
"""simple docstring"""





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
    import torch


_a									=				logging.get_logger(__name__)


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=21							,  lowercase_=[]							,  lowercase_=None    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       model
     UpperCAmelCase_   :   Dict     	=       trace_batch_nums
     UpperCAmelCase_   :   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([]							,  lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       []
     UpperCAmelCase_   :   Tuple     	=       0
     UpperCAmelCase_   :   List[Any]     	=       0
     UpperCAmelCase_   :   Any     	=       False
     UpperCAmelCase_   :   str     	=       "                 "

     self.analyse_model()

     self.register_forward_hook()







  def 			UpperCamelCase__   (						self							,  lowercase_=None    ):

     """simple docstring"""
     if frame is not None:
        self.expand_frame(lowercase_    )
     self.frames.append("\n".join(self.frame    )    )
     UpperCAmelCase_   :   Optional[int]     	=       []  # start a new frame







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.frame.append(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     print("\n".join(self.frames    )    )
     UpperCAmelCase_   :   Any     	=       []







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       []







  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_   :   Dict     	=       []







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # 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
     UpperCAmelCase_   :   str     	=       {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							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     if torch.is_tensor(lowercase_    ):
        self.expand_frame(get_abs_min_max(lowercase_							,  lowercase_    )    )
        if detect_overflow(lowercase_							,  lowercase_    ):
           UpperCAmelCase_   :   int     	=       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							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

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

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

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

     self.save_frame()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.model.apply(self._register_forward_hook    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     module.register_forward_hook(self.forward_hook    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     # - input is a tuple of packed inputs (could be non-Tensors)
     # - output could be a Tensor or a tuple of Tensors and non-Tensors

     UpperCAmelCase_   :   Union[str, Any]     	=       False

     UpperCAmelCase_   :   Union[str, 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_   :   str     	=       True

     self.create_frame(lowercase_							,  lowercase_							,  lowercase_    )

     # 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	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       var.abs()
   return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[str]     	=       False
   if torch.isnan(__lowerCamelCase    ).any().item():
      UpperCAmelCase_   :   Optional[int]     	=       True
      print(f"""{ctx} has nans"""    )
   if torch.isinf(__lowerCamelCase    ).any().item():
      UpperCAmelCase_   :   Dict     	=       True
      print(f"""{ctx} has infs"""    )

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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						"""underflow_overflow"""
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						"""tpu_metrics_debug"""










'''simple docstring'''






from math import gcd




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 1_20_00	)     ->			int:







   """simple docstring"""
   __snake_case					:   Dict  				=			0
   for d in range(max_d + 1	):
      for n in range(d // 3 + 1	,		(d + 1) // 2	):
         if gcd(A	,		A	) == 1:
            fractions_number += 1
   return fractions_number


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