
# Function to print upper half of diamond (pyramid)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any  )   ->			Union[str, Any]:






						'''simple docstring'''

						for i in range(0 ,			_UpperCAmelCase  ):
												for _ in range(0 ,			n - i - 1  ):  # printing spaces
																		print(" " ,			end=""  )
												for _ in range(0 ,			i + 1  ):  # printing stars
																		print("* " ,			end=""  )
												print()



def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			Dict:






						'''simple docstring'''

						for i in range(_UpperCAmelCase ,			0 ,			-1  ):
												for _ in range(_UpperCAmelCase ,			0 ,			-1  ):  # printing stars
																		print("* " ,			end=""  )
												print()
												for _ in range(n - i + 1 ,			0 ,			-1  ):  # printing spaces
																		print(" " ,			end=""  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any  )   ->			str:






						'''simple docstring'''

						if n <= 0:
												print("       ...       ....        nothing printing :("  )
												return
						floyd(_UpperCAmelCase  )  # upper half
						reverse_floyd(_UpperCAmelCase  )  # lower half


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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




