

from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	T       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    data
      _lowerCAmelCase   :					Node[T] | None        =    None


 def __str__(		self :	Any       ):






      '''simple docstring'''




      return F'''{self.data}'''





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Node[T] | None        =    None

 def __iter__(		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.top
      while node:
           yield node.data
           _lowerCAmelCase   :					Union[str, Any]        =    node.next

 def __str__(		self :	int       ):






      '''simple docstring'''




      return "->".join([str(A_       ) for item in self]       )

 def __len__(		self :	str       ):






      '''simple docstring'''




      return len(tuple(iter(self       )       )       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      return self.top is None

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	T       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    Node(A_       )
      if not self.is_empty():
           _lowerCAmelCase   :					str        =    self.top
      _lowerCAmelCase   :					Optional[Any]        =    node

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      if self.is_empty():
           raise IndexError("pop from empty stack"       )
      assert isinstance(self.top     ,						A_       )
      _lowerCAmelCase   :					Optional[int]        =    self.top
      _lowerCAmelCase   :					Optional[Any]        =    self.top.next
      return pop_node.data

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      if self.is_empty():
           raise IndexError("peek from empty stack"       )

      assert self.top is not None
      return self.top.data


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    None


if __name__ == "__main__":
  from doctest import testmod

  testmod()









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






