




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


SCREAMING_SNAKE_CASE__ :   Optional[int]						=     logging.get_logger(__name__)




class 	UpperCamelCase__			:

    '''simple docstring'''



    def __init__(					self  ,						UpperCamelCase__  ,						UpperCamelCase__=21  ,						UpperCamelCase__=[]  ,						UpperCamelCase__=None  )  ->   str:
           lowerCamelCase     :   int    				=      model
           lowerCamelCase     :   int    				=      trace_batch_nums
           lowerCamelCase     :   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
           lowerCamelCase     :   Any    				=      collections.deque([]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      []
           lowerCamelCase     :   int    				=      0
           lowerCamelCase     :   int    				=      0
           lowerCamelCase     :   Any    				=      False
           lowerCamelCase     :   int    				=      "                 "

           self.analyse_model()

           self.register_forward_hook()







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  )  ->   Dict:
           if frame is not None:
                  self.expand_frame(UpperCamelCase__  )
           self.frames.append("\n".join(self.frame  )  )
           lowerCamelCase     :   Union[str, Any]    				=      []  # start a new frame







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           self.frame.append(UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           print("\n".join(self.frames  )  )
           lowerCamelCase     :   Union[str, Any]    				=      []







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   Dict    				=      []







    def 					_lowercase  (					self  )  ->   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     :   Optional[int]    				=      []







    def 					_lowercase  (					self  )  ->   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     :   int    				=      {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 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   List[Any]:
           if torch.is_tensor(UpperCamelCase__  ):
                  self.expand_frame(get_abs_min_max(UpperCamelCase__  ,						UpperCamelCase__  )  )
                  if detect_overflow(UpperCamelCase__  ,						UpperCamelCase__  ):
                         lowerCamelCase     :   Optional[Any]    				=      True
           elif var is None:
                  self.expand_frame(F'''{"None":>17} {ctx}'''  )
           else:
                  self.expand_frame(F'''{"not a tensor":>17} {ctx}'''  )







    def 					_lowercase  (					self  )  ->   Dict:
           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 					_lowercase  (					self  )  ->   Union[str, Any]:
           self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   int:
           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 					_lowercase  (					self  )  ->   Dict:
           self.model.apply(self._register_forward_hook  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Optional[Any]:
           module.register_forward_hook(self.forward_hook  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   int:
           # - 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     :   str    				=      False

           lowerCamelCase     :   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
                  lowerCamelCase     :   Optional[Any]    				=      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				,_SCREAMING_SNAKE_CASE					)			->				List[str]:
       lowerCamelCase     :   Dict    				=      var.abs()
       return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Union[str, Any]:
       lowerCamelCase     :   Any    				=      False
       if torch.isnan(_SCREAMING_SNAKE_CASE					).any().item():
              lowerCamelCase     :   Union[str, Any]    				=      True
              print(f'''{ctx} has nans'''					)
       if torch.isinf(_SCREAMING_SNAKE_CASE					).any().item():
              lowerCamelCase     :   List[Any]    				=      True
              print(f'''{ctx} has infs'''					)

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

    '''simple docstring'''



    lowerCamelCase_   :			Union[str, Any]			  =   """underflow_overflow"""
    lowerCamelCase_   :			Any			  =   """tpu_metrics_debug"""








from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				list:
       lowerCamelCase     :   int    				=      [None] * (len(_SCREAMING_SNAKE_CASE					) + len(_SCREAMING_SNAKE_CASE					))

       lowerCamelCase     :   Any    				=      0  # pointer to current index for left Half
       lowerCamelCase     :   int    				=      0  # pointer to current index for the right Half
       lowerCamelCase     :   Optional[int]    				=      0  # pointer to current index for the sorted array Half

       while pointera < len(_SCREAMING_SNAKE_CASE					) and pointera < len(_SCREAMING_SNAKE_CASE					):
              if left_half[pointera] < right_half[pointera]:
                     lowerCamelCase     :   Any    				=      left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     lowerCamelCase     :   Any    				=      right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Optional[Any]    				=      left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   str    				=      right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list:
       if len(_SCREAMING_SNAKE_CASE					) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       lowerCamelCase     :   int    				=      0 + (len(_SCREAMING_SNAKE_CASE					) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       lowerCamelCase     :   Optional[Any]    				=      array[:middle]
       lowerCamelCase     :   Optional[Any]    				=      array[middle:]

       return merge(merge_sort(_SCREAMING_SNAKE_CASE					)				,merge_sort(_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






