





'''simple docstring'''


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						:




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=21     ,       _lowerCAmelCase=[]     ,       _lowerCAmelCase=None						)      ->							Optional[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([]     ,       _lowerCAmelCase						)
     _lowerCAmelCase		     = []
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = False
     _lowerCAmelCase		     = "                 "

     self.analyse_model()

     self.register_forward_hook()




 def 							_snake_case				(		self     ,       _lowerCAmelCase=None						)      ->							Optional[Any]:
     if frame is not None:
         self.expand_frame(_lowerCAmelCase						)
     self.frames.append("\n".join(self.frame						)						)
     _lowerCAmelCase		     = []  # start a new frame




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[int]:
     self.frame.append(_lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Any:
     print("\n".join(self.frames						)						)
     _lowerCAmelCase		     = []




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = []




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




 def 							_snake_case				(		self						)      ->							List[str]:
     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 							_snake_case				(		self						)      ->							Tuple:
     self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Tuple:
     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 							_snake_case				(		self						)      ->							int:
     self.model.apply(self._register_forward_hook						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							List[str]:
     module.register_forward_hook(self.forward_hook						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Dict:
     # - 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(_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 		__a(SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   List[Any]      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    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 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							List[Any]    					=   "underflow_overflow"
 __lowerCamelCase  :							List[str]    					=   "tpu_metrics_debug"









'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE			=							TypeVar("T", bound=bool)

class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = value
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = None




 @property
 def 							_snake_case				(		self						)      ->							T:
     return self._value





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							SkewNode[T] | None:
     if not roota:
         return roota

     if not roota:
         return roota

     if roota.value > roota.value:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = roota, roota

     _lowerCAmelCase		     = roota
     _lowerCAmelCase		     = roota.right
     _lowerCAmelCase		     = roota.left
     _lowerCAmelCase		     = SkewNode.merge(_lowerCAmelCase     ,       _lowerCAmelCase						)

     return result







class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase = ()						)      ->							None:
     _lowerCAmelCase		     = None
     if data:
         for item in data:
             self.insert(_lowerCAmelCase						)




 def __bool__(		self						)      ->							bool:
     return self._root is not None




 def __iter__(		self						)      ->							Iterator[T]:
     _lowerCAmelCase		     = []
     while self:
         result.append(self.pop()						)

     # Pushing items back to the heap not to clear it.
     for item in result:
         self.insert(_lowerCAmelCase						)

     return iter(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = SkewNode.merge(self._root     ,       SkewNode(_lowerCAmelCase						)						)




 def 							_snake_case				(		self						)      ->							T | None:
     _lowerCAmelCase		     = self.top()
     _lowerCAmelCase		     = (
         SkewNode.merge(self._root.left     ,       self._root.right						) if self._root else None
     )

     return result




 def 							_snake_case				(		self						)      ->							T:
     if not self._root:
         raise IndexError("Can't get top element for the empty heap."						)
     return self._root.value





 def 							_snake_case				(		self						)      ->							None:
     _lowerCAmelCase		     = None


if __name__ == "__main__":
       import doctest

       doctest.testmod()


