





"""simple docstring"""





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
    import torch


_lowerCamelCase       =	logging.get_logger(__name__)





class 			snake_case		:

      def __init__(			self   :Any						,							_lowerCamelCase   :str						,							_lowerCamelCase   :Any=2_1						,							_lowerCamelCase   :Dict=[]						,							_lowerCamelCase   :Tuple=None						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model
       __SCREAMING_SNAKE_CASE :			int												=       trace_batch_nums
       __SCREAMING_SNAKE_CASE :			List[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
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       collections.deque([]						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       []
       __SCREAMING_SNAKE_CASE :			Tuple												=       0
       __SCREAMING_SNAKE_CASE :			List[Any]												=       0
       __SCREAMING_SNAKE_CASE :			Tuple												=       False
       __SCREAMING_SNAKE_CASE :			Tuple												=       '''                 '''

       self.analyse_model()

       self.register_forward_hook()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :str=None						):
       if frame is not None:
        self.expand_frame(_lowerCamelCase						)
       self.frames.append('''\n'''.join(self.frame						)						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       []  # start a new frame

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Tuple						):
       self.frame.append(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       print('''\n'''.join(self.frames						)						)
       __SCREAMING_SNAKE_CASE :			int												=       []

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			int												=       []

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       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'''						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       []

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :str						):
       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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.model.apply(self._register_forward_hook						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						,							_lowerCamelCase   :Tuple						):
       module.register_forward_hook(self.forward_hook						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :str						):
       # - input is a tuple of packed inputs (could be non-Tensors)
       # - output could be a Tensor or a tuple of Tensors and non-Tensors

       __SCREAMING_SNAKE_CASE :			List[str]												=       False

       __SCREAMING_SNAKE_CASE :			Dict												=       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
        __SCREAMING_SNAKE_CASE :			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 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       var.abs()
 return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       False
 if torch.isnan(lowercase_		).any().item():
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
  print(F'''{ctx} has nans'''		)
 if torch.isinf(lowercase_		).any().item():
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
  print(F'''{ctx} has infs'''		)

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







def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=1.0
    for i in range(1,				r + 1	):
        ret *= (n + 1 - i) / i
    return round(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return choose(8 + n,				__UpperCamelCase	) + choose(9 + n,				__UpperCamelCase	) - 10



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return sum(non_bouncy_exact(__UpperCamelCase	) for i in range(1,				n + 1	)	)



def 	UpperCAmelCase_    (	__UpperCamelCase = 100	):
    return non_bouncy_upto(__UpperCamelCase	)


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






