





'''simple docstring'''


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


lowerCAmelCase      :  Tuple      							=logging.get_logger(__name__)
class    a_     :





     def __init__(				self	:      List[Any]			,   lowercase	:      Optional[Any]			,   lowercase	:      Tuple=21			,   lowercase	:      Union[str, Any]=[]			,   lowercase	:      Dict=None		):





        """simple docstring"""




        lowercase_    :List[str]					    =				model
        lowercase_    :List[Any]					    =				trace_batch_nums
        lowercase_    :Optional[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
        lowercase_    :Any					    =				collections.deque([]			,   lowercase		)
        lowercase_    :Optional[int]					    =				[]
        lowercase_    :Any					    =				0
        lowercase_    :Optional[Any]					    =				0
        lowercase_    :Union[str, Any]					    =				False
        lowercase_    :Optional[Any]					    =				"                 "

        self.analyse_model()

        self.register_forward_hook()





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      Optional[Any]=None		):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      Tuple		):





        """simple docstring"""




        self.frame.append(lowercase		)





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        print("\n".join(self.frames		)		)
        lowercase_    :int					    =				[]





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :int					    =				[]





     def 							lowercase__   (				self	:      List[str]		):





        """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"		)
        lowercase_    :Any					    =				[]





     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    :str					    =				{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 							lowercase__   (				self	:      List[Any]			,   lowercase	:      int			,   lowercase	:      Optional[Any]		):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Optional[int]		):





        """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 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      Optional[Any]			,   lowercase	:      int			,   lowercase	:      Optional[int]		):





        """simple docstring"""




        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 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        self.model.apply(self._register_forward_hook		)





     def 							lowercase__   (				self	:      Any			,   lowercase	:      List[Any]		):





        """simple docstring"""




        module.register_forward_hook(self.forward_hook		)







     def 							lowercase__   (				self	:      int			,   lowercase	:      Any			,   lowercase	:      Optional[Any]			,   lowercase	:      Any		):





        """simple docstring"""




        lowercase_    :str					    =				False

        lowercase_    :Union[str, Any]					    =				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
           lowercase_    :str					    =				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      :      Optional[Any]			,__lowerCamelCase      :      Dict				):
   lowercase_    :Optional[Any]					    =				var.abs()
   return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[Any]			,__lowerCamelCase      :      List[Any]				):
   lowercase_    :List[Any]					    =				False
   if torch.isnan(__lowerCamelCase				).any().item():
      lowercase_    :List[str]					    =				True
      print(F'{ctx} has nans'				)
   if torch.isinf(__lowerCamelCase				).any().item():
      lowercase_    :Dict					    =				True
      print(F'{ctx} has infs'				)

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      lowercase_    :Union[str, Any]					    =				var[torch.ge(var.abs()			,1_00				)]
      if naaa.numel() > 0:
         print(F'{ctx}:  n100={naaa.numel()}'				)
      lowercase_    :Tuple					    =				var[torch.ge(var.abs()			,10_00				)]
      if naaaa.numel() > 0:
         print(F'{ctx}: n1000={naaaa.numel()}'				)
      lowercase_    :List[str]					    =				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    a_     (       _lowerCAmelCase							):
     __A  				=    "underflow_overflow"
     __A  				=    "tpu_metrics_debug"









'''simple docstring'''


import math
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int				):

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5			,int(math.sqrt(__lowerCamelCase				) + 1				)			,6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int = 2_00_00_00				):
   return sum(num for num in range(3			,__lowerCamelCase			,2				) if is_prime(__lowerCamelCase				)				) + 2 if n > 2 else 0


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


