code: 
  
  
  
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  __lowerCAmelCase							:			List[str]				=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      return [int(_lowerCamelCase ) for c in F"{ruleset:08}"[:8]]
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      list[list[int]]		, _lowerCamelCase      :      list[int]		, _lowerCamelCase      :      int ):
      A__       =    len(cells[0] )  # 31
      A__       =    []
      for i in range(_lowerCamelCase ):
          # Get the neighbors of each cell
          # Handle neighbours outside bounds by using 0 as their value
          A__       =    0 if i == 0 else cells[time][i - 1]
          A__       =    0 if i == population - 1 else cells[time][i + 1]
          # Define a new cell and add it to the new generation
          A__       =    7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"		, 2 )
          next_generation.append(rule[situation] )
      return next_generation
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      list[list[int]] ):
      A__       =    Image.new("RGB"		, (len(cells[0] ), len(_lowerCamelCase )) )
      A__       =    img.load()
      # Generates image
      for w in range(img.width ):
          for h in range(img.height ):
              A__       =    2_55 - int(2_55 * cells[h][w] )
              A__       =    (color, color, color)
      return img
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			int				=bin(int(input("Rule:\n").strip()))[2:]
    __lowerCAmelCase							:			List[str]				=format_ruleset(int(rule_num))
    for time in range(16):
      CELLS.append(new_generation(CELLS, rule, time))
    __lowerCAmelCase							:			int				=generate_image(CELLS)
    # Uncomment to save the image
    # img.save(f"rule_{rule_num}.png")
    img.show()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
    import torch
  
  
  __lowerCAmelCase							:			Optional[int]				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Optional[int]		, lowercase_   :Any		, lowercase_   :Any=21		, lowercase_   :Any=[]		, lowercase_   :Dict=None							)->							Tuple:
       A__       =    model
       A__       =    trace_batch_nums
       A__       =    abort_after_batch_num
  
       # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
       A__       =    collections.deque([]		, lowercase_							)
       A__       =    []
       A__       =    0
       A__       =    0
       A__       =    False
       A__       =    "                 "
  
       self.analyse_model()
  
       self.register_forward_hook()
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any=None							)->							Optional[Any]:
       if frame is not None:
           self.expand_frame(lowercase_							)
       self.frames.append("\n".join(self.frame							)							)
       A__       =    []  # start a new frame
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :Dict							)->							List[str]:
       self.frame.append(lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       print("\n".join(self.frames							)							)
       A__       =    []
   def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
       A__       =    []
   def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[Any]:
       print(F"\nDetected inf/nan during batch_number={self.batch_number}"							)
       print(F"Last {len(self.frames							)} forward frames:"							)
       print(F"{'abs min':8} {'abs max':8} metadata"							)
       print("\n".join(self.frames							)							)
       print("\n\n"							)
       A__       =    []
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
       # extract the fully qualified module names, to be able to report at run time. e.g.:
       # encoder.block.2.layer.0.SelfAttention.o
       #
       # for shared weights only the first shared module name will be registered
       A__       =    {m: name for name, m in self.model.named_modules()}
       # self.longest_module_name = max(len(v) for v in self.module_names.values())
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Any							)->							str:
       if torch.is_tensor(lowercase_							):
           self.expand_frame(get_abs_min_max(lowercase_		, lowercase_							)							)
           if detect_overflow(lowercase_		, lowercase_							):
               A__       =    True
       elif var is None:
           self.expand_frame(F"{'None':>17} {ctx}"							)
       else:
           self.expand_frame(F"{'not a tensor':>17} {ctx}"							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							str:
       self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"							)
       self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"							)
   def   UpperCAmelCase_							(       self   :int							)->							int:
       self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"							)
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :Dict		, lowercase_   :str							)->							int:
       self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"							)
  
       # params
       for name, p in module.named_parameters(recurse=lowercase_							):
           self.analyse_variable(lowercase_		, lowercase_							)
  
       # inputs
       if isinstance(lowercase_		, lowercase_							):
           for i, x in enumerate(lowercase_							):
               self.analyse_variable(lowercase_		, F"input[{i}]"							)
       else:
           self.analyse_variable(lowercase_		, "input"							)
  
       # outputs
       if isinstance(lowercase_		, lowercase_							):
           for i, x in enumerate(lowercase_							):
               # possibly a tuple of tuples
               if isinstance(lowercase_		, lowercase_							):
                   for j, y in enumerate(lowercase_							):
                       self.analyse_variable(lowercase_		, F"output[{i}][{j}]"							)
               else:
                   self.analyse_variable(lowercase_		, F"output[{i}]"							)
       else:
           self.analyse_variable(lowercase_		, "output"							)
  
       self.save_frame()
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       self.model.apply(self._register_forward_hook							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]							)->							Tuple:
       module.register_forward_hook(self.forward_hook							)
  
  
  
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :int		, lowercase_   :Optional[Any]		, lowercase_   :Dict							)->							Union[str, Any]:
       # - input is a tuple of packed inputs (could be non-Tensors)
       # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
       A__       =    False
  
       A__       =    True if self.batch_number in self.trace_batch_nums else False
       if trace_mode:
           self.reset_saved_frames()
  
       if self.total_calls == 0:
           self.batch_start_frame()
       self.total_calls += 1
  
       # count batch numbers - the very first forward hook of the batch will be called when the
       # batch completes - i.e. it gets called very last - we know this batch has finished
       if module == self.model:
           self.batch_number += 1
           A__       =    True
  
       self.create_frame(lowercase_		, lowercase_		, lowercase_							)
  
       # if last_frame_of_batch:
       #     self.batch_end_frame()
  
       if trace_mode:
           self.trace_frames()
  
       if last_frame_of_batch:
           self.batch_start_frame()
  
       if self.detected_overflow and not trace_mode:
           self.dump_saved_frames()
  
           # now we can abort, as it's pointless to continue running
           raise ValueError(
               "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
               "Please scroll up above this traceback to see the activation values prior to this event."							)
  
       # abort after certain batch if requested to do so
       if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
           raise ValueError(
               F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
               F" `abort_after_batch_num={self.abort_after_batch_num}` arg"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      int ):
      A__       =    var.abs()
      return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Optional[int] ):
      A__       =    False
      if torch.isnan(_lowerCamelCase ).any().item():
          A__       =    True
          print(F"{ctx} has nans" )
      if torch.isinf(_lowerCamelCase ).any().item():
          A__       =    True
          print(F"{ctx} has infs" )
  
      # if needed to monitor large elements can enable the following
      if 0:  # and detected:
          A__       =    var[torch.ge(var.abs()		, 1_00 )]
          if naaa.numel() > 0:
              print(F"{ctx}:  n100={naaa.numel()}" )
          A__       =    var[torch.ge(var.abs()		, 10_00 )]
          if naaaa.numel() > 0:
              print(F"{ctx}: n1000={naaaa.numel()}" )
          A__       =    var[torch.ge(var.abs()		, 1_00_00 )]
          if naaaaa.numel() > 0:
              print(F"{ctx}: n10000={naaaaa.numel()}" )
  
      if 0:
          print(F"min={var.min():9.2e} max={var.max():9.2e}" )
  
      if 0:
          print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})" )
  
      return detected
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """underflow_overflow"""
   __lowercase     	=       """tpu_metrics_debug"""
  
  
  
  