


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXLMForMultipleChoice,
			    TFXLMForQuestionAnsweringSimple,
			    TFXLMForSequenceClassification,
			    TFXLMForTokenClassification,
			    TFXLMModel,
			    TFXLMWithLMHeadModel,
			    XLMConfig,
			)



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      parent
										__magic_name__				:							Dict				=      13
										__magic_name__				:							Any				=      7
										__magic_name__				:							List[str]				=      True
										__magic_name__				:							Union[str, Any]				=      True
										__magic_name__				:							Tuple				=      True
										__magic_name__				:							Tuple				=      True
										__magic_name__				:							List[Any]				=      True
										__magic_name__				:							int				=      False
										__magic_name__				:							List[str]				=      False
										__magic_name__				:							Union[str, Any]				=      False
										__magic_name__				:							Tuple				=      2
										__magic_name__				:							Union[str, Any]				=      99
										__magic_name__				:							Union[str, Any]				=      0
										__magic_name__				:							Tuple				=      32
										__magic_name__				:							Dict				=      2
										__magic_name__				:							List[str]				=      4
										__magic_name__				:							Optional[int]				=      0.1
										__magic_name__				:							Optional[int]				=      0.1
										__magic_name__				:							Union[str, Any]				=      512
										__magic_name__				:							Tuple				=      16
										__magic_name__				:							int				=      2
										__magic_name__				:							List[str]				=      0.02
										__magic_name__				:							Optional[int]				=      3
										__magic_name__				:							List[str]				=      4
										__magic_name__				:							Dict				=      'last'
										__magic_name__				:							List[str]				=      True
										__magic_name__				:							int				=      None
										__magic_name__				:							int				=      0
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size      )
										__magic_name__				:							Tuple				=      random_attention_mask([self.batch_size, self.seq_length]     ,      dtype=tf.floataa      )

										__magic_name__				:							Optional[int]				=      None
										if self.use_input_lengths:
																__magic_name__				:							List[str]				=      (
																    ids_tensor([self.batch_size]     ,      vocab_size=2      ) + self.seq_length - 2
																)  # small variation of seq_length

										__magic_name__				:							Tuple				=      None
										if self.use_token_type_ids:
																__magic_name__				:							int				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.n_langs      )

										__magic_name__				:							Optional[int]				=      None
										__magic_name__				:							Any				=      None
										__magic_name__				:							Tuple				=      None
										if self.use_labels:
																__magic_name__				:							str				=      ids_tensor([self.batch_size]     ,      self.type_sequence_label_size      )
																__magic_name__				:							Optional[int]				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels      )
																__magic_name__				:							Optional[Any]				=      ids_tensor([self.batch_size]     ,      2     ,      dtype=tf.floataa      )
																__magic_name__				:							List[Any]				=      ids_tensor([self.batch_size]     ,      self.num_choices      )

										__magic_name__				:							Any				=      XLMConfig(
										    vocab_size=self.vocab_size     ,      n_special=self.n_special     ,      emb_dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      gelu_activation=self.gelu_activation     ,      sinusoidal_embeddings=self.sinusoidal_embeddings     ,      asm=self.asm     ,      causal=self.causal     ,      n_langs=self.n_langs     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      summary_type=self.summary_type     ,      use_proj=self.use_proj     ,      bos_token_id=self.bos_token_id     ,      )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_lengths,
										    sequence_labels,
										    token_labels,
										    is_impossible_labels,
										    choice_labels,
										    input_mask,
										)
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      TFXLMModel(config=_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
										__magic_name__				:							List[str]				=      model(_UpperCAmelCase      )

										__magic_name__				:							Tuple				=      [input_ids, input_mask]
										__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      TFXLMWithLMHeadModel(_UpperCAmelCase      )

										__magic_name__				:							str				=      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
										__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      outputs

										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							str				=      TFXLMForQuestionAnsweringSimple(_UpperCAmelCase      )

										__magic_name__				:							str				=      {'input_ids': input_ids, 'lengths': input_lengths}

										__magic_name__				:							Tuple				=      model(_UpperCAmelCase      )

										self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      TFXLMForSequenceClassification(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      {'input_ids': input_ids, 'lengths': input_lengths}

										__magic_name__				:							List[Any]				=      model(_UpperCAmelCase      )

										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.type_sequence_label_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.num_labels
										__magic_name__				:							List[str]				=      TFXLMForTokenClassification(config=_UpperCAmelCase      )
										__magic_name__				:							List[Any]				=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
										__magic_name__				:							Union[str, Any]				=      model(_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.num_choices
										__magic_name__				:							Union[str, Any]				=      TFXLMForMultipleChoice(config=_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      tf.tile(tf.expand_dims(_UpperCAmelCase     ,      1      )     ,      (1, self.num_choices, 1)      )
										__magic_name__				:							List[str]				=      tf.tile(tf.expand_dims(_UpperCAmelCase     ,      1      )     ,      (1, self.num_choices, 1)      )
										__magic_name__				:							Tuple				=      tf.tile(tf.expand_dims(_UpperCAmelCase     ,      1      )     ,      (1, self.num_choices, 1)      )
										__magic_name__				:							int				=      {
										    'input_ids': multiple_choice_inputs_ids,
										    'attention_mask': multiple_choice_input_mask,
										    'token_type_ids': multiple_choice_token_type_ids,
										}
										__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, (
										    __magic_name__
										)		, 
										)				:							Optional[int]				=      config_and_inputs
										__magic_name__				:							Union[str, Any]				=      {
										    'input_ids': input_ids,
										    'token_type_ids': token_type_ids,
										    'langs': token_type_ids,
										    'lengths': input_lengths,
										}
										return config, inputs_dict






@require_tf
class     snake_case__  (      _A						,       _A						,       unittest.TestCase  ):
				__lowerCAmelCase		=      (
				    (
				        TFXLMModel,
				        TFXLMWithLMHeadModel,
				        TFXLMForSequenceClassification,
				        TFXLMForQuestionAnsweringSimple,
				        TFXLMForTokenClassification,
				        TFXLMForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__lowerCAmelCase		=      (
				    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__lowerCAmelCase		=      (
				    {
				        '''feature-extraction''': TFXLMModel,
				        '''fill-mask''': TFXLMWithLMHeadModel,
				        '''question-answering''': TFXLMForQuestionAnsweringSimple,
				        '''text-classification''': TFXLMForSequenceClassification,
				        '''text-generation''': TFXLMWithLMHeadModel,
				        '''token-classification''': TFXLMForTokenClassification,
				        '''zero-shot''': TFXLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__lowerCAmelCase		=      False
				__lowerCAmelCase		=      False
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if (
										    pipeline_test_casse_name == "QAPipelineTests"
										    and tokenizer_name is not None
										    and not tokenizer_name.endswith('Fast'      )
										):
																# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																return True

										return False
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      TFXLMModelTester(self      )
										__magic_name__				:							Union[str, Any]				=      ConfigTester(self     ,      config_class=_UpperCAmelCase     ,      emb_dim=37      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.config_tester.run_common_tests()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_model(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_lm_head(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_qa(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_sequence_classif(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_for_token_classification(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_xlm_for_multiple_choice(*_UpperCAmelCase      )





				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__				:							int				=      TFXLMModel.from_pretrained(_UpperCAmelCase      )
																self.assertIsNotNone(_UpperCAmelCase      )






@require_tf
class     snake_case__  (      unittest.TestCase  ):
				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
										__magic_name__				:							Tuple				=      tf.convert_to_tensor([[14, 447]]     ,      dtype=tf.intaa      )  # the president
										__magic_name__				:							Union[str, Any]				=      [
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										    14,
										    447,
										]  # the president the president the president the president the president the president the president the president the president the president
										# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
										__magic_name__				:							str				=      model.generate(_UpperCAmelCase     ,      do_sample=_UpperCAmelCase      )
										self.assertListEqual(output_ids[0].numpy().tolist()     ,      _UpperCAmelCase      )






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


snake_case__     							= logging.get_logger(__name__)



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=21     ,      _UpperCAmelCase=[]     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      model
										__magic_name__				:							List[str]				=      trace_batch_nums
										__magic_name__				:							int				=      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
										__magic_name__				:							Union[str, Any]				=      collections.deque([]     ,      _UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      []
										__magic_name__				:							List[str]				=      0
										__magic_name__				:							Optional[int]				=      0
										__magic_name__				:							Any				=      False
										__magic_name__				:							str				=      '                 '

										self.analyse_model()

										self.register_forward_hook()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if frame is not None:
																self.expand_frame(_UpperCAmelCase      )
										self.frames.append('\n'.join(self.frame      )      )
										__magic_name__				:							List[Any]				=      []  # start a new frame
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										self.frame.append(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										print('\n'.join(self.frames      )      )
										__magic_name__				:							List[str]				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										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'      )
										__magic_name__				:							Any				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      {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     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if torch.is_tensor(_UpperCAmelCase      ):
																self.expand_frame(get_abs_min_max(_UpperCAmelCase     ,      _UpperCAmelCase      )      )
																if detect_overflow(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																						__magic_name__				:							List[str]				=      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      ):




										"""simple docstring"""




										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      ):




										"""simple docstring"""




										self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"      )

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

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

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

										self.save_frame()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.model.apply(self._register_forward_hook      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										module.register_forward_hook(self.forward_hook      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      False

										__magic_name__				:							Any				=      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
																__magic_name__				:							str				=      True

										self.create_frame(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )

										# 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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :Any				)       ->  Any:
						__magic_name__				:							List[Any]				=      var.abs()
						return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Optional[int]				)       ->  Optional[Any]:
						__magic_name__				:							str				=      False
						if torch.isnan(lowercase__				).any().item():
												__magic_name__				:							Optional[Any]				=      True
												print(f"{ctx} has nans"				)
						if torch.isinf(lowercase__				).any().item():
												__magic_name__				:							str				=      True
												print(f"{ctx} has infs"				)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												__magic_name__				:							Any				=      var[torch.ge(var.abs()      ,						1_0_0				)]
												if naaa.numel() > 0:
																		print(f"{ctx}:  n100={naaa.numel()}"				)
												__magic_name__				:							Union[str, Any]				=      var[torch.ge(var.abs()      ,						1_0_0_0				)]
												if naaaa.numel() > 0:
																		print(f"{ctx}: n1000={naaaa.numel()}"				)
												__magic_name__				:							Any				=      var[torch.ge(var.abs()      ,						1_0_0_0_0				)]
												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     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''underflow_overflow'''
				__lowerCAmelCase		=      '''tpu_metrics_debug'''


