





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


snake_case__   :				int						=	logging.get_logger(__name__)





class A_   :




   def __init__(self      :Optional[Any]       ,  _UpperCamelCase      :int       ,  _UpperCamelCase      :List[str]=21       ,  _UpperCamelCase      :Optional[int]=[]       ,  _UpperCamelCase      :Tuple=None					)->   Any:
         __A			  =							model
         __A			  =							trace_batch_nums
         __A			  =							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			  =							collections.deque([]       ,  _UpperCamelCase					)
         __A			  =							[]
         __A			  =							0
         __A			  =							0
         __A			  =							False
         __A			  =							'''                 '''

         self.analyse_model()

         self.register_forward_hook()




   def _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :str=None					)->   str:
         if frame is not None:
               self.expand_frame(_UpperCamelCase					)
         self.frames.append('''\n'''.join(self.frame					)					)
         __A			  =							[]  # start a new frame




   def _lowerCAmelCase (self      :Dict       ,  _UpperCamelCase      :Union[str, Any]					)->   Dict:
         self.frame.append(_UpperCamelCase					)




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




   def _lowerCAmelCase (self      :Optional[Any]					)->   Union[str, Any]:
         __A			  =							[]




   def _lowerCAmelCase (self      :str					)->   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'''					)
         __A			  =							[]




   def _lowerCAmelCase (self      :Tuple					)->   Dict:
         # 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
         __A			  =							{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 _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]					)->   List[str]:
         if torch.is_tensor(_UpperCamelCase					):
               self.expand_frame(get_abs_min_max(_UpperCamelCase       ,  _UpperCamelCase					)					)
               if detect_overflow(_UpperCamelCase       ,  _UpperCamelCase					):
                     __A			  =							True
         elif var is None:
               self.expand_frame(f"""{'None':>17} {ctx}"""					)
         else:
               self.expand_frame(f"""{'not a tensor':>17} {ctx}"""					)




   def _lowerCAmelCase (self      :Optional[int]					)->   List[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 _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         self.expand_frame(f"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""					)




   def _lowerCAmelCase (self      :Dict       ,  _UpperCamelCase      :Dict       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :List[Any]					)->   str:
         self.expand_frame(f"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""					)

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

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

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

         self.save_frame()




   def _lowerCAmelCase (self      :Tuple					)->   List[str]:
         self.model.apply(self._register_forward_hook					)




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :int					)->   int:
         module.register_forward_hook(self.forward_hook					)






   def _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Union[str, Any]					)->   Optional[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

         __A			  =							False

         __A			  =							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			  =							True

         self.create_frame(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         # 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							(      lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  List[str]					)      ->		Optional[int]:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  str					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							False
      if torch.isnan(lowerCamelCase					).any().item():
            __A			  =							True
            print(F"""{ctx} has nans"""					)
      if torch.isinf(lowerCamelCase					).any().item():
            __A			  =							True
            print(F"""{ctx} has infs"""					)

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












def _a							(      lowerCamelCase:  List[Any]					)      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase   ,							len(lowerCamelCase					)					):
                  __A			  =							collection[i]
                  __A			  =							i
                  while j >= gap and collection[j - gap] > insert_value:
                        __A			  =							collection[j - gap]
                        j -= gap
                  if j != i:
                        __A			  =							insert_value
      return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       snake_case__   :				Optional[int]						=	input('Enter numbers separated by a comma:\n').strip()
       snake_case__   :				int						=	[int(item) for item in user_input.split(',')]
       print(shell_sort(unsorted))





