


'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


a__  : Union[str, Any]				=	logging.get_logger(__name__)
class  lowercase_					:


    def __init__(					self  , a  , a=21  , a=[]  , a=None   ):
          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([]  , a   )
          UpperCamelCase__  =    []
          UpperCamelCase__  =    0
          UpperCamelCase__  =    0
          UpperCamelCase__  =    False
          UpperCamelCase__  =    "                 "

          self.analyse_model()

          self.register_forward_hook()


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


    def        __a							(					self  , a   ):
          self.frame.append(a   )


    def        __a							(					self   ):
          print("\n".join(self.frames   )   )
          UpperCamelCase__  =    []


    def        __a							(					self   ):
          UpperCamelCase__  =    []


    def        __a							(					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"   )
          UpperCamelCase__  =    []


    def        __a							(					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
          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        __a							(					self  , a  , a   ):
          if torch.is_tensor(a   ):
                self.expand_frame(get_abs_min_max(a  , a   )   )
                if detect_overflow(a  , a   ):
                      UpperCamelCase__  =    True
          elif var is None:
                self.expand_frame(f'''{'None':>17} {ctx}'''   )
          else:
                self.expand_frame(f'''{'not a tensor':>17} {ctx}'''   )


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


    def        __a							(					self  , a  , a  , a   ):
          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        __a							(					self   ):
          self.model.apply(self._register_forward_hook   )


    def        __a							(					self  , a   ):
          module.register_forward_hook(self.forward_hook   )


    def        __a							(					self  , a  , a  , a   ):
          # - 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(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 _UpperCamelCase	(					__A				,      __A   )							->		Optional[Any]:


      '''simple docstring'''



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







def _UpperCamelCase	(					__A				,      __A   )							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    False
      if torch.isnan(__A   ).any().item():
            UpperCamelCase__  =    True
            print(F'''{ctx} has nans'''   )
      if torch.isinf(__A   ).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()				,      10000   )]
            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  lowercase_					(   a__       ):
    __UpperCAmelCase             =     'underflow_overflow'
    __UpperCAmelCase             =     'tpu_metrics_debug'









'''simple docstring'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__  : Any				=	{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[Any]				=	['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

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





