






from ..utils import requires_backends


SCREAMING_SNAKE_CASE__		:       int   							=							None







class 							UpperCAmelCase_     :




     def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(self  ,	["""detectron2"""]		)




     @classmethod
     def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(cls  ,	["""detectron2"""]		)











import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE__		:       Any   							=							logging.get_logger(__name__)







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
          UpperCAmelCase__			:     Union[str, Any]										= model
          UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
          UpperCAmelCase__			:     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
          UpperCAmelCase__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
          UpperCAmelCase__			:     str										= []
          UpperCAmelCase__			:     Optional[int]										= 0
          UpperCAmelCase__			:     int										= 0
          UpperCAmelCase__			:     int										= False
          UpperCAmelCase__			:     List[str]										= """                 """

          self.analyse_model()

          self.register_forward_hook()




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
          if frame is not None:
               self.expand_frame(_lowerCAmelCase		)
          self.frames.append("""\n""".join(self.frame		)		)
          UpperCAmelCase__			:     str										= []  # start a new frame




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          self.frame.append(_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self		):
          print("""\n""".join(self.frames		)		)
          UpperCAmelCase__			:     List[Any]										= []




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[int]										= []




     def      __UpperCAmelCase     (							self		):
          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__			:     Any										= []




     def      __UpperCAmelCase     (							self		):
          # 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__			:     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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          if torch.is_tensor(_lowerCAmelCase		):
               self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
               if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                    UpperCAmelCase__			:     Optional[int]										= True
          elif var is None:
               self.expand_frame(f"{'None':>17} {ctx}"		)
          else:
               self.expand_frame(f"{'not a tensor':>17} {ctx}"		)




     def      __UpperCAmelCase     (							self		):
          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      __UpperCAmelCase     (							self		):
          self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)

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

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

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

          self.save_frame()




     def      __UpperCAmelCase     (							self		):
          self.model.apply(self._register_forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          module.register_forward_hook(self.forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          # - 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__			:     Union[str, Any]										= False

          UpperCAmelCase__			:     List[str]										= 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__			:     Dict										= True

          self.create_frame(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)

          # 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Dict										= var.abs()
     return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:

     '''simple docstring'''
     UpperCAmelCase__			:     str										= False
     if torch.isnan(__lowerCamelCase					).any().item():
          UpperCAmelCase__			:     List[Any]										= True
          print(F"{ctx} has nans"					)
     if torch.isinf(__lowerCamelCase					).any().item():
          UpperCAmelCase__			:     List[str]										= True
          print(F"{ctx} has infs"					)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
          if naaa.numel() > 0:
               print(F"{ctx}:  n100={naaa.numel()}"					)
          UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
          if naaaa.numel() > 0:
               print(F"{ctx}: n1000={naaaa.numel()}"					)
          UpperCAmelCase__			:     Optional[int]										= var[torch.ge(var.abs()   ,       1_0000					)]
          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_     (		__lowerCamelCase		):
     __lowerCamelCase										=     'underflow_overflow'
     __lowerCamelCase										=     'tpu_metrics_debug'






