


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


_A									=		logging.get_logger(__name__)






class        UpperCAmelCase__			:



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=21   ,  A_=[]   ,  A_=None       )    ->			List[Any]:
         __UpperCamelCase       				=model
         __UpperCamelCase       				=trace_batch_nums
         __UpperCamelCase       				=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
         __UpperCamelCase       				=collections.deque([]   ,  A_       )
         __UpperCamelCase       				=[]
         __UpperCamelCase       				=0
         __UpperCamelCase       				=0
         __UpperCamelCase       				=False
         __UpperCamelCase       				='                 '

         self.analyse_model()

         self.register_forward_hook()






    def _a   (     self   ,  A_=None       )    ->			Dict:
         if frame is not None:
              self.expand_frame(A_       )
         self.frames.append('\n'.join(self.frame       )       )
         __UpperCamelCase       				=[]  # start a new frame






    def _a   (     self   ,  A_       )    ->			Dict:
         self.frame.append(A_       )






    def _a   (     self       )    ->			str:
         print('\n'.join(self.frames       )       )
         __UpperCamelCase       				=[]






    def _a   (     self       )    ->			Optional[int]:
         __UpperCamelCase       				=[]






    def _a   (     self       )    ->			List[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'       )
         __UpperCamelCase       				=[]






    def _a   (     self       )    ->			Tuple:
         # 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
         __UpperCamelCase       				={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   ,  A_   ,  A_       )    ->			Dict:
         if torch.is_tensor(A_       ):
              self.expand_frame(get_abs_min_max(A_   ,  A_       )       )
              if detect_overflow(A_   ,  A_       ):
                   __UpperCamelCase       				=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       )    ->			Union[str, Any]:
         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       )    ->			Any:
         self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'       )






    def _a   (     self   ,  A_   ,  A_   ,  A_       )    ->			List[str]:
         self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'       )

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

         # inputs
         if isinstance(A_   ,  A_       ):
              for i, x in enumerate(A_       ):
                   self.analyse_variable(A_   ,  f'input[{i}]'       )
         else:
              self.analyse_variable(A_   ,  'input'       )

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

         self.save_frame()






    def _a   (     self       )    ->			int:
         self.model.apply(self._register_forward_hook       )






    def _a   (     self   ,  A_       )    ->			List[str]:
         module.register_forward_hook(self.forward_hook       )






    def _a   (     self   ,  A_   ,  A_   ,  A_       )    ->			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

         __UpperCamelCase       				=False

         __UpperCamelCase       				=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
              __UpperCamelCase       				=True

         self.create_frame(A_   ,  A_   ,  A_       )

         # 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							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __UpperCamelCase       				=var.abs()
     return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     __UpperCamelCase       				=False
     if torch.isnan(SCREAMING_SNAKE_CASE__							).any().item():
          __UpperCamelCase       				=True
          print(F'{ctx} has nans'							)
     if torch.isinf(SCREAMING_SNAKE_CASE__							).any().item():
          __UpperCamelCase       				=True
          print(F'{ctx} has infs'							)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          __UpperCamelCase       				=var[torch.ge(var.abs()     ,	1_00							)]
          if naaa.numel() > 0:
               print(F'{ctx}:  n100={naaa.numel()}'							)
          __UpperCamelCase       				=var[torch.ge(var.abs()     ,	10_00							)]
          if naaaa.numel() > 0:
               print(F'{ctx}: n1000={naaaa.numel()}'							)
          __UpperCamelCase       				=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__			(   A_						):



    """simple docstring"""






    UpperCAmelCase__		:						Dict    =    "underflow_overflow"
    UpperCAmelCase__		:						int    =    "tpu_metrics_debug"









def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     if n < 0:
          return 0
     # table[i] represents the number of ways to get to amount i
     __UpperCamelCase       				=[0] * (n + 1)

     # There is exactly 1 way to get to zero(You pick no coins).
     __UpperCamelCase       				=1

     # Pick all coins one by one and update table[] values
     # after the index greater than or equal to the value of the
     # picked coin
     for coin_val in s:
          for j in range(SCREAMING_SNAKE_CASE__     ,	n + 1							):
               table[j] += table[j - coin_val]

     return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





