from __future__ import annotations

from typing import Any
class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Union[str, Any]			,    lowercase__					:  Any			,    lowercase__					:  Any):  # noqa: A002







        '''simple docstring'''



        lowerCAmelCase__     	=					item
        lowerCAmelCase__     	=					next






class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					None
        lowerCAmelCase__     	=					0




 def 		__snake_case						(			self					:  str			,    lowercase__					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					Node(lowercase__			,    self.head)
        self.size += 1




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        if self.head is None:
               return None
        else:
               lowerCAmelCase__     	=					self.head.item
               lowerCAmelCase__     	=					self.head.next
               self.size -= 1
               return item




 def 		__snake_case						(			self					:  List[Any]):







        '''simple docstring'''



        return self.head is None




 def __str__(			self					:  List[str]):







        '''simple docstring'''



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

               lowerCAmelCase__     	=					' --> '.join(lowercase__)

               return item_str




 def __len__(			self					:  Tuple):







        '''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__)
