code: 
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  SCREAMING_SNAKE_CASE__		:       int   							=							logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__		:       List[str]   							=							{
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'mbart'
       __lowerCamelCase										=     ['past_key_values']
       __lowerCamelCase										=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase=50265  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase="gelu"  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=0.0_2  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=False  ,	_lowerCAmelCase=1  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=2  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     int										= vocab_size
            UpperCAmelCase__			:     str										= max_position_embeddings
            UpperCAmelCase__			:     Any										= d_model
            UpperCAmelCase__			:     int										= encoder_ffn_dim
            UpperCAmelCase__			:     Dict										= encoder_layers
            UpperCAmelCase__			:     Optional[Any]										= encoder_attention_heads
            UpperCAmelCase__			:     Union[str, Any]										= decoder_ffn_dim
            UpperCAmelCase__			:     Dict										= decoder_layers
            UpperCAmelCase__			:     Any										= decoder_attention_heads
            UpperCAmelCase__			:     Optional[Any]										= dropout
            UpperCAmelCase__			:     Optional[Any]										= attention_dropout
            UpperCAmelCase__			:     Optional[Any]										= activation_dropout
            UpperCAmelCase__			:     str										= activation_function
            UpperCAmelCase__			:     Tuple										= init_std
            UpperCAmelCase__			:     List[Any]										= encoder_layerdrop
            UpperCAmelCase__			:     int										= decoder_layerdrop
            UpperCAmelCase__			:     Optional[Any]										= classifier_dropout
            UpperCAmelCase__			:     Optional[int]										= use_cache
            UpperCAmelCase__			:     int										= encoder_layers
            UpperCAmelCase__			:     str										= scale_embedding  # scale factor will be sqrt(d_model) if True
            super().__init__(
                pad_token_id=_lowerCAmelCase  ,	bos_token_id=_lowerCAmelCase  ,	eos_token_id=_lowerCAmelCase  ,	is_encoder_decoder=_lowerCAmelCase  ,	forced_eos_token_id=_lowerCAmelCase  ,	**_lowerCAmelCase  ,	)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            if self.task in ["default", "seq2seq-lm"]:
                 UpperCAmelCase__			:     Union[str, Any]										= OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]		)
  
                 if self.use_past:
                      UpperCAmelCase__			:     Optional[Any]										= {0: """batch"""}
                      UpperCAmelCase__			:     int										= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
                 else:
                      UpperCAmelCase__			:     Optional[int]										= {0: """batch""", 1: """decoder_sequence"""}
                      UpperCAmelCase__			:     Union[str, Any]										= {0: """batch""", 1: """decoder_sequence"""}
  
                 if self.use_past:
                      self.fill_with_past_key_values_(_lowerCAmelCase  ,	direction="""inputs"""		)
            elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 UpperCAmelCase__			:     Dict										= OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]		)
                 if self.use_past:
                      UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.num_layers
                      for i in range(_lowerCAmelCase		):
                           UpperCAmelCase__			:     Dict										= {0: """batch""", 2: """past_sequence + sequence"""}
                           UpperCAmelCase__			:     List[Any]										= {0: """batch""", 2: """past_sequence + sequence"""}
            else:
                 UpperCAmelCase__			:     List[Any]										= OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                         ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                     ]		)
  
            return common_inputs
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            if self.task in ["default", "seq2seq-lm"]:
                 UpperCAmelCase__			:     Optional[int]										= super().outputs
            else:
                 UpperCAmelCase__			:     List[str]										= super(_lowerCAmelCase  ,	self		).outputs
                 if self.use_past:
                      UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= self.num_layers
                      for i in range(_lowerCAmelCase		):
                           UpperCAmelCase__			:     List[str]										= {0: """batch""", 2: """past_sequence + sequence"""}
                           UpperCAmelCase__			:     List[Any]										= {0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
            UpperCAmelCase__			:     List[str]										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # Generate decoder inputs
            UpperCAmelCase__			:     List[str]										= seq_length if not self.use_past else 1
            UpperCAmelCase__			:     str										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
            UpperCAmelCase__			:     List[Any]										= dict(**_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
            if self.use_past:
                 if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
                 else:
                      import torch
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[Any]										= common_inputs["""input_ids"""].shape
                 UpperCAmelCase__			:     Union[str, Any]										= common_inputs["""decoder_input_ids"""].shape[1]
                 UpperCAmelCase__			,		UpperCAmelCase__			:     List[Any]										= self.num_attention_heads
                 UpperCAmelCase__			:     List[str]										= (
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 UpperCAmelCase__			:     str										= decoder_seq_length + 3
                 UpperCAmelCase__			:     Union[str, Any]										= (
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )
  
                 UpperCAmelCase__			:     Tuple										= torch.cat(
                     [common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCAmelCase  ,	_lowerCAmelCase		)]  ,	dim=1		)
  
                 UpperCAmelCase__			:     List[Any]										= []
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Optional[int]										= self.num_layers
                 UpperCAmelCase__			:     str										= min(_lowerCAmelCase  ,	_lowerCAmelCase		)
                 UpperCAmelCase__			:     Optional[Any]										= max(_lowerCAmelCase  ,	_lowerCAmelCase		) - min_num_layers
                 UpperCAmelCase__			:     Tuple										= """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
  
                 for _ in range(_lowerCAmelCase		):
                      common_inputs["past_key_values"].append(
                          (
                              torch.zeros(_lowerCAmelCase		),
                              torch.zeros(_lowerCAmelCase		),
                              torch.zeros(_lowerCAmelCase		),
                              torch.zeros(_lowerCAmelCase		),
                          )		)
                 # TODO: test this.
                 UpperCAmelCase__			:     List[Any]										= encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                 for _ in range(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		))		)
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
            UpperCAmelCase__			:     Tuple										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            if self.use_past:
                 if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""		)
                 else:
                      import torch
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 UpperCAmelCase__			:     Tuple										= seqlen + 2
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Union[str, Any]										= self.num_layers
                 UpperCAmelCase__			,		UpperCAmelCase__			:     str										= self.num_attention_heads
                 UpperCAmelCase__			:     Dict										= (
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
  
                 UpperCAmelCase__			:     Tuple										= common_inputs["""attention_mask"""].dtype
                 UpperCAmelCase__			:     List[str]										= torch.cat(
                     [common_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase  ,	_lowerCAmelCase  ,	dtype=_lowerCAmelCase		)]  ,	dim=1		)
                 UpperCAmelCase__			:     Union[str, Any]										= [
                     (torch.zeros(_lowerCAmelCase		), torch.zeros(_lowerCAmelCase		)) for _ in range(_lowerCAmelCase		)
                 ]
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
            # Copied from OnnxConfig.generate_dummy_inputs
            # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            UpperCAmelCase__			:     Any										= compute_effective_axis_dimension(
                _lowerCAmelCase  ,	fixed_dimension=OnnxConfig.default_fixed_batch  ,	num_token_to_add=0		)
  
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            UpperCAmelCase__			:     List[Any]										= tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[int]										= compute_effective_axis_dimension(
                _lowerCAmelCase  ,	fixed_dimension=OnnxConfig.default_fixed_sequence  ,	num_token_to_add=_lowerCAmelCase		)
  
            # Generate dummy inputs according to compute batch and sequence
            UpperCAmelCase__			:     Union[str, Any]										= [""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
            UpperCAmelCase__			:     Union[str, Any]										= dict(tokenizer(_lowerCAmelCase  ,	return_tensors=_lowerCAmelCase		)		)
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
            if self.task in ["default", "seq2seq-lm"]:
                 UpperCAmelCase__			:     Tuple										= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     _lowerCAmelCase  ,	batch_size=_lowerCAmelCase  ,	seq_length=_lowerCAmelCase  ,	is_pair=_lowerCAmelCase  ,	framework=_lowerCAmelCase		)
  
            elif self.task == "causal-lm":
                 UpperCAmelCase__			:     Union[str, Any]										= self._generate_dummy_inputs_for_causal_lm(
                     _lowerCAmelCase  ,	batch_size=_lowerCAmelCase  ,	seq_length=_lowerCAmelCase  ,	is_pair=_lowerCAmelCase  ,	framework=_lowerCAmelCase		)
            else:
                 UpperCAmelCase__			:     Optional[int]										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     _lowerCAmelCase  ,	batch_size=_lowerCAmelCase  ,	seq_length=_lowerCAmelCase  ,	is_pair=_lowerCAmelCase  ,	framework=_lowerCAmelCase		)
  
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if self.task in ["default", "seq2seq-lm"]:
                 UpperCAmelCase__			:     Optional[int]										= super()._flatten_past_key_values_(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
            else:
                 UpperCAmelCase__			:     int										= super(_lowerCAmelCase  ,	self		)._flatten_past_key_values_(
                     _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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'
  
  
  
  
  
  
  