





"""simple docstring"""


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


a__					:     Any							=				logging.get_logger(__name__)



class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=21   ,					__SCREAMING_SNAKE_CASE=[]   ,					__SCREAMING_SNAKE_CASE=None     ):






        '''simple docstring'''

        snake_case__    							=				model
        snake_case__    							=				trace_batch_nums
        snake_case__    							=				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
        snake_case__    							=				collections.deque([]   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[]
        snake_case__    							=				0
        snake_case__    							=				0
        snake_case__    							=				False
        snake_case__    							=				"                 "

        self.analyse_model()

        self.register_forward_hook()

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE=None     ):






        '''simple docstring'''

        if frame is not None:
         self.expand_frame(__SCREAMING_SNAKE_CASE     )
        self.frames.append("\n".join(self.frame     )     )
        snake_case__    							=				[]  # start a new frame

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        self.frame.append(__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        print("\n".join(self.frames     )     )
        snake_case__    							=				[]

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				[]

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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"     )
        snake_case__    							=				[]

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				{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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if torch.is_tensor(__SCREAMING_SNAKE_CASE     ):
         self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )     )
         if detect_overflow(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):
          snake_case__    							=				True
        elif var is None:
         self.expand_frame(F"{'None':>17} {ctx}"     )
        else:
         self.expand_frame(F"{'not a tensor':>17} {ctx}"     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"     )

        # params
        for name, p in module.named_parameters(recurse=__SCREAMING_SNAKE_CASE     ):
         self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

        # inputs
        if isinstance(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):
         for i, x in enumerate(__SCREAMING_SNAKE_CASE     ):
          self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					F"input[{i}]"     )
        else:
         self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					"input"     )

        # outputs
        if isinstance(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):
         for i, x in enumerate(__SCREAMING_SNAKE_CASE     ):
          # possibly a tuple of tuples
          if isinstance(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):
           for j, y in enumerate(__SCREAMING_SNAKE_CASE     ):
            self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					F"output[{i}][{j}]"     )
          else:
           self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					F"output[{i}]"     )
        else:
         self.analyse_variable(__SCREAMING_SNAKE_CASE   ,					"output"     )

        self.save_frame()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.model.apply(self._register_forward_hook     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        module.register_forward_hook(self.forward_hook     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				False

        snake_case__    							=				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
         snake_case__    							=				True

        self.create_frame(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

        # 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 	a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[int]:

 '''simple docstring'''






 snake_case__    							=				var.abs()
 return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 	a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[Any]:

 '''simple docstring'''






 snake_case__    							=				False
 if torch.isnan(_UpperCAmelCase	).any().item():
  snake_case__    							=				True
  print(f"{ctx} has nans"	)
 if torch.isinf(_UpperCAmelCase	).any().item():
  snake_case__    							=				True
  print(f"{ctx} has infs"	)

 # if needed to monitor large elements can enable the following
 if 0:  # and detected:
  snake_case__    							=				var[torch.ge(var.abs()	,     1_0_0	)]
  if naaa.numel() > 0:
   print(f"{ctx}:  n100={naaa.numel()}"	)
  snake_case__    							=				var[torch.ge(var.abs()	,     1_0_0_0	)]
  if naaaa.numel() > 0:
   print(f"{ctx}: n1000={naaaa.numel()}"	)
  snake_case__    							=				var[torch.ge(var.abs()	,     1_0_0_0_0	)]
  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_				(     __lowerCAmelCase							):
       snake_case__				:							List[str]											=     "underflow_overflow"
       snake_case__				:							Optional[Any]											=     "tpu_metrics_debug"








"""simple docstring"""


def 							a__			(  _UpperCAmelCase = 1_0_0_0	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				[]
 snake_case__    							=				[]
 snake_case__    							=				3
 snake_case__    							=				5
 snake_case__    							=				1
 while True:
  snake_case__    							=				z * temp
  if result < n:
   zmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   snake_case__    							=				1
   break
 while True:
  snake_case__    							=				x * temp
  if result < n:
   xmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   break
 snake_case__    							=				list(set(xmulti + zmulti	)	)
 return sum(_UpperCAmelCase	)


if __name__ == "__main__":
   print(f'''{solution() = }''')

