
'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    =		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecAudioForAudioFrameClassification""",
						    """Data2VecAudioForCTC""",
						    """Data2VecAudioForSequenceClassification""",
						    """Data2VecAudioForXVector""",
						    """Data2VecAudioModel""",
						    """Data2VecAudioPreTrainedModel""",
						]
						_lowercase    =		[
						    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecTextForCausalLM""",
						    """Data2VecTextForMaskedLM""",
						    """Data2VecTextForMultipleChoice""",
						    """Data2VecTextForQuestionAnswering""",
						    """Data2VecTextForSequenceClassification""",
						    """Data2VecTextForTokenClassification""",
						    """Data2VecTextModel""",
						    """Data2VecTextPreTrainedModel""",
						]
						_lowercase    =		[
						    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecVisionForImageClassification""",
						    """Data2VecVisionForMaskedImageModeling""",
						    """Data2VecVisionForSemanticSegmentation""",
						    """Data2VecVisionModel""",
						    """Data2VecVisionPreTrainedModel""",
						]

if is_tf_available():
						_lowercase    =		[
						    """TFData2VecVisionForImageClassification""",
						    """TFData2VecVisionForSemanticSegmentation""",
						    """TFData2VecVisionModel""",
						    """TFData2VecVisionPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
						from .configuration_dataavec_text import (
						    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DataaVecTextConfig,
						    DataaVecTextOnnxConfig,
						)
						from .configuration_dataavec_vision import (
						    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DataaVecVisionConfig,
						    DataaVecVisionOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dataavec_audio import (
												    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DataaVecAudioForAudioFrameClassification,
												    DataaVecAudioForCTC,
												    DataaVecAudioForSequenceClassification,
												    DataaVecAudioForXVector,
												    DataaVecAudioModel,
												    DataaVecAudioPreTrainedModel,
												)
												from .modeling_dataavec_text import (
												    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DataaVecTextForCausalLM,
												    DataaVecTextForMaskedLM,
												    DataaVecTextForMultipleChoice,
												    DataaVecTextForQuestionAnswering,
												    DataaVecTextForSequenceClassification,
												    DataaVecTextForTokenClassification,
												    DataaVecTextModel,
												    DataaVecTextPreTrainedModel,
												)
												from .modeling_dataavec_vision import (
												    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DataaVecVisionForImageClassification,
												    DataaVecVisionForMaskedImageModeling,
												    DataaVecVisionForSemanticSegmentation,
												    DataaVecVisionModel,
												    DataaVecVisionPreTrainedModel,
												)
						if is_tf_available():
												from .modeling_tf_dataavec_vision import (
												    TFDataaVecVisionForImageClassification,
												    TFDataaVecVisionForSemanticSegmentation,
												    TFDataaVecVisionModel,
												    TFDataaVecVisionPreTrainedModel,
												)

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
