
"""simple docstring"""





def       lowerCamelCase__		(			__snake_case	)							->		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       lowerCamelCase__		(			__snake_case	)							->		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       lowerCamelCase__		(			__snake_case	)							->		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__)
