
'''simple docstring'''
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


a_           =    logging.get_logger(__name__)






class  UpperCAmelCase_					:





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

     self.analyse_model()

     self.register_forward_hook()





 def 							snake_case__		(     self			,							lowercase_=None):
     if frame is not None:
         self.expand_frame(lowercase_)
     self.frames.append("\n".join(self.frame))
     snake_case_			:     Any        =    []  # start a new frame





 def 							snake_case__		(     self			,							lowercase_):
     self.frame.append(lowercase_)





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





 def 							snake_case__		(     self):
     snake_case_			:     Dict        =    []





 def 							snake_case__		(     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")
     snake_case_			:     Tuple        =    []





 def 							snake_case__		(     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
     snake_case_			:     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 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     if torch.is_tensor(lowercase_):
         self.expand_frame(get_abs_min_max(lowercase_			,							lowercase_))
         if detect_overflow(lowercase_			,							lowercase_):
             snake_case_			:     List[Any]        =    True
     elif var is None:
         self.expand_frame(F'{"None":>17} {ctx}')
     else:
         self.expand_frame(F'{"not a tensor":>17} {ctx}')





 def 							snake_case__		(     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 							snake_case__		(     self):
     self.expand_frame(F'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n')





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     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 							snake_case__		(     self):
     self.model.apply(self._register_forward_hook)





 def 							snake_case__		(     self			,							lowercase_):
     module.register_forward_hook(self.forward_hook)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     # - input is a tuple of packed inputs (could be non-Tensors)
     # - output could be a Tensor or a tuple of Tensors and non-Tensors

     snake_case_			:     Tuple        =    False

     snake_case_			:     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
         snake_case_			:     Tuple        =    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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Tuple        =    var.abs()
    return f'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Union[str, Any]        =    False
    if torch.isnan(__SCREAMING_SNAKE_CASE							).any().item():
        snake_case_			:     Tuple        =    True
        print(f'{ctx} has nans'							)
    if torch.isinf(__SCREAMING_SNAKE_CASE							).any().item():
        snake_case_			:     int        =    True
        print(f'{ctx} has infs'							)

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        snake_case_			:     Any        =    var[torch.ge(var.abs(),		1_0_0							)]
        if naaa.numel() > 0:
            print(f'{ctx}:  n100={naaa.numel()}'							)
        snake_case_			:     List[str]        =    var[torch.ge(var.abs(),		1_0_0_0							)]
        if naaaa.numel() > 0:
            print(f'{ctx}: n1000={naaaa.numel()}'							)
        snake_case_			:     Optional[int]        =    var[torch.ge(var.abs(),		1_0_0_0_0							)]
        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_					(						snake_case__       ):
 UpperCAmelCase_		     =    """underflow_overflow"""
 UpperCAmelCase_		     =    """tpu_metrics_debug"""






'''simple docstring'''
import itertools

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    [1]

    for i in itertools.count(len(__SCREAMING_SNAKE_CASE							)							):
        snake_case_			:     str        =    0
        for j in itertools.count(1							):
            snake_case_			:     List[str]        =    -1 if j % 2 == 0 else +1
            snake_case_			:     str        =    (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(__SCREAMING_SNAKE_CASE							)

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




