'''simple docstring'''


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
 import torch


snake_case__	:     Dict      =    logging.get_logger(__name__)






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=2_1 ,						snake_case_=[] ,						snake_case_=None		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = model
       UpperCAmelCase_				:  Dict  = trace_batch_nums
       UpperCAmelCase_				:  Optional[int]  = 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_				:  str  = collections.deque([] ,						snake_case_		)
       UpperCAmelCase_				:  List[str]  = []
       UpperCAmelCase_				:  List[Any]  = 0
       UpperCAmelCase_				:  List[str]  = 0
       UpperCAmelCase_				:  Any  = False
       UpperCAmelCase_				:  Tuple  = '                 '

       self.analyse_model()

       self.register_forward_hook()


  def _UpperCamelCase	(					self ,						snake_case_=None		):




       '''simple docstring'''

       if frame is not None:
            self.expand_frame(snake_case_		)
       self.frames.append('\n'.join(self.frame		)		)
       UpperCAmelCase_				:  int  = []  # start a new frame


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       self.frame.append(snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       print('\n'.join(self.frames		)		)
       UpperCAmelCase_				:  str  = []


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = []


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       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_				:  Optional[Any]  = []


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = {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 ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if torch.is_tensor(snake_case_		):
            self.expand_frame(get_abs_min_max(snake_case_ ,						snake_case_		)		)
            if detect_overflow(snake_case_ ,						snake_case_		):
                 UpperCAmelCase_				:  Union[str, Any]  = 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		):




       '''simple docstring'''

       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		):




       '''simple docstring'''

       self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''		)

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

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

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

       self.save_frame()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.model.apply(self._register_forward_hook		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       module.register_forward_hook(self.forward_hook		)






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = False

       UpperCAmelCase_				:  Optional[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
            UpperCAmelCase_				:  Dict  = True

       self.create_frame(snake_case_ ,						snake_case_ ,						snake_case_		)

       # 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_   :				Tuple      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





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


def    _lowerCamelCase       ( lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Optional[Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = False
     if torch.isnan(lowerCamelCase_  ).any().item():
          UpperCAmelCase_				:  Optional[Any]  = True
          print(F'''{ctx} has nans'''  )
     if torch.isinf(lowerCamelCase_  ).any().item():
          UpperCAmelCase_				:  Tuple  = True
          print(F'''{ctx} has infs'''  )

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







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''underflow_overflow'''
  lowerCamelCase_						:str		       =						'''tpu_metrics_debug'''







'''simple docstring'''


from __future__ import annotations

snake_case__	:     str      =    [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  list[int]  = [-1] * length
       UpperCAmelCase_				:  Node | None  = None
       UpperCAmelCase_				:  Node | None  = None






  def __repr__(					self		):




       '''simple docstring'''

       return F'''Node(min_value={self.minn} max_value={self.maxx})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				list[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = Node(len(lowerCamelCase_  )  )
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = min(lowerCamelCase_  ), max(lowerCamelCase_  )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     UpperCAmelCase_				:  str  = (root.minn + root.maxx) // 2

     UpperCAmelCase_				:  list[int]  = []
     UpperCAmelCase_				:  list[int]  = []

     for index, num in enumerate(lowerCamelCase_  ):
          if num <= pivot:
               left_arr.append(lowerCamelCase_  )
          else:
               right_arr.append(lowerCamelCase_  )
          UpperCAmelCase_				:  Tuple  = len(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     return root


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     UpperCAmelCase_				:  Any  = (node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left      ,	lowerCamelCase_      ,	node.map_left[index] - 1  )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right      ,	lowerCamelCase_      ,	index - node.map_left[index]  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if start > end:
          return 0
     UpperCAmelCase_				:  int  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     UpperCAmelCase_				:  List[Any]  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	start - 1  )
     return rank_till_end - rank_before_start


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     UpperCAmelCase_				:  Tuple  = node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left      ,	lowerCamelCase_      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	)
     else:
          return quantile(
              node.right      ,	index - num_elements_in_left_tree      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	)


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     UpperCAmelCase_				:  Any  = range_counting(
         node.left      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     UpperCAmelCase_				:  List[str]  = range_counting(
         node.right      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     return left + right


if __name__ == "__main__":
 import doctest

 doctest.testmod()






