






def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						if not head:
												return True
						# split the list to two parts
						_UpperCAmelCase					, _UpperCAmelCase									=			head.next, head
						while fast and fast.next:
												_UpperCAmelCase									=			fast.next.next
												_UpperCAmelCase									=			slow.next
						_UpperCAmelCase									=			slow.next
						_UpperCAmelCase									=			None  # Don't forget here! But forget still works!
						# reverse the second part
						_UpperCAmelCase									=			None
						while second:
												_UpperCAmelCase									=			second.next
												_UpperCAmelCase									=			node
												_UpperCAmelCase									=			second
												_UpperCAmelCase									=			nxt
						# compare two parts
						# second part has the same or one less node
						while node:
												if node.val != head.val:
																		return False
												_UpperCAmelCase									=			node.next
												_UpperCAmelCase									=			head.next
						return True


def __A    (      __lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						if not head or not head.next:
												return True

						# 1. Get the midpoint (slow)
						_UpperCAmelCase									=			_UpperCAmelCase									=			_UpperCAmelCase									=			head
						while fast and fast.next:
												_UpperCAmelCase					, _UpperCAmelCase									=			fast.next.next, slow.next

						# 2. Push the second half into the stack
						_UpperCAmelCase									=			[slow.val]
						while slow.next:
												_UpperCAmelCase									=			slow.next
												stack.append(slow.val	)

						# 3. Comparison
						while stack:
												if stack.pop() != cur.val:
																		return False
												_UpperCAmelCase									=			cur.next

						return True


def __A    (      __lowerCAmelCase	)->  int:





						"""simple docstring"""



						if not head or not head.next:
												return True
						_UpperCAmelCase									=			{}
						_UpperCAmelCase									=			0
						while head:
												if head.val in d:
																		d[head.val].append(__lowerCAmelCase	)
												else:
																		_UpperCAmelCase									=			[pos]
												_UpperCAmelCase									=			head.next
												pos += 1
						_UpperCAmelCase									=			pos - 1
						_UpperCAmelCase									=			0
						for v in d.values():
												if len(__lowerCAmelCase	) % 2 != 0:
																		middle += 1
												else:
																		_UpperCAmelCase									=			0
																		for i in range(0 ,		len(__lowerCAmelCase	)	):
																								if v[i] + v[len(__lowerCAmelCase	) - 1 - step] != checksum:
																														return False
																								step += 1
												if middle > 1:
																		return False
						return True








from typing import TYPE_CHECKING

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


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
