




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


_lowerCAmelCase    : Tuple		= logging.get_logger(__name__)
class       _UpperCamelCase   :
      def __init__(      self :Optional[Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Dict=21			,   lowerCamelCase :Optional[int]=[]			,   lowerCamelCase :Any=None   )  ->      Optional[int]:
       UpperCAmelCase__							=    model
       UpperCAmelCase__							=    trace_batch_nums
       UpperCAmelCase__							=    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__							=    collections.deque([]			,   lowerCamelCase   )
       UpperCAmelCase__							=    []
       UpperCAmelCase__							=    0
       UpperCAmelCase__							=    0
       UpperCAmelCase__							=    False
       UpperCAmelCase__							=    "                 "

       self.analyse_model()

       self.register_forward_hook()
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :str=None   )  ->      Optional[Any]:
       if frame is not None:
        self.expand_frame(lowerCamelCase   )
       self.frames.append("\n".join(self.frame   )   )
       UpperCAmelCase__							=    []  # start a new frame
      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :Optional[Any]   )  ->      List[Any]:
       self.frame.append(lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      str:
       print("\n".join(self.frames   )   )
       UpperCAmelCase__							=    []
      def 						UpperCAmelCase_    (      self :Any   )  ->      Optional[int]:
       UpperCAmelCase__							=    []
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Dict:
       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__							=    []
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Any:
       # 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__							=    {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 :Union[str, Any]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Dict   )  ->      List[Any]:
       if torch.is_tensor(lowerCamelCase   ):
        self.expand_frame(get_abs_min_max(lowerCamelCase			,   lowerCamelCase   )   )
        if detect_overflow(lowerCamelCase			,   lowerCamelCase   ):
         UpperCAmelCase__							=    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 :Dict   )  ->      int:
       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 :str   )  ->      str:
       self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :int			,   lowerCamelCase :List[str]   )  ->      Optional[Any]:
       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 						UpperCAmelCase_    (      self :Optional[int]   )  ->      List[str]:
       self.model.apply(self._register_forward_hook   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :int   )  ->      Optional[int]:
       module.register_forward_hook(self.forward_hook   )







      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :int			,   lowerCamelCase :List[str]			,   lowerCamelCase :Optional[Any]   )  ->      Any:
       # - 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__							=    False

       UpperCAmelCase__							=    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__							=    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		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: List[str]						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: str    ,				_lowerCAmelCase				: Any						):




 """simple docstring"""







 UpperCAmelCase__							=    False
 if torch.isnan(_lowerCAmelCase						).any().item():
  UpperCAmelCase__							=    True
  print(F'''{ctx} has nans'''						)
 if torch.isinf(_lowerCAmelCase						).any().item():
  UpperCAmelCase__							=    True
  print(F'''{ctx} has infs'''						)

 # if needed to monitor large elements can enable the following
 if 0:  # and detected:
  UpperCAmelCase__							=    var[torch.ge(var.abs()    ,				100						)]
  if naaa.numel() > 0:
   print(F'''{ctx}:  n100={naaa.numel()}'''						)
  UpperCAmelCase__							=    var[torch.ge(var.abs()    ,				1000						)]
  if naaaa.numel() > 0:
   print(F'''{ctx}: n1000={naaaa.numel()}'''						)
  UpperCAmelCase__							=    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       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		"""underflow_overflow"""
      UpperCAmelCase_          =		"""tpu_metrics_debug"""











from math import cos, sin



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__							=    float(_lowerCAmelCase						)
 return [[scaling_factor * int(x == y						) for x in range(2						)] for y in range(2						)]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 return [[c, -s], [s, c]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[c * c, cs], [cs, s * s]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'''    {scaling(5) = }''')
print(F'''  {rotation(4_5) = }''')
print(F'''{projection(4_5) = }''')


print(F'''{reflection(4_5) = }''')





