
from typing import TYPE_CHECKING

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


_snake_case      :							str     	=    {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Optional[int]     	=    ["XLNetTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Any     	=    ["XLNetTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLNetForMultipleChoice",
						    "XLNetForQuestionAnswering",
						    "XLNetForQuestionAnsweringSimple",
						    "XLNetForSequenceClassification",
						    "XLNetForTokenClassification",
						    "XLNetLMHeadModel",
						    "XLNetModel",
						    "XLNetPreTrainedModel",
						    "load_tf_weights_in_xlnet",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Optional[Any]     	=    [
						    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFXLNetForMultipleChoice",
						    "TFXLNetForQuestionAnsweringSimple",
						    "TFXLNetForSequenceClassification",
						    "TFXLNetForTokenClassification",
						    "TFXLNetLMHeadModel",
						    "TFXLNetMainLayer",
						    "TFXLNetModel",
						    "TFXLNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet import XLNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlnet_fast import XLNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlnet import (
												    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLNetForMultipleChoice,
												    XLNetForQuestionAnswering,
												    XLNetForQuestionAnsweringSimple,
												    XLNetForSequenceClassification,
												    XLNetForTokenClassification,
												    XLNetLMHeadModel,
												    XLNetModel,
												    XLNetPreTrainedModel,
												    load_tf_weights_in_xlnet,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlnet import (
												    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLNetForMultipleChoice,
												    TFXLNetForQuestionAnsweringSimple,
												    TFXLNetForSequenceClassification,
												    TFXLNetForTokenClassification,
												    TFXLNetLMHeadModel,
												    TFXLNetMainLayer,
												    TFXLNetModel,
												    TFXLNetPreTrainedModel,
												)

else:
						import sys

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


# Function to print upper half of diamond (pyramid)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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


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

						print("Good Bye...")
