code: import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
   import torch
  
  
  a     :			Optional[int]					    =						logging.get_logger(__name__)
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=21					,	lowerCamelCase__=[]					,	lowerCamelCase__=None   ):
         lowerCAmelCase_:       Optional[Any]         =  model
         lowerCAmelCase_:       Union[str, Any]         =  trace_batch_nums
         lowerCAmelCase_:       Any         =  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
         lowerCAmelCase_:       str         =  collections.deque([]					,	lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  []
         lowerCAmelCase_:       Optional[int]         =  0
         lowerCAmelCase_:       Any         =  0
         lowerCAmelCase_:       List[str]         =  False
         lowerCAmelCase_:       Any         =  "                 "
  
         self.analyse_model()
  
         self.register_forward_hook()
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__=None   ):
         if frame is not None:
            self.expand_frame(lowerCamelCase__   )
         self.frames.append("\n".join(self.frame   )   )
         lowerCAmelCase_:       List[str]         =  []  # start a new frame
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         self.frame.append(lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         print("\n".join(self.frames   )   )
         lowerCAmelCase_:       str         =  []
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  []
  
  
  
  
      def     _a						(						self   ):
         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"   )
         lowerCAmelCase_:       List[str]         =  []
  
  
  
  
      def     _a						(						self   ):
         # 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
         lowerCAmelCase_:       Union[str, Any]         =  {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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         if torch.is_tensor(lowerCamelCase__   ):
            self.expand_frame(get_abs_min_max(lowerCamelCase__					,	lowerCamelCase__   )   )
            if detect_overflow(lowerCamelCase__					,	lowerCamelCase__   ):
               lowerCAmelCase_:       Dict         =  True
         elif var is None:
            self.expand_frame(F'''{"None":>17} {ctx}'''   )
         else:
            self.expand_frame(F'''{"not a tensor":>17} {ctx}'''   )
  
  
  
  
      def     _a						(						self   ):
         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     _a						(						self   ):
         self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''   )
  
         # params
         for name, p in module.named_parameters(recurse=lowerCamelCase__   ):
            self.analyse_variable(lowerCamelCase__					,	lowerCamelCase__   )
  
         # inputs
         if isinstance(lowerCamelCase__					,	lowerCamelCase__   ):
            for i, x in enumerate(lowerCamelCase__   ):
               self.analyse_variable(lowerCamelCase__					,	F'''input[{i}]'''   )
         else:
            self.analyse_variable(lowerCamelCase__					,	"input"   )
  
         # outputs
         if isinstance(lowerCamelCase__					,	lowerCamelCase__   ):
            for i, x in enumerate(lowerCamelCase__   ):
               # possibly a tuple of tuples
               if isinstance(lowerCamelCase__					,	lowerCamelCase__   ):
                  for j, y in enumerate(lowerCamelCase__   ):
                     self.analyse_variable(lowerCamelCase__					,	F'''output[{i}][{j}]'''   )
               else:
                  self.analyse_variable(lowerCamelCase__					,	F'''output[{i}]'''   )
         else:
            self.analyse_variable(lowerCamelCase__					,	"output"   )
  
         self.save_frame()
  
  
  
  
      def     _a						(						self   ):
         self.model.apply(self._register_forward_hook   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         module.register_forward_hook(self.forward_hook   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         # - input is a tuple of packed inputs (could be non-Tensors)
         # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
         lowerCAmelCase_:       Optional[int]         =  False
  
         lowerCAmelCase_:       int         =  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
            lowerCAmelCase_:       Any         =  True
  
         self.create_frame(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
         # 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 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Union[str, Any]         =  var.abs()
     return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Union[str, Any]         =  False
     if torch.isnan(lowercase				).any().item():
        lowerCAmelCase_:       Optional[int]         =  True
        print(F'''{ctx} has nans'''				)
     if torch.isinf(lowercase				).any().item():
        lowerCAmelCase_:       str         =  True
        print(F'''{ctx} has infs'''				)
  
     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
        lowerCAmelCase_:       Any         =  var[torch.ge(var.abs()       ,							100				)]
        if naaa.numel() > 0:
           print(F'''{ctx}:  n100={naaa.numel()}'''				)
        lowerCAmelCase_:       Dict         =  var[torch.ge(var.abs()       ,							1000				)]
        if naaaa.numel() > 0:
           print(F'''{ctx}: n1000={naaaa.numel()}'''				)
        lowerCAmelCase_:       Optional[int]         =  var[torch.ge(var.abs()       ,							10000				)]
        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 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						'underflow_overflow'
      SCREAMING_SNAKE_CASE:							Tuple        =						'tpu_metrics_debug'
style_context: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')