"""simple docstring"""







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


lowerCamelCase      : Any       =logging.get_logger(__name__)






class 							__snake_case:


    '''simple docstring'''





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




         '''simple docstring'''



         __A					:					int								=		model
         __A					:					Any								=		trace_batch_nums
         __A					:					List[str]								=		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					:					str								=		collections.deque([]					,       lowercase__	)
         __A					:					Union[str, Any]								=		[]
         __A					:					Optional[int]								=		0
         __A					:					Any								=		0
         __A					:					int								=		False
         __A					:					Dict								=		'''                 '''

         self.analyse_model()

         self.register_forward_hook()
    def 						_a							(  self					,       __lowerCamelCase=None	):




         '''simple docstring'''



         if frame is not None:
              self.expand_frame(lowercase__	)
         self.frames.append('\n'.join(self.frame	)	)
         __A					:					Optional[int]								=		[]  # start a new frame
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         self.frame.append(lowercase__	)
    def 						_a							(  self	):




         '''simple docstring'''



         print('\n'.join(self.frames	)	)
         __A					:					int								=		[]
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		[]
    def 						_a							(  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'	)
         __A					:					int								=		[]
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[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	):




         '''simple docstring'''



         if torch.is_tensor(lowercase__	):
              self.expand_frame(get_abs_min_max(lowercase__					,       lowercase__	)	)
              if detect_overflow(lowercase__					,       lowercase__	):
                   __A					:					Union[str, Any]								=		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	):




         '''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 						_a							(  self	):




         '''simple docstring'''



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




         '''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 						_a							(  self	):




         '''simple docstring'''



         self.model.apply(self._register_forward_hook	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         module.register_forward_hook(self.forward_hook	)

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					int								=		False

         __A					:					Optional[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
              __A					:					Optional[int]								=		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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				Union[str, Any]						)    ->     str:







     '''simple docstring'''


     __A					:					List[str]								=		var.abs()
     return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					Optional[Any]								=		False
     if torch.isnan(lowerCAmelCase_						).any().item():
          __A					:					str								=		True
          print(F'{ctx} has nans'						)
     if torch.isinf(lowerCAmelCase_						).any().item():
          __A					:					List[Any]								=		True
          print(F'{ctx} has infs'						)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          __A					:					Optional[Any]								=		var[torch.ge(var.abs()  ,       100						)]
          if naaa.numel() > 0:
               print(F'{ctx}:  n100={naaa.numel()}'						)
          __A					:					int								=		var[torch.ge(var.abs()  ,       1000						)]
          if naaaa.numel() > 0:
               print(F'{ctx}: n1000={naaaa.numel()}'						)
          __A					:					str								=		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 							__snake_case(					a__	):


    '''simple docstring'''





    _UpperCAmelCase       =			"""underflow_overflow"""
    _UpperCAmelCase       =			"""tpu_metrics_debug"""










'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      if counter == 0 or capacity == 0:
            return 0

      # If weight of the nth item is more than Knapsack of capacity,
      #   then this item cannot be included in the optimal solution,
      # else return the maximum of two cases:
      #   (1) nth item included
      #   (2) not included
      if weights[counter - 1] > capacity:
            return knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
      else:
            _snake_case		:						List[str]         =  capacity - weights[counter - 1]
            _snake_case		:						Tuple         =  values[counter - 1] + knapsack(
                lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            _snake_case		:						Any         =  knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            return max(lowerCAmelCase_      ,   lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


