code: 
  
  
  
  
  
  '''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'''
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    {
              """model_details""": {
                  """Organization""": """testing""",
                  """Model date""": """today""",
                  """Model version""": """v2.1, Developed by Test Corp in 2019.""",
                  """Architecture""": """Convolutional Neural Network.""",
              },
              """metrics""": """BLEU and ROUGE-1""",
              """evaluation_data""": {
                  """Datasets""": {"""BLEU""": """My-great-dataset-v1""", """ROUGE-1""": """My-short-dataset-v2.1"""},
                  """Preprocessing""": """See details on https://arxiv.org/pdf/1810.03993.pdf""",
              },
              """training_data""": {
                  """Dataset""": """English Wikipedia dump dated 2018-12-01""",
                  """Preprocessing""": (
                      """Using SentencePiece vocabulary of size 52k tokens. See details on"""
                      """ https://arxiv.org/pdf/1810.03993.pdf"""
                  ),
              },
              """quantitative_analyses""": {"""BLEU""": 55.1, """ROUGE-1""": 76},
          }
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          self.assertTrue(hasattr(A				,	"""model_details""" ) )
          self.assertTrue(hasattr(A				,	"""intended_use""" ) )
          self.assertTrue(hasattr(A				,	"""factors""" ) )
          self.assertTrue(hasattr(A				,	"""metrics""" ) )
          self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
          self.assertTrue(hasattr(A				,	"""training_data""" ) )
          self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
          self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
          self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
          for key, value in self.inputs_dict.items():
              self.assertEqual(obj[key]				,	A )
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	"""modelcard.json""" )
              model_card_first.to_json_file(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_json_file(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              model_card_first.save_pretrained(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_pretrained(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  