
from typing import TYPE_CHECKING

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


lowercase  :					int						    =							{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Optional[int]						    =							["""MobileBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Optional[Any]						    =							[
      """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MobileBertForMaskedLM""",
      """MobileBertForMultipleChoice""",
      """MobileBertForNextSentencePrediction""",
      """MobileBertForPreTraining""",
      """MobileBertForQuestionAnswering""",
      """MobileBertForSequenceClassification""",
      """MobileBertForTokenClassification""",
      """MobileBertLayer""",
      """MobileBertModel""",
      """MobileBertPreTrainedModel""",
      """load_tf_weights_in_mobilebert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					int						    =							[
      """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFMobileBertForMaskedLM""",
      """TFMobileBertForMultipleChoice""",
      """TFMobileBertForNextSentencePrediction""",
      """TFMobileBertForPreTraining""",
      """TFMobileBertForQuestionAnswering""",
      """TFMobileBertForSequenceClassification""",
      """TFMobileBertForTokenClassification""",
      """TFMobileBertMainLayer""",
      """TFMobileBertModel""",
      """TFMobileBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  lowercase  :					Union[str, Any]						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    n
							__lowerCamelCase		       =    [None] * self.n
							__lowerCamelCase		       =    0  # index of the first element
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0


			def __len__(      self							)    ->      int:



							'''simple docstring'''
							return self.size


			def       lowercase_    (      self							)    ->      bool:



							'''simple docstring'''
							return self.size == 0


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return False if self.is_empty() else self.array[self.front]


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							if self.size >= self.n:
											raise Exception('QUEUE IS FULL'							)

							__lowerCamelCase		       =    data
							__lowerCamelCase		       =    (self.rear + 1) % self.n
							self.size += 1
							return self






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							if self.size == 0:
											raise Exception('UNDERFLOW'							)

							__lowerCamelCase		       =    self.array[self.front]
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    (self.front + 1) % self.n
							self.size -= 1
							return temp


