
'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case:  Optional[int]								=			logging.get_logger(__name__)

__snake_case:  Dict								=			{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"canine"


     def __init__(      self							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=12							,		lowerCAmelCase_=12							,		lowerCAmelCase_=30_72							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1_63_84							,		lowerCAmelCase_=16							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=1E-1_2							,		lowerCAmelCase_=0							,		lowerCAmelCase_=0xE000							,		lowerCAmelCase_=0xE001							,		lowerCAmelCase_=4							,		lowerCAmelCase_=4							,		lowerCAmelCase_=8							,		lowerCAmelCase_=1_63_84							,		lowerCAmelCase_=1_28							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(pad_token_id=lowerCAmelCase_							,		bos_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		**lowerCAmelCase_ )

       a_      :	Optional[Any]      =							max_position_embeddings
       a_      :	Tuple      =							hidden_size
       a_      :	Optional[int]      =							num_hidden_layers
       a_      :	Union[str, Any]      =							num_attention_heads
       a_      :	Tuple      =							intermediate_size
       a_      :	List[str]      =							hidden_act
       a_      :	int      =							hidden_dropout_prob
       a_      :	Dict      =							attention_probs_dropout_prob
       a_      :	List[str]      =							initializer_range
       a_      :	Dict      =							type_vocab_size
       a_      :	List[str]      =							layer_norm_eps

       # Character config:
       a_      :	Any      =							downsampling_rate
       a_      :	Optional[Any]      =							upsampling_kernel_size
       a_      :	List[str]      =							num_hash_functions
       a_      :	Any      =							num_hash_buckets
       a_      :	str      =							local_transformer_stride




'''simple docstring'''

def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    List[str]   ):
  """simple docstring"""







  if not head or not head.next:
    return True

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

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

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    a_      :	Union[str, Any]      =							cur.next

  return True






def 	_snake_case						(      A_       :    Optional[int]   ):
  """simple docstring"""







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


