




from typing import TYPE_CHECKING

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


__A						:		int          =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Dict          =						[
	    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RegNetForImageClassification""",
	    """RegNetModel""",
	    """RegNetPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Optional[Any]          =						[
	    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRegNetForImageClassification""",
	    """TFRegNetModel""",
	    """TFRegNetPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Union[str, Any]          =						[
	    """FlaxRegNetForImageClassification""",
	    """FlaxRegNetModel""",
	    """FlaxRegNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_regnet import (
		    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RegNetForImageClassification,
		    RegNetModel,
		    RegNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_regnet import (
		    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRegNetForImageClassification,
		    TFRegNetModel,
		    TFRegNetPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_regnet import (
		    FlaxRegNetForImageClassification,
		    FlaxRegNetModel,
		    FlaxRegNetPreTrainedModel,
		)


else:
	import sys

	__A						:		Dict          =						_LazyModule(__name__, globals()["__file__"], _import_structure)







'''simple docstring'''







class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase=None   ):
								_SCREAMING_SNAKE_CASE					=			data
								_SCREAMING_SNAKE_CASE					=			None




		def __repr__(					self   ):
								_SCREAMING_SNAKE_CASE					=			""
								_SCREAMING_SNAKE_CASE					=			self
								while temp:
														string_rep += F'<{temp.data}> ---> '
														_SCREAMING_SNAKE_CASE					=			temp.next
								string_rep += "<END>"
								return string_rep



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):

						# if elements_list is empty
						if not elements_list:
												raise Exception("The Elements List is empty"	)

						# Set first element as Head
						_SCREAMING_SNAKE_CASE					=			Node(elements_list[0]	)
						_SCREAMING_SNAKE_CASE					=			head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_SCREAMING_SNAKE_CASE					=			Node(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			current.next
						return head


_snake_case					: List[Any]     			=					[1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
_snake_case					: List[Any]     			=					make_linked_list(list_data)
print("""Linked List:""")




print(linked_list)