


def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      if not head:
            return True
      # split the list to two parts
      a				,							a		:List[Any]									=			head.next, head
      while fast and fast.next:
            a		:Optional[Any]									=			fast.next.next
            a		:Union[str, Any]									=			slow.next
      a		:Union[str, Any]									=			slow.next
      a		:int									=			None  # Don't forget here! But forget still works!
      # reverse the second part
      a		:Any									=			None
      while second:
            a		:str									=			second.next
            a		:str									=			node
            a		:Tuple									=			second
            a		:Optional[int]									=			nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            a		:Optional[Any]									=			node.next
            a		:int									=			head.next
      return True
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      a		:Dict									=			head
      while fast and fast.next:
            a				,							a		:List[Any]									=			fast.next.next, slow.next

      # 2. Push the second half into the stack
      a		:Union[str, Any]									=			[slow.val]
      while slow.next:
            a		:Union[str, Any]									=			slow.next
            stack.append(slow.val		)

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

      return True






def 							__lowerCamelCase		(					UpperCAmelCase_		:						int		):




      """simple docstring"""





      if not head or not head.next:
            return True
      a		:Dict									=			{}
      a		:Optional[int]									=			0
      while head:
            if head.val in d:
                  d[head.val].append(UpperCAmelCase_		)
            else:
                  a		:int									=			[pos]
            a		:Tuple									=			head.next
            pos += 1
      a		:List[str]									=			pos - 1
      a		:Any									=			0
      for v in d.values():
            if len(UpperCAmelCase_		) % 2 != 0:
                  middle += 1
            else:
                  a		:Tuple									=			0
                  for i in range(0					,    len(UpperCAmelCase_		)		):
                        if v[i] + v[len(UpperCAmelCase_		) - 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_torch_available


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTNeoForCausalLM''',
         '''GPTNeoForQuestionAnswering''',
         '''GPTNeoForSequenceClassification''',
         '''GPTNeoForTokenClassification''',
         '''GPTNeoModel''',
         '''GPTNeoPreTrainedModel''',
         '''load_tf_weights_in_gpt_neo''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Any										=	[
         '''FlaxGPTNeoForCausalLM''',
         '''FlaxGPTNeoModel''',
         '''FlaxGPTNeoPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

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





