
'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case:  List[Any]								=			{
    "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:
    __snake_case:  List[Any]								=			[
        "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecAudioForAudioFrameClassification",
        "Data2VecAudioForCTC",
        "Data2VecAudioForSequenceClassification",
        "Data2VecAudioForXVector",
        "Data2VecAudioModel",
        "Data2VecAudioPreTrainedModel",
    ]
    __snake_case:  List[str]								=			[
        "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecTextForCausalLM",
        "Data2VecTextForMaskedLM",
        "Data2VecTextForMultipleChoice",
        "Data2VecTextForQuestionAnswering",
        "Data2VecTextForSequenceClassification",
        "Data2VecTextForTokenClassification",
        "Data2VecTextModel",
        "Data2VecTextPreTrainedModel",
    ]
    __snake_case:  Union[str, Any]								=			[
        "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecVisionForImageClassification",
        "Data2VecVisionForMaskedImageModeling",
        "Data2VecVisionForSemanticSegmentation",
        "Data2VecVisionModel",
        "Data2VecVisionPreTrainedModel",
    ]

if is_tf_available():
    __snake_case:  int								=			[
        "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

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




'''simple docstring'''

def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head:
    return True
  # split the list to two parts
  a_      , a_      :	Union[str, Any]      =							head.next, head
  while fast and fast.next:
    a_      :	Dict      =							fast.next.next
    a_      :	List[str]      =							slow.next
  a_      :	str      =							slow.next
  a_      :	Optional[int]      =							None  # Don't forget here! But forget still works!
  # reverse the second part
  a_      :	Dict      =							None
  while second:
    a_      :	Any      =							second.next
    a_      :	Any      =							node
    a_      :	Optional[Any]      =							second
    a_      :	Optional[Any]      =							nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    a_      :	List[str]      =							node.next
    a_      :	Dict      =							head.next
  return True






def 	_snake_case						(      A_       :    List[str]   ):
  """simple docstring"""







  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  a_      :	Optional[Any]      =							head
  while fast and fast.next:
    a_      , a_      :	Optional[int]      =							fast.next.next, slow.next

  # 2. Push the second half into the stack
  a_      :	List[str]      =							[slow.val]
  while slow.next:
    a_      :	List[str]      =							slow.next
    stack.append(slow.val   )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    a_      :	Union[str, Any]      =							cur.next

  return True






def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







  if not head or not head.next:
    return True
  a_      :	List[Any]      =							{}
  a_      :	int      =							0
  while head:
    if head.val in d:
      d[head.val].append(A_   )
    else:
      a_      :	int      =							[pos]
    a_      :	List[str]      =							head.next
    pos += 1
  a_      :	str      =							pos - 1
  a_      :	List[str]      =							0
  for v in d.values():
    if len(A_   ) % 2 != 0:
      middle += 1
    else:
      a_      :	Optional[int]      =							0
      for i in range(0     ,		len(A_   )   ):
        if v[i] + v[len(A_   ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True


