






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__		:       str   							=							{
    """auto_factory""": ["""get_values"""],
    """configuration_auto""": ["""ALL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CONFIG_MAPPING""", """MODEL_NAMES_MAPPING""", """AutoConfig"""],
    """feature_extraction_auto""": ["""FEATURE_EXTRACTOR_MAPPING""", """AutoFeatureExtractor"""],
    """image_processing_auto""": ["""IMAGE_PROCESSOR_MAPPING""", """AutoImageProcessor"""],
    """processing_auto""": ["""PROCESSOR_MAPPING""", """AutoProcessor"""],
    """tokenization_auto""": ["""TOKENIZER_MAPPING""", """AutoTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       List[Any]   							=							[
          """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_AUDIO_XVECTOR_MAPPING""",
          """MODEL_FOR_BACKBONE_MAPPING""",
          """MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING""",
          """MODEL_FOR_CAUSAL_LM_MAPPING""",
          """MODEL_FOR_CTC_MAPPING""",
          """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_DEPTH_ESTIMATION_MAPPING""",
          """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING""",
          """MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING""",
          """MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
          """MODEL_FOR_MASKED_LM_MAPPING""",
          """MODEL_FOR_MASK_GENERATION_MAPPING""",
          """MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """MODEL_FOR_OBJECT_DETECTION_MAPPING""",
          """MODEL_FOR_PRETRAINING_MAPPING""",
          """MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
          """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
          """MODEL_FOR_TEXT_ENCODING_MAPPING""",
          """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING""",
          """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING""",
          """MODEL_MAPPING""",
          """MODEL_WITH_LM_HEAD_MAPPING""",
          """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
          """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING""",
          """AutoModel""",
          """AutoBackbone""",
          """AutoModelForAudioClassification""",
          """AutoModelForAudioFrameClassification""",
          """AutoModelForAudioXVector""",
          """AutoModelForCausalLM""",
          """AutoModelForCTC""",
          """AutoModelForDepthEstimation""",
          """AutoModelForImageClassification""",
          """AutoModelForImageSegmentation""",
          """AutoModelForInstanceSegmentation""",
          """AutoModelForMaskGeneration""",
          """AutoModelForTextEncoding""",
          """AutoModelForMaskedImageModeling""",
          """AutoModelForMaskedLM""",
          """AutoModelForMultipleChoice""",
          """AutoModelForNextSentencePrediction""",
          """AutoModelForObjectDetection""",
          """AutoModelForPreTraining""",
          """AutoModelForQuestionAnswering""",
          """AutoModelForSemanticSegmentation""",
          """AutoModelForSeq2SeqLM""",
          """AutoModelForSequenceClassification""",
          """AutoModelForSpeechSeq2Seq""",
          """AutoModelForTableQuestionAnswering""",
          """AutoModelForTokenClassification""",
          """AutoModelForUniversalSegmentation""",
          """AutoModelForVideoClassification""",
          """AutoModelForVision2Seq""",
          """AutoModelForVisualQuestionAnswering""",
          """AutoModelForDocumentQuestionAnswering""",
          """AutoModelWithLMHead""",
          """AutoModelForZeroShotImageClassification""",
          """AutoModelForZeroShotObjectDetection""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       str   							=							[
          """TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_CAUSAL_LM_MAPPING""",
          """TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_MASK_GENERATION_MAPPING""",
          """TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
          """TF_MODEL_FOR_MASKED_LM_MAPPING""",
          """TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """TF_MODEL_FOR_PRETRAINING_MAPPING""",
          """TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
          """TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
          """TF_MODEL_FOR_TEXT_ENCODING_MAPPING""",
          """TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """TF_MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
          """TF_MODEL_MAPPING""",
          """TF_MODEL_WITH_LM_HEAD_MAPPING""",
          """TFAutoModel""",
          """TFAutoModelForAudioClassification""",
          """TFAutoModelForCausalLM""",
          """TFAutoModelForImageClassification""",
          """TFAutoModelForMaskedImageModeling""",
          """TFAutoModelForMaskedLM""",
          """TFAutoModelForMaskGeneration""",
          """TFAutoModelForMultipleChoice""",
          """TFAutoModelForNextSentencePrediction""",
          """TFAutoModelForPreTraining""",
          """TFAutoModelForDocumentQuestionAnswering""",
          """TFAutoModelForQuestionAnswering""",
          """TFAutoModelForSemanticSegmentation""",
          """TFAutoModelForSeq2SeqLM""",
          """TFAutoModelForSequenceClassification""",
          """TFAutoModelForSpeechSeq2Seq""",
          """TFAutoModelForTableQuestionAnswering""",
          """TFAutoModelForTextEncoding""",
          """TFAutoModelForTokenClassification""",
          """TFAutoModelForVision2Seq""",
          """TFAutoModelForZeroShotImageClassification""",
          """TFAutoModelWithLMHead""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							[
          """FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_CAUSAL_LM_MAPPING""",
          """FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_MASKED_LM_MAPPING""",
          """FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
          """FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
          """FLAX_MODEL_FOR_PRETRAINING_MAPPING""",
          """FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
          """FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
          """FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
          """FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
          """FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING""",
          """FLAX_MODEL_MAPPING""",
          """FlaxAutoModel""",
          """FlaxAutoModelForCausalLM""",
          """FlaxAutoModelForImageClassification""",
          """FlaxAutoModelForMaskedLM""",
          """FlaxAutoModelForMultipleChoice""",
          """FlaxAutoModelForNextSentencePrediction""",
          """FlaxAutoModelForPreTraining""",
          """FlaxAutoModelForQuestionAnswering""",
          """FlaxAutoModelForSeq2SeqLM""",
          """FlaxAutoModelForSequenceClassification""",
          """FlaxAutoModelForSpeechSeq2Seq""",
          """FlaxAutoModelForTokenClassification""",
          """FlaxAutoModelForVision2Seq""",
      ]


if TYPE_CHECKING:
      from .auto_factory import get_values
      from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
      from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
      from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
      from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
      from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_auto import (
                MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
                MODEL_FOR_AUDIO_XVECTOR_MAPPING,
                MODEL_FOR_BACKBONE_MAPPING,
                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
                MODEL_FOR_CAUSAL_LM_MAPPING,
                MODEL_FOR_CTC_MAPPING,
                MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
                MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
                MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
                MODEL_FOR_MASK_GENERATION_MAPPING,
                MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                MODEL_FOR_MASKED_LM_MAPPING,
                MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                MODEL_FOR_OBJECT_DETECTION_MAPPING,
                MODEL_FOR_PRETRAINING_MAPPING,
                MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_TEXT_ENCODING_MAPPING,
                MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
                MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
                MODEL_FOR_VISION_2_SEQ_MAPPING,
                MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
                MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
                MODEL_MAPPING,
                MODEL_WITH_LM_HEAD_MAPPING,
                AutoBackbone,
                AutoModel,
                AutoModelForAudioClassification,
                AutoModelForAudioFrameClassification,
                AutoModelForAudioXVector,
                AutoModelForCausalLM,
                AutoModelForCTC,
                AutoModelForDepthEstimation,
                AutoModelForDocumentQuestionAnswering,
                AutoModelForImageClassification,
                AutoModelForImageSegmentation,
                AutoModelForInstanceSegmentation,
                AutoModelForMaskedImageModeling,
                AutoModelForMaskedLM,
                AutoModelForMaskGeneration,
                AutoModelForMultipleChoice,
                AutoModelForNextSentencePrediction,
                AutoModelForObjectDetection,
                AutoModelForPreTraining,
                AutoModelForQuestionAnswering,
                AutoModelForSemanticSegmentation,
                AutoModelForSeqaSeqLM,
                AutoModelForSequenceClassification,
                AutoModelForSpeechSeqaSeq,
                AutoModelForTableQuestionAnswering,
                AutoModelForTextEncoding,
                AutoModelForTokenClassification,
                AutoModelForUniversalSegmentation,
                AutoModelForVideoClassification,
                AutoModelForVisionaSeq,
                AutoModelForVisualQuestionAnswering,
                AutoModelForZeroShotImageClassification,
                AutoModelForZeroShotObjectDetection,
                AutoModelWithLMHead,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_auto import (
                TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_MASK_GENERATION_MAPPING,
                TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                TF_MODEL_FOR_MASKED_LM_MAPPING,
                TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                TF_MODEL_FOR_PRETRAINING_MAPPING,
                TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
                TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                TF_MODEL_MAPPING,
                TF_MODEL_WITH_LM_HEAD_MAPPING,
                TFAutoModel,
                TFAutoModelForAudioClassification,
                TFAutoModelForCausalLM,
                TFAutoModelForDocumentQuestionAnswering,
                TFAutoModelForImageClassification,
                TFAutoModelForMaskedImageModeling,
                TFAutoModelForMaskedLM,
                TFAutoModelForMaskGeneration,
                TFAutoModelForMultipleChoice,
                TFAutoModelForNextSentencePrediction,
                TFAutoModelForPreTraining,
                TFAutoModelForQuestionAnswering,
                TFAutoModelForSemanticSegmentation,
                TFAutoModelForSeqaSeqLM,
                TFAutoModelForSequenceClassification,
                TFAutoModelForSpeechSeqaSeq,
                TFAutoModelForTableQuestionAnswering,
                TFAutoModelForTextEncoding,
                TFAutoModelForTokenClassification,
                TFAutoModelForVisionaSeq,
                TFAutoModelForZeroShotImageClassification,
                TFAutoModelWithLMHead,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_auto import (
                FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_MASKED_LM_MAPPING,
                FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                FLAX_MODEL_FOR_PRETRAINING_MAPPING,
                FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
                FLAX_MODEL_MAPPING,
                FlaxAutoModel,
                FlaxAutoModelForCausalLM,
                FlaxAutoModelForImageClassification,
                FlaxAutoModelForMaskedLM,
                FlaxAutoModelForMultipleChoice,
                FlaxAutoModelForNextSentencePrediction,
                FlaxAutoModelForPreTraining,
                FlaxAutoModelForQuestionAnswering,
                FlaxAutoModelForSeqaSeqLM,
                FlaxAutoModelForSequenceClassification,
                FlaxAutoModelForSpeechSeqaSeq,
                FlaxAutoModelForTokenClassification,
                FlaxAutoModelForVisionaSeq,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE__		:       int   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE__		:       Any   							=							logging.get_logger(__name__)







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
          UpperCAmelCase__			:     Union[str, Any]										= model
          UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
          UpperCAmelCase__			:     Optional[Any]										= 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__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
          UpperCAmelCase__			:     str										= []
          UpperCAmelCase__			:     Optional[int]										= 0
          UpperCAmelCase__			:     int										= 0
          UpperCAmelCase__			:     int										= False
          UpperCAmelCase__			:     List[str]										= """                 """

          self.analyse_model()

          self.register_forward_hook()




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




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          self.frame.append(_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self		):
          print("""\n""".join(self.frames		)		)
          UpperCAmelCase__			:     List[Any]										= []




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[int]										= []




     def      __UpperCAmelCase     (							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__			:     Any										= []




     def      __UpperCAmelCase     (							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__			:     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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          if torch.is_tensor(_lowerCAmelCase		):
               self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
               if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                    UpperCAmelCase__			:     Optional[int]										= 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		):
          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		):
          self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)

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

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

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

          self.save_frame()




     def      __UpperCAmelCase     (							self		):
          self.model.apply(self._register_forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          module.register_forward_hook(self.forward_hook		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          # - 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__			:     Union[str, Any]										= False

          UpperCAmelCase__			:     List[str]										= 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__			:     Dict										= True

          self.create_frame(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)

          # 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   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Dict										= var.abs()
     return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:

     '''simple docstring'''
     UpperCAmelCase__			:     str										= False
     if torch.isnan(__lowerCamelCase					).any().item():
          UpperCAmelCase__			:     List[Any]										= True
          print(F"{ctx} has nans"					)
     if torch.isinf(__lowerCamelCase					).any().item():
          UpperCAmelCase__			:     List[str]										= True
          print(F"{ctx} has infs"					)

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
          if naaa.numel() > 0:
               print(F"{ctx}:  n100={naaa.numel()}"					)
          UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
          if naaaa.numel() > 0:
               print(F"{ctx}: n1000={naaaa.numel()}"					)
          UpperCAmelCase__			:     Optional[int]										= 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 							UpperCAmelCase_     (		__lowerCamelCase		):
     __lowerCamelCase										=     'underflow_overflow'
     __lowerCamelCase										=     'tpu_metrics_debug'






