





'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


UpperCAmelCase       :		Any		=				logging.get_logger(__name__)





class lowerCamelCase    :







  def __init__(		self  ,  lowercase__  ,  lowercase__=21  ,  lowercase__=[]  ,  lowercase__=None		)		->    str:






        """simple docstring"""





        _snake_case		:						int         =  model
        _snake_case		:						Any         =  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		:						str         =  collections.deque([]  ,  lowercase__		)
        _snake_case		:						Union[str, Any]         =  []
        _snake_case		:						Optional[int]         =  0
        _snake_case		:						Any         =  0
        _snake_case		:						int         =  False
        _snake_case		:						Dict         =  '''                 '''

        self.analyse_model()

        self.register_forward_hook()







  def 					UpperCAmelCase_					(		self  ,  lowercase__=None		)		->    Optional[int]:






        """simple docstring"""





        if frame is not None:
              self.expand_frame(lowercase__		)
        self.frames.append('''\n'''.join(self.frame		)		)
        _snake_case		:						Optional[int]         =  []  # start a new frame







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    List[str]:






        """simple docstring"""





        self.frame.append(lowercase__		)







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        print('''\n'''.join(self.frames		)		)
        _snake_case		:						int         =  []







  def 					UpperCAmelCase_					(		self		)		->    Tuple:






        """simple docstring"""





        _snake_case		:						Dict         =  []







  def 					UpperCAmelCase_					(		self		)		->    str:






        """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'''		)
        _snake_case		:						int         =  []







  def 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  {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__		)		->    Dict:






        """simple docstring"""





        if torch.is_tensor(lowercase__		):
              self.expand_frame(get_abs_min_max(lowercase__  ,  lowercase__		)		)
              if detect_overflow(lowercase__  ,  lowercase__		):
                    _snake_case		:						Union[str, Any]         =  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		)		->    Optional[Any]:






        """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		)		->    Optional[Any]:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    Optional[int]:






        """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		)		->    Dict:






        """simple docstring"""





        self.model.apply(self._register_forward_hook		)







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    List[str]:






        """simple docstring"""





        module.register_forward_hook(self.forward_hook		)







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    Tuple:






        """simple docstring"""





        _snake_case		:						int         =  False

        _snake_case		:						Optional[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		:						Optional[int]         =  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_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  var.abs()
      return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[Any]         =  False
      if torch.isnan(lowerCAmelCase_						).any().item():
            _snake_case		:						str         =  True
            print(f'''{ctx} has nans'''						)
      if torch.isinf(lowerCAmelCase_						).any().item():
            _snake_case		:						List[Any]         =  True
            print(f'''{ctx} has infs'''						)

      # if needed to monitor large elements can enable the following
      if 0:  # and detected:
            _snake_case		:						Optional[Any]         =  var[torch.ge(var.abs()      ,   100						)]
            if naaa.numel() > 0:
                  print(f'''{ctx}:  n100={naaa.numel()}'''						)
            _snake_case		:						int         =  var[torch.ge(var.abs()      ,   1_000						)]
            if naaaa.numel() > 0:
                  print(f'''{ctx}: n1000={naaaa.numel()}'''						)
            _snake_case		:						str         =  var[torch.ge(var.abs()      ,   10_000						)]
            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 lowerCamelCase    (a__       ):
  _lowercase	:						List[str]							       =					"""underflow_overflow"""
  _lowercase	:						Optional[Any]							       =					"""tpu_metrics_debug"""









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase       :		Optional[int]		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


