





from typing import TYPE_CHECKING

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


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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =    [
						    """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:
						lowerCAmelCase_						   =    [
						    """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

						lowerCAmelCase_						   =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




