


from __future__ import annotations

from typing import Any




class A						:






      def __init__(      self		:  str    ,     lowercase_		:  Any    ,     lowercase_		:  Any				)      ->		None:  # noqa: A002
             """simple docstring"""




             _lowerCamelCase :	str						=item
             _lowerCamelCase :	Tuple						=next




class A						:






      def __init__(      self		:  Tuple				)      ->		None:
             """simple docstring"""




             _lowerCamelCase :	Node | None						=None
             _lowerCamelCase :	Union[str, Any]						=0






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  Any				)      ->		None:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=Node(lowercase_    ,     self.head				)
             self.size += 1






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Any:
             """simple docstring"""




             if self.head is None:
                    return None
             else:
                    _lowerCamelCase :	Union[str, Any]						=self.head.item
                    _lowerCamelCase :	List[str]						=self.head.next
                    self.size -= 1
                    return item






      def  lowerCamelCase (      self		:  Tuple				)      ->		bool:
             """simple docstring"""




             return self.head is None






      def __str__(      self		:  Any				)      ->		str:
             """simple docstring"""




             if self.is_empty():
                    return ""
             else:
                    _lowerCamelCase :	Optional[int]						=self.head
                    _lowerCamelCase :	str						=''
                    _lowerCamelCase :	list[str]						=[]
                    while iterate:
                           item_list.append(str(iterate.item				)				)
                           _lowerCamelCase :	int						=iterate.next

                    _lowerCamelCase :	int						=' --> '.join(lowercase_				)

                    return item_str






      def __len__(      self		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             return self.size






from typing import TYPE_CHECKING

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


lowerCamelCase		      = {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = ['DebertaV2TokenizerFast']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaV2ForMaskedLM',
     'TFDebertaV2ForQuestionAnswering',
     'TFDebertaV2ForSequenceClassification',
     'TFDebertaV2ForTokenClassification',
     'TFDebertaV2Model',
     'TFDebertaV2PreTrainedModel',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaV2ForMaskedLM',
     'DebertaV2ForMultipleChoice',
     'DebertaV2ForQuestionAnswering',
     'DebertaV2ForSequenceClassification',
     'DebertaV2ForTokenClassification',
     'DebertaV2Model',
     'DebertaV2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

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


