






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		)














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'






