_CHECKING

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


A__	:    Any			 = {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = ['FunnelTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Tuple			 = [
						    'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FunnelBaseModel',
						    'FunnelForMaskedLM',
						    'FunnelForMultipleChoice',
						    'FunnelForPreTraining',
						    'FunnelForQuestionAnswering',
						    'FunnelForSequenceClassification',
						    'FunnelForTokenClassification',
						    'FunnelModel',
						    'FunnelPreTrainedModel',
						    'load_tf_weights_in_funnel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[Any]			 = [
						    'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFFunnelBaseModel',
						    'TFFunnelForMaskedLM',
						    'TFFunnelForMultipleChoice',
						    'TFFunnelForPreTraining',
						    'TFFunnelForQuestionAnswering',
						    'TFFunnelForSequenceClassification',
						    'TFFunnelForTokenClassification',
						    'TFFunnelModel',
						    'TFFunnelPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
						from .tokenization_funnel import FunnelTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_funnel_fast import FunnelTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_funnel import (
												    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FunnelBaseModel,
												    FunnelForMaskedLM,
												    FunnelForMultipleChoice,
												    FunnelForPreTraining,
												    FunnelForQuestionAnswering,
												    FunnelForSequenceClassification,
												    FunnelForTokenClassification,
												    FunnelModel,
												    FunnelPreTrainedModel,
												    load_tf_weights_in_funnel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_funnel import (
												    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFFunnelBaseModel,
												    TFFunnelForMaskedLM,
												    TFFunnelForMultipleChoice,
												    TFFunnelForPreTraining,
												    TFFunnelForQuestionAnswering,
												    TFFunnelForSequenceClassification,
												    TFFunnelForTokenClassification,
												    TFFunnelModel,
												    TFFunnelPreTrainedModel,
												)

else:
						import sys

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











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   list[int]  )    ->     bool:

							# 1. Validate that path exists between current and next vertices
							if graph[path[curr_ind - 1]][next_ver] == 0:
														return False

							# 2. Validate that next vertex is not already in path
							return not any(vertex == next_ver for vertex in path  )

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   list[int]    ,						lowerCamelCase__     :   int  )    ->     bool:

							# Base Case
							if curr_ind == len(lowerCamelCase__  ):
														# return whether path exists between current and starting vertices
														return graph[path[curr_ind - 1]][path[0]] == 1

							# Recursive Step
							for next_ver in range(0    ,						len(lowerCamelCase__  )  ):
														if valid_connection(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# Insert current vertex  into path as next transition
																					lowerCamelCase_	:							Tuple									=next_ver
																					# Validate created path
																					if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						curr_ind + 1  ):
																												return True
																					# Backtrack
																					lowerCamelCase_	:							int									=-1
							return False

def 			_snake_case    (					lowerCamelCase__     :   list[list[int]]    ,						lowerCamelCase__     :   int = 0  )    ->     list[int]:
							lowerCamelCase_	:							Optional[Any]									=[-1] * (len(lowerCamelCase__  ) + 1)
							# initialize start and end of path with starting index
							lowerCamelCase_	:							Optional[int]									=start_index
							# evaluate and if we find answer return path either return empty array
							return path if util_hamilton_cycle(lowerCamelCase__    ,						lowerCamelCase__    ,						1  ) else []



