






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       ,   _UpperCAmelCase       ,   _UpperCAmelCase=21       ,   _UpperCAmelCase=[]       ,   _UpperCAmelCase=None     )		->							Union[str, Any]:
        lowerCamelCase											=model
        lowerCamelCase											=trace_batch_nums
        lowerCamelCase											=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
        lowerCamelCase											=collections.deque([]       ,   _UpperCAmelCase     )
        lowerCamelCase											=[]
        lowerCamelCase											=0
        lowerCamelCase											=0
        lowerCamelCase											=False
        lowerCamelCase											="""                 """

        self.analyse_model()

        self.register_forward_hook()





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase=None     )		->							str:
        if frame is not None:
           self.expand_frame(_UpperCAmelCase     )
        self.frames.append("""\n""".join(self.frame     )     )
        lowerCamelCase											=[]  # start a new frame





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Optional[Any]:
        self.frame.append(_UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Any:
        print("""\n""".join(self.frames     )     )
        lowerCamelCase											=[]





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=[]





     def 	lowerCamelCase       (  self     )		->							Optional[Any]:
        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"""     )
        lowerCamelCase											=[]





     def 	lowerCamelCase       (  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
        lowerCamelCase											={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 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )		->							Optional[int]:
        if torch.is_tensor(_UpperCAmelCase     ):
           self.expand_frame(get_abs_min_max(_UpperCAmelCase       ,   _UpperCAmelCase     )     )
           if detect_overflow(_UpperCAmelCase       ,   _UpperCAmelCase     ):
              lowerCamelCase											=True
        elif var is None:
           self.expand_frame(F'''{"None":>17} {ctx}'''     )
        else:
           self.expand_frame(F'''{"not a tensor":>17} {ctx}'''     )





     def 	lowerCamelCase       (  self     )		->							str:
        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 	lowerCamelCase       (  self     )		->							List[Any]:
        self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )		->							Tuple:
        self.expand_frame(F'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''     )

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

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

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

        self.save_frame()





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





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Union[str, Any]:
        module.register_forward_hook(self.forward_hook     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )		->							Optional[int]:
        # - input is a tuple of packed inputs (could be non-Tensors)
        # - output could be a Tensor or a tuple of Tensors and non-Tensors

        lowerCamelCase											=False

        lowerCamelCase											=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
           lowerCamelCase											=True

        self.create_frame(_UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase     )

        # 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__	(						lowerCamelCase  : Union[str, Any] ,lowerCamelCase  : List[str]						):
   lowerCamelCase											=var.abs()
   return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''






def    UpperCAmelCase__	(						lowerCamelCase  : Dict ,lowerCamelCase  : List[Any]						):
   lowerCamelCase											=False
   if torch.isnan(lowerCamelCase						).any().item():
      lowerCamelCase											=True
      print(f'''{ctx} has nans'''						)
   if torch.isinf(lowerCamelCase						).any().item():
      lowerCamelCase											=True
      print(f'''{ctx} has infs'''						)

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      lowerCamelCase											=var[torch.ge(var.abs() ,1_00						)]
      if naaa.numel() > 0:
         print(f'''{ctx}:  n100={naaa.numel()}'''						)
      lowerCamelCase											=var[torch.ge(var.abs() ,10_00						)]
      if naaaa.numel() > 0:
         print(f'''{ctx}: n1000={naaaa.numel()}'''						)
      lowerCamelCase											=var[torch.ge(var.abs() ,1_00_00						)]
      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_							(     UpperCamelCase   ):
     __lowerCamelCase					:						Tuple     =    '''underflow_overflow'''
     __lowerCamelCase					:						List[Any]     =    '''tpu_metrics_debug'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase				:	List[str]    						=    {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase				:	Optional[Any]    						=    [
       'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'UniSpeechSatForAudioFrameClassification',
       'UniSpeechSatForCTC',
       'UniSpeechSatForPreTraining',
       'UniSpeechSatForSequenceClassification',
       'UniSpeechSatForXVector',
       'UniSpeechSatModel',
       'UniSpeechSatPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

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