





def      a__			(       lowercase__	):






   '''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__			(       lowercase__	):






   '''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__			(       lowercase__	):






   '''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(lowercase__	)
      else:
         UpperCAmelCase_           =[pos]
      UpperCAmelCase_           =head.next
      pos += 1
   UpperCAmelCase_           =pos - 1
   UpperCAmelCase_           =0
   for v in d.values():
      if len(lowercase__	) % 2 != 0:
         middle += 1
      else:
         UpperCAmelCase_           =0
         for i in range(0	,     len(lowercase__	)	):
            if v[i] + v[len(lowercase__	) - 1 - step] != checksum:
               return False
            step += 1
      if middle > 1:
         return False
   return True










'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







       def        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

             logger.info(F"{len(A  )} controlnets loaded from {pretrained_model_path}."  )

             if len(A  ) == 0:
                   raise ValueError(
                       F"No ControlNets found under {os.path.dirname(A  )}. Expected at least {pretrained_model_path + '_0'}."  )

             return cls(A  )


