





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


__lowercase				    =      logging.get_logger(__name__)

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Any      ,    snake_case    :		Any      ,    snake_case    :		int=21      ,    snake_case    :		Optional[Any]=[]      ,    snake_case    :		int=None				):

      """simple docstring"""





      __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 						A		( self    :		Tuple      ,    snake_case    :		Any=None				):

      """simple docstring"""





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





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		Optional[Any]				):

      """simple docstring"""





      self.frame.append(lowerCAmelCase_				)





    def 						A		( self    :		Union[str, Any]				):

      """simple docstring"""





      print('''\n'''.join(self.frames				)				)
      __lowerCAmelCase								=					[]





    def 						A		( self    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase								=					[]





    def 						A		( self    :		Optional[Any]				):

      """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'''				)
      __lowerCAmelCase								=					[]





    def 						A		( self    :		Tuple				):

      """simple docstring"""





      __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 						A		( self    :		int      ,    snake_case    :		Any      ,    snake_case    :		str				):

      """simple docstring"""





      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 						A		( self    :		Optional[Any]				):

      """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 						A		( self    :		Tuple				):

      """simple docstring"""





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





    def 						A		( self    :		Any      ,    snake_case    :		List[Any]      ,    snake_case    :		List[str]      ,    snake_case    :		List[str]				):

      """simple docstring"""





      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 						A		( self    :		Tuple				):

      """simple docstring"""





      self.model.apply(self._register_forward_hook				)





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		Dict				):

      """simple docstring"""





      module.register_forward_hook(self.forward_hook				)





    def 						A		( self    :		Any      ,    snake_case    :		int      ,    snake_case    :		int      ,    snake_case    :		Dict				):

      """simple docstring"""





      __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        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]  ,				__lowercase:		str):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]  ,				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					False
  if torch.isnan(lowerCAmelCase_).any().item():
    __lowerCAmelCase								=					True
    print(F'''{ctx} has nans''')
  if torch.isinf(lowerCAmelCase_).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()  ,				1_00)]
    if naaa.numel() > 0:
      print(F'''{ctx}:  n100={naaa.numel()}''')
    __lowerCAmelCase								=					var[torch.ge(var.abs()  ,				10_00)]
    if naaaa.numel() > 0:
      print(F'''{ctx}: n1000={naaaa.numel()}''')
    __lowerCAmelCase								=					var[torch.ge(var.abs()  ,				1_00_00)]
    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 	__snake_case					(      snake_case__ ):






    '''simple docstring'''
    _lowerCAmelCase  =						'underflow_overflow'
    _lowerCAmelCase  =						'tpu_metrics_debug'

UpperCAmelCase				 =   {
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase				 =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase				 =   [0 for x in range(len(graph))]
UpperCAmelCase				 =   []







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[Any]:
     a	:     Tuple						      =    1
     while stack:
          a	:     Optional[Any]						      =    stack.pop()
          print(lowerCAmelCase_    ,   clothes[current_clothing]							)
          order += 1







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     stack.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     for v in range(len(lowerCAmelCase_							)							):
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)


if __name__ == "__main__":
  topological_sort(graph, visited)
  print(stack)
  print_stack(stack, clothes)