



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case				:  List[Any] =       {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Any =       ['DistilBertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[int] =       [
	    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DistilBertForMaskedLM',
	    'DistilBertForMultipleChoice',
	    'DistilBertForQuestionAnswering',
	    'DistilBertForSequenceClassification',
	    'DistilBertForTokenClassification',
	    'DistilBertModel',
	    'DistilBertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Any =       [
	    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDistilBertForMaskedLM',
	    'TFDistilBertForMultipleChoice',
	    'TFDistilBertForQuestionAnswering',
	    'TFDistilBertForSequenceClassification',
	    'TFDistilBertForTokenClassification',
	    'TFDistilBertMainLayer',
	    'TFDistilBertModel',
	    'TFDistilBertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  int =       [
	    'FlaxDistilBertForMaskedLM',
	    'FlaxDistilBertForMultipleChoice',
	    'FlaxDistilBertForQuestionAnswering',
	    'FlaxDistilBertForSequenceClassification',
	    'FlaxDistilBertForTokenClassification',
	    'FlaxDistilBertModel',
	    'FlaxDistilBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  List[Any] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






# Function to print upper half of diamond (pyramid)


def      _lowercase							(    __snake_case      )		->  Dict:
			for i in range(0   ,__snake_case      ):
						for _ in range(0   ,n - i - 1      ):  # printing spaces
									print(" "   ,end=""      )
						for _ in range(0   ,i + 1      ):  # printing stars
									print("* "   ,end=""      )
						print()


def      _lowercase							(    __snake_case      )		->  Tuple:
			for i in range(__snake_case   ,0   ,-1      ):
						for _ in range(__snake_case   ,0   ,-1      ):  # printing stars
									print("* "   ,end=""      )
						print()
						for _ in range(n - i + 1   ,0   ,-1      ):  # printing spaces
									print(" "   ,end=""      )


def      _lowercase							(    __snake_case      )		->  Any:
			if n <= 0:
						print("       ...       ....        nothing printing :("      )
						return
			floyd(__snake_case      )  # upper half
			reverse_floyd(__snake_case      )  # lower half


if __name__ == "__main__":
	print(R'| /\ | |- |  |-  |--| |\  /| |-')
	print(R'|/  \| |- |_ |_  |__| | \/ | |_')
	__snake_case				:  Tuple =       1
	while K:
		__snake_case				:  List[str] =       int(input('enter the number and , and see the magic : '))
		print()
		pretty_print(user_number)
		__snake_case				:  Optional[Any] =       int(input('press 0 to exit... and 1 to continue...'))

	print('Good Bye...')