
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


_UpperCamelCase:					int     =logging.get_logger(__name__)





class 						__lowercase:


       """simple docstring"""







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

         self.analyse_model()

         self.register_forward_hook()



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



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



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



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



       def SCREAMING_SNAKE_CASE_				(     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'       )
         _lowerCAmelCase						=		[]



       def SCREAMING_SNAKE_CASE_				(     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
         _lowerCAmelCase						=		{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					:			Union[str, Any]  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[int]       )					->   List[str]:
         if torch.is_tensor(_UpperCamelCase       ):
           self.expand_frame(get_abs_min_max(_UpperCamelCase  ,      _UpperCamelCase       )       )
           if detect_overflow(_UpperCamelCase  ,      _UpperCamelCase       ):
             _lowerCAmelCase						=		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					:			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 SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   List[Any]:
         self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[str]:
         self.model.apply(self._register_forward_hook       )



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






       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			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

         _lowerCAmelCase						=		False

         _lowerCAmelCase						=		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
           _lowerCAmelCase						=		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  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  _lowerCAmelCase						=		False
  if torch.isnan(__SCREAMING_SNAKE_CASE    ).any().item():
    _lowerCAmelCase						=		True
    print(f'''{ctx} has nans'''    )
  if torch.isinf(__SCREAMING_SNAKE_CASE    ).any().item():
    _lowerCAmelCase						=		True
    print(f'''{ctx} has infs'''    )

  # if needed to monitor large elements can enable the following
  if 0:  # and detected:
    _lowerCAmelCase						=		var[torch.ge(var.abs()		,				100    )]
    if naaa.numel() > 0:
      print(f'''{ctx}:  n100={naaa.numel()}'''    )
    _lowerCAmelCase						=		var[torch.ge(var.abs()		,				1000    )]
    if naaaa.numel() > 0:
      print(f'''{ctx}: n1000={naaaa.numel()}'''    )
    _lowerCAmelCase						=		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 						__lowercase(     _lowerCamelCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''underflow_overflow'''
       UpperCamelCase_				 =							'''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))





