import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
 import torch


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

class 				_lowercase						:






    '''simple docstring'''




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

       self.analyse_model()

       self.register_forward_hook()




    def     _a						(						self					,	lowerCamelCase__=None   ):
       if frame is not None:
          self.expand_frame(lowerCamelCase__   )
       self.frames.append("\n".join(self.frame   )   )
       lowerCAmelCase_:       List[str]         =  []  # start a new frame




    def     _a						(						self					,	lowerCamelCase__   ):
       self.frame.append(lowerCamelCase__   )




    def     _a						(						self   ):
       print("\n".join(self.frames   )   )
       lowerCAmelCase_:       str         =  []




    def     _a						(						self   ):
       lowerCAmelCase_:       str         =  []




    def     _a						(						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"   )
       lowerCAmelCase_:       List[str]         =  []




    def     _a						(						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
       lowerCAmelCase_:       Union[str, 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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
       if torch.is_tensor(lowerCamelCase__   ):
          self.expand_frame(get_abs_min_max(lowerCamelCase__					,	lowerCamelCase__   )   )
          if detect_overflow(lowerCamelCase__					,	lowerCamelCase__   ):
             lowerCAmelCase_:       Dict         =  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   ):
       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   ):
       self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''   )




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




    def     _a						(						self					,	lowerCamelCase__   ):
       module.register_forward_hook(self.forward_hook   )




    def     _a						(						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

       lowerCAmelCase_:       Optional[int]         =  False

       lowerCAmelCase_:       int         =  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_:       Any         =  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 		snake_case__  (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Union[str, Any]         =  var.abs()
   return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''
def 		snake_case__  (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Union[str, Any]         =  False
   if torch.isnan(lowercase				).any().item():
      lowerCAmelCase_:       Optional[int]         =  True
      print(F'''{ctx} has nans'''				)
   if torch.isinf(lowercase				).any().item():
      lowerCAmelCase_:       str         =  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_:       Dict         =  var[torch.ge(var.abs()       ,							1000				)]
      if naaaa.numel() > 0:
         print(F'''{ctx}: n1000={naaaa.numel()}'''				)
      lowerCAmelCase_:       Optional[int]         =  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 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							str        =						'underflow_overflow'
    SCREAMING_SNAKE_CASE:							Tuple        =						'tpu_metrics_debug'
def 		snake_case__  (		lowercase = 998001				):

   # fetches the next number
   for number in range(n - 1       ,							9999       ,							-1				):
      lowerCAmelCase_:       str         =  str(lowercase				)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         lowerCAmelCase_:       List[str]         =  999

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 99:
            if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
               return number
            divisor -= 1
   raise ValueError("That number is larger than our acceptable range."				)


if __name__ == "__main__":
 print(f'''{solution() = }''')