"""simple docstring"""






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase__  :  Union[str, Any]											=     16
lowerCAmelCase__  :  Union[str, Any]											=     32



def   a_						( lowerCamelCase	):
				return int(x / 2**2_0	)
class     snake_case	:




					"""simple docstring"""

					def __enter__(    self       :     Optional[int]      ):
									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									UpperCAmelCase__					=							torch.cuda.memory_allocated()
									UpperCAmelCase__					=							psutil.Process()

									UpperCAmelCase__					=							self.cpu_mem_used()
									UpperCAmelCase__					=							True
									UpperCAmelCase__					=							threading.Thread(target=self.peak_monitor_func      )
									UpperCAmelCase__					=							True
									peak_monitor_thread.start()
									return self







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return self.process.memory_info().rss







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							-1

									while True:
													UpperCAmelCase__					=							max(self.cpu_mem_used()   ,self.cpu_peak      )

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													# time.sleep(0.001) # 1msec

													if not self.peak_monitoring:
																	break







					def __exit__(    self       :     Optional[int]   ,*lowerCamelCase__       :     Any      ):
									UpperCAmelCase__					=							False

									gc.collect()
									torch.cuda.empty_cache()
									UpperCAmelCase__					=							torch.cuda.memory_allocated()
									UpperCAmelCase__					=							torch.cuda.max_memory_allocated()
									UpperCAmelCase__					=							bamb(self.end - self.begin      )
									UpperCAmelCase__					=							bamb(self.peak - self.begin      )

									UpperCAmelCase__					=							self.cpu_mem_used()
									UpperCAmelCase__					=							bamb(self.cpu_end - self.cpu_begin      )
									UpperCAmelCase__					=							bamb(self.cpu_peak - self.cpu_begin      )
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						lowerCAmelCase__  :  Any											=     mocked_dataloaders  # noqa: F811



def   a_						( lowerCamelCase ,      lowerCamelCase	):

				if os.environ.get('TESTING_MOCKED_DATALOADERS' ,      lowercase_	) == "1":
								UpperCAmelCase__					=							2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				UpperCAmelCase__					=							FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_ ,      ranka_only=lowercase_	) ,      optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_ ,      ranka_only=lowercase_	) ,      )

				# Initialize accelerator
				if args.with_tracking:
								UpperCAmelCase__					=							Accelerator(
								    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with='wandb' ,      project_dir=args.logging_dir ,      fsdp_plugin=lowercase_ ,      )
				else:
								UpperCAmelCase__					=							Accelerator(fsdp_plugin=lowercase_	)
				accelerator.print(accelerator.distributed_type	)

				if hasattr(args.checkpointing_steps ,      'isdigit'	):
								if args.checkpointing_steps == "epoch":
												UpperCAmelCase__					=							args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												UpperCAmelCase__					=							int(args.checkpointing_steps	)
								else:
												raise ValueError(
												    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
				else:
								UpperCAmelCase__					=							None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCAmelCase__					=							config['lr']
				UpperCAmelCase__					=							int(config['num_epochs']	)
				UpperCAmelCase__					=							int(config['seed']	)
				UpperCAmelCase__					=							int(config['batch_size']	)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								UpperCAmelCase__					=							vars(lowercase_	)
								accelerator.init_trackers('fsdp_glue_no_trainer' ,      lowercase_	)

				UpperCAmelCase__					=							AutoTokenizer.from_pretrained(args.model_name_or_path	)
				UpperCAmelCase__					=							load_dataset('glue' ,      'mrpc'	)
				UpperCAmelCase__					=							evaluate.load('glue' ,      'mrpc'	)

				def tokenize_function(lowerCamelCase	):
								# max_length=None => use the model max length (it's actually the default)
								UpperCAmelCase__					=							tokenizer(examples['sentence1'] ,      examples['sentence2'] ,      truncation=lowercase_ ,      max_length=lowercase_	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								UpperCAmelCase__					=							datasets.map(
								    lowercase_ ,      batched=lowercase_ ,      remove_columns=['idx', 'sentence1', 'sentence2'] ,      )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCAmelCase__					=							tokenized_datasets.rename_column('label' ,      'labels'	)

				# If the batch size is too big we use gradient accumulation
				UpperCAmelCase__					=							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								UpperCAmelCase__					=							batch_size // MAX_GPU_BATCH_SIZE
								UpperCAmelCase__					=							MAX_GPU_BATCH_SIZE

				def collate_fn(lowerCamelCase	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								UpperCAmelCase__					=							1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												UpperCAmelCase__					=							1_6
								elif accelerator.mixed_precision != "no":
												UpperCAmelCase__					=							8
								else:
												UpperCAmelCase__					=							None

								return tokenizer.pad(
								    lowercase_ ,      padding='longest' ,      max_length=lowercase_ ,      pad_to_multiple_of=lowercase_ ,      return_tensors='pt' ,      )

				# Instantiate dataloaders.
				UpperCAmelCase__					=							DataLoader(
				    tokenized_datasets['train'] ,      shuffle=lowercase_ ,      collate_fn=lowercase_ ,      batch_size=lowercase_	)
				UpperCAmelCase__					=							DataLoader(
				    tokenized_datasets['validation'] ,      shuffle=lowercase_ ,      collate_fn=lowercase_ ,      batch_size=lowercase_	)

				set_seed(lowercase_	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCAmelCase__					=							AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path ,      return_dict=lowercase_ ,      low_cpu_mem_usage=lowercase_	)

				# New Code #
				# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
				UpperCAmelCase__					=							accelerator.prepare(lowercase_	)
				accelerator.print(lowercase_	)

				# Instantiate optimizer
				# New Code #
				# For FSDP feature, at present it doesn't support multiple parameter groups,
				# so we need to create a single parameter group for the whole model
				UpperCAmelCase__					=							torch.optim.AdamW(params=model.parameters() ,      lr=lowercase_ ,      weight_decay=2e-4	)

				# Instantiate scheduler
				UpperCAmelCase__					=							get_linear_schedule_with_warmup(
				    optimizer=lowercase_ ,      num_warmup_steps=1_0 ,      num_training_steps=(len(lowercase_	) * num_epochs) // gradient_accumulation_steps ,      )

				# New Code #
				# For FSDP feature, prepare everything except the model as we have already prepared the model
				# before creating the optimizer
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							accelerator.prepare(
				    lowercase_ ,      lowercase_ ,      lowercase_ ,      lowercase_	)

				UpperCAmelCase__					=							0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
												accelerator.load_state(args.resume_from_checkpoint	)
												UpperCAmelCase__					=							os.path.basename(args.resume_from_checkpoint	)
								else:
												# Get the most recent checkpoint
												UpperCAmelCase__					=							[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
												dirs.sort(key=os.path.getctime	)
												UpperCAmelCase__					=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								UpperCAmelCase__					=							os.path.splitext(lowercase_	)[0]

								if "epoch" in training_difference:
												num_epochs -= int(training_difference.replace('epoch_' ,      ''	)	)
												UpperCAmelCase__					=							None
								else:
												UpperCAmelCase__					=							int(training_difference.replace('step_' ,      ''	)	)
												num_epochs -= resume_step // len(lowercase_	)
												# If resuming by step, we also need to know exactly how far into the DataLoader we went
												UpperCAmelCase__					=							(num_epochs * len(lowercase_	)) - resume_step

												 # Now we train the model
				for epoch in range(lowercase_	):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																UpperCAmelCase__					=							0
												for step, batch in enumerate(lowercase_	):
																# We need to skip steps until we reach the resumed step
																if args.resume_from_checkpoint and epoch == 0:
																				if resume_step is not None and step < resume_step:
																								pass
																								          # We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																UpperCAmelCase__					=							model(**lowercase_	)
																UpperCAmelCase__					=							outputs.loss
																UpperCAmelCase__					=							loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(lowercase_	)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# accelerator.print(lr_scheduler.get_lr())

																overall_step += 1

																if isinstance(lowercase_ ,      lowercase_	):
																				UpperCAmelCase__					=							f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												UpperCAmelCase__					=							os.path.join(args.output_dir ,      lowercase_	)
																								accelerator.save_state(lowercase_	)
																								  # New Code #
																								  # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin	)	)	)
								accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used	)	)
								accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked	)	)
								accelerator.print(
								    'Total Peak Memory consumed during the train (max): {}'.format(
								        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin	),
												    } ,      step=lowercase_ ,      )

								# New Code #
								# context manager to track the peak memory usage during the evaluation
								with TorchTracemalloc() as tracemalloc:
												model.eval()
												for step, batch in enumerate(lowercase_	):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																with torch.no_grad():
																				UpperCAmelCase__					=							model(**lowercase_	)
																UpperCAmelCase__					=							outputs.logits.argmax(dim=-1	)
																UpperCAmelCase__				,     UpperCAmelCase__					=							accelerator.gather_for_metrics((predictions, batch['labels'])	)
																metric.add_batch(
																    predictions=lowercase_ ,      references=lowercase_ ,      )

												UpperCAmelCase__					=							metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,      lowercase_	)
												if args.with_tracking:
																accelerator.log(
																    {
																        'accuracy': eval_metric['accuracy'],
																        'f1': eval_metric['f1'],
																        'train_loss': total_loss.item() / len(lowercase_	),
																    } ,      step=lowercase_ ,      )

												if checkpointing_steps == "epoch":
																UpperCAmelCase__					=							f'''epoch_{epoch}'''
																if args.output_dir is not None:
																				UpperCAmelCase__					=							os.path.join(args.output_dir ,      lowercase_	)
																accelerator.save_state(lowercase_	)
																    # New Code #
																    # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin	)	)	)
								accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used	)	)
								accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked	)	)
								accelerator.print(
								    'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin	),
												    } ,      step=lowercase_ ,      )

				if args.with_tracking:
								accelerator.end_training()



def   a_						( ):
				UpperCAmelCase__					=							argparse.ArgumentParser(description='Simple example of training script.'	)
				parser.add_argument(
				    '--mixed_precision' ,      type=lowercase_ ,      default=lowercase_ ,      choices=['no', 'fp16', 'bf16', 'fp8'] ,      help='Whether to use mixed precision. Choose'
				    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
				    'and an Nvidia Ampere GPU.' ,      )
				parser.add_argument('--cpu' ,      action='store_true' ,      help='If passed, will train on the CPU.'	)
				parser.add_argument(
				    '--checkpointing_steps' ,      type=lowercase_ ,      default=lowercase_ ,      help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,      )
				parser.add_argument(
				    '--resume_from_checkpoint' ,      type=lowercase_ ,      default=lowercase_ ,      help='If the training should continue from a checkpoint folder.' ,      )
				parser.add_argument(
				    '--with_tracking' ,      action='store_true' ,      help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,      )
				parser.add_argument(
				    '--output_dir' ,      type=lowercase_ ,      default='.' ,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,      )
				parser.add_argument(
				    '--logging_dir' ,      type=lowercase_ ,      default='logs' ,      help='Location on where to store experiment tracking logs`' ,      )
				parser.add_argument(
				    '--model_name_or_path' ,      type=lowercase_ ,      help='Path to pretrained model or model identifier from huggingface.co/models.' ,      required=lowercase_ ,      )
				UpperCAmelCase__					=							parser.parse_args()
				UpperCAmelCase__					=							{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
				training_function(lowercase_ ,      lowercase_	)


if __name__ == "__main__":
						main()



"""simple docstring"""






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCAmelCase__  :  Optional[Any]											=     logging.get_logger(__name__)
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Optional[Any]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Optional[Any]=21   ,lowerCamelCase__       :     str=[]   ,lowerCamelCase__       :     Any=None      ):
									UpperCAmelCase__					=							model
									UpperCAmelCase__					=							trace_batch_nums
									UpperCAmelCase__					=							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__					=							collections.deque([]   ,lowerCamelCase__      )
									UpperCAmelCase__					=							[]
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							False
									UpperCAmelCase__					=							'                 '

									self.analyse_model()

									self.register_forward_hook()







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Tuple=None      ):
									if frame is not None:
													self.expand_frame(lowerCamelCase__      )
									self.frames.append('\n'.join(self.frame      )      )
									UpperCAmelCase__					=							[]  # start a new frame







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     int      ):
									self.frame.append(lowerCamelCase__      )







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







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									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__					=							[]







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# 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__					=							{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 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     List[Any]      ):
									if torch.is_tensor(lowerCamelCase__      ):
													self.expand_frame(get_abs_min_max(lowerCamelCase__   ,lowerCamelCase__      )      )
													if detect_overflow(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							True
									elif var is None:
													self.expand_frame(f'''{"None":>17} {ctx}'''      )
									else:
													self.expand_frame(f'''{"not a tensor":>17} {ctx}'''      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									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 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''      )







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Optional[Any]      ):
									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 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									self.model.apply(self._register_forward_hook      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     str      ):
									module.register_forward_hook(self.forward_hook      )







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Union[str, Any]      ):
									# - 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__					=							False

									UpperCAmelCase__					=							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__					=							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   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							var.abs()
				return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							False
				if torch.isnan(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has nans'''	)
				if torch.isinf(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has infs'''	)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0	)]
								if naaa.numel() > 0:
												print(f'''{ctx}:  n100={naaa.numel()}'''	)
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0_0	)]
								if naaaa.numel() > 0:
												print(f'''{ctx}: n1000={naaaa.numel()}'''	)
								UpperCAmelCase__					=							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	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"underflow_overflow"
					snake_case__       =			"tpu_metrics_debug"



