





'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


lowercase_       =				logging.get_logger(__name__)


class       a_  :

    '''simple docstring'''


    def __init__(	self				,	A				,	A=21				,	A=[]				,	A=None )	->     Any:
        _SCREAMING_SNAKE_CASE             =    model
        _SCREAMING_SNAKE_CASE             =    trace_batch_nums
        _SCREAMING_SNAKE_CASE             =    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             =    collections.deque([]				,	A )
        _SCREAMING_SNAKE_CASE             =    []
        _SCREAMING_SNAKE_CASE             =    0
        _SCREAMING_SNAKE_CASE             =    0
        _SCREAMING_SNAKE_CASE             =    False
        _SCREAMING_SNAKE_CASE             =    """                 """

        self.analyse_model()

        self.register_forward_hook()


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


    def     snake_case_(	self				,	A )	->     Union[str, Any]:
        self.frame.append(A )


    def     snake_case_(	self )	->     List[str]:
        print("""\n""".join(self.frames ) )
        _SCREAMING_SNAKE_CASE             =    []


    def     snake_case_(	self )	->     str:
        _SCREAMING_SNAKE_CASE             =    []


    def     snake_case_(	self )	->     int:
        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             =    []


    def     snake_case_(	self )	->     str:
        # 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             =    {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				,	A				,	A )	->     Optional[int]:
        if torch.is_tensor(A ):
            self.expand_frame(get_abs_min_max(A				,	A ) )
            if detect_overflow(A				,	A ):
                _SCREAMING_SNAKE_CASE             =    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 )	->     Optional[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     snake_case_(	self )	->     Optional[int]:
        self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n' )


    def     snake_case_(	self				,	A				,	A				,	A )	->     List[Any]:
        self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}' )

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

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

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

        self.save_frame()


    def     snake_case_(	self )	->     int:
        self.model.apply(self._register_forward_hook )


    def     snake_case_(	self				,	A )	->     str:
        module.register_forward_hook(self.forward_hook )





    def     snake_case_(	self				,	A				,	A				,	A )	->     Tuple:
        # - 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             =    False

        _SCREAMING_SNAKE_CASE             =    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             =    True

        self.create_frame(A				,	A				,	A )

        # 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				:					Optional[int]      , __lowerCamelCase				:					Dict					)		->List[Any]:
    _SCREAMING_SNAKE_CASE             =    var.abs()
    return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'





def lowerCamelCase		(  __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Optional[Any]					)		->List[str]:
    _SCREAMING_SNAKE_CASE             =    False
    if torch.isnan(__lowerCamelCase					).any().item():
        _SCREAMING_SNAKE_CASE             =    True
        print(F'{ctx} has nans'					)
    if torch.isinf(__lowerCamelCase					).any().item():
        _SCREAMING_SNAKE_CASE             =    True
        print(F'{ctx} has infs'					)

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

    '''simple docstring'''
    UpperCamelCase									=				'''underflow_overflow'''
    UpperCamelCase									=				'''tpu_metrics_debug'''










'''simple docstring'''

import safs


class       a_  (       safs.SaFileSystem ):

    '''simple docstring'''



