'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase_     :   List[Any] 				=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Any 				=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   List[Any] 				=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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


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







def 			A__      (	snake_case_ :		Optional[Any]      ):
 if not head or not head.next:
  return True

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

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

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

 return True







def 			A__      (	snake_case_ :		Any      ):
 if not head or not head.next:
  return True
 SCREAMING_SNAKE_CASE__:   Optional[int]=      {}
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      0
 while head:
  if head.val in d:
   d[head.val].append(snake_case_      )
  else:
   SCREAMING_SNAKE_CASE__:   Optional[int]=      [pos]
  SCREAMING_SNAKE_CASE__:   Dict=      head.next
  pos += 1
 SCREAMING_SNAKE_CASE__:   Dict=      pos - 1
 SCREAMING_SNAKE_CASE__:   str=      0
 for v in d.values():
  if len(snake_case_      ) % 2 != 0:
   middle += 1
  else:
   SCREAMING_SNAKE_CASE__:   List[Any]=      0
   for i in range(0       ,				len(snake_case_      )      ):
    if v[i] + v[len(snake_case_      ) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

