
'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    =		{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		["""RemBertTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		["""RemBertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """RemBertForCausalLM""",
						    """RemBertForMaskedLM""",
						    """RemBertForMultipleChoice""",
						    """RemBertForQuestionAnswering""",
						    """RemBertForSequenceClassification""",
						    """RemBertForTokenClassification""",
						    """RemBertLayer""",
						    """RemBertModel""",
						    """RemBertPreTrainedModel""",
						    """load_tf_weights_in_rembert""",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFRemBertForCausalLM""",
						    """TFRemBertForMaskedLM""",
						    """TFRemBertForMultipleChoice""",
						    """TFRemBertForQuestionAnswering""",
						    """TFRemBertForSequenceClassification""",
						    """TFRemBertForTokenClassification""",
						    """TFRemBertLayer""",
						    """TFRemBertModel""",
						    """TFRemBertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

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


'''simple docstring'''



from __future__ import annotations

from typing import Any




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						pass






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			data
												_lowerCAmelCase  						=			None




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self
												_lowerCAmelCase  						=			[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(_lowercase				)
																		yield node.data
																		_lowerCAmelCase  						=			node.next_node




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												try:
																		list(self				)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
						_lowercase    =		Node(1)
						_lowercase    =		Node(2)
						_lowercase    =		Node(3)
						_lowercase    =		Node(4)
						print(root_node.has_loop)  # False
						_lowercase    =		root_node.next_node
						print(root_node.has_loop)  # True

						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						print(root_node.has_loop)  # False

						_lowercase    =		Node(1)
						print(root_node.has_loop)  # False
