"""simple docstring"""
class   __snake_case			:



      def __init__(		self)      ->     Union[str, Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      []
        a__:	Dict     				=      0
        a__:	List[str]     				=      0



      def __str__(		self)      ->     str:





        '''simple docstring'''





        a__:	int     				=      '<' + str(self.entries)[1:-1] + '>'
        return printed



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        self.entries.append(lowercase)
        a__:	str     				=      self.length + 1



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.length - 1
        a__:	List[str]     				=      self.entries[self.front]
        # self.front-=1
        # self.entries = self.entries[self.front:]
        a__:	str     				=      self.entries[1:]
        return dequeued



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        for _ in range(lowercase):
          self.put(self.get())



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        return self.entries[0]



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        return self.length



"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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


