



import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
							import torch


UpperCamelCase_      			=  logging.get_logger(__name__)







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :Tuple=21  ,		_lowercase      :Optional[int]=[]  ,		_lowercase      :List[str]=None)						->   List[Any]:
												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([]  ,		a__)
												UpperCAmelCase_					=	[]
												UpperCAmelCase_					=	0
												UpperCAmelCase_					=	0
												UpperCAmelCase_					=	False
												UpperCAmelCase_					=	'''                 '''

												self.analyse_model()

												self.register_forward_hook()

						def       __a			(       self      :Optional[Any]  ,		_lowercase      :Union[str, Any]=None)						->   Dict:
												if frame is not None:
																		self.expand_frame(a__)
												self.frames.append('''\n'''.join(self.frame))
												UpperCAmelCase_					=	[]  # start a new frame

						def       __a			(       self      :int  ,		_lowercase      :Dict)						->   int:
												self.frame.append(a__)

						def       __a			(       self      :List[str])						->   Union[str, Any]:
												print('''\n'''.join(self.frames))
												UpperCAmelCase_					=	[]

						def       __a			(       self      :Optional[int])						->   Dict:
												UpperCAmelCase_					=	[]

						def       __a			(       self      :Any)						->   Optional[int]:
												print(f"\nDetected inf/nan during batch_number={self.batch_number}")
												print(f"Last {len(self.frames)} forward frames:")
												print(f"{'abs min':8} {'abs max':8} metadata")
												print('''\n'''.join(self.frames))
												print('''\n\n''')
												UpperCAmelCase_					=	[]

						def       __a			(       self      :Tuple)						->   Optional[Any]:
												# 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       __a			(       self      :str  ,		_lowercase      :Optional[int]  ,		_lowercase      :Optional[Any])						->   List[Any]:
												if torch.is_tensor(a__):
																		self.expand_frame(get_abs_min_max(a__  ,		a__))
																		if detect_overflow(a__  ,		a__):
																								UpperCAmelCase_					=	True
												elif var is None:
																		self.expand_frame(f"{'None':>17} {ctx}")
												else:
																		self.expand_frame(f"{'not a tensor':>17} {ctx}")

						def       __a			(       self      :List[Any])						->   Tuple:
												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       __a			(       self      :Tuple)						->   Union[str, Any]:
												self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n")

						def       __a			(       self      :Optional[Any]  ,		_lowercase      :Dict  ,		_lowercase      :List[str]  ,		_lowercase      :Dict)						->   Optional[Any]:
												self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}")

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

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

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

												self.save_frame()

						def       __a			(       self      :Optional[int])						->   int:
												self.model.apply(self._register_forward_hook)

						def       __a			(       self      :List[str]  ,		_lowercase      :int)						->   Any:
												module.register_forward_hook(self.forward_hook)




						def       __a			(       self      :List[str]  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :List[Any])						->   Optional[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(a__  ,		a__  ,		a__)

												# if last_frame_of_batch:
												#     self.batch_end_frame()

												if trace_mode:
																		self.trace_frames()

												if last_frame_of_batch:
																		self.batch_start_frame()

												if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.''')

												# abort after certain batch if requested to do so
												if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
																		    f" `abort_after_batch_num={self.abort_after_batch_num}` arg")







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						UpperCAmelCase_					=	var.abs()
						return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_					=	False
						if torch.isnan(__UpperCAmelCase      ).any().item():
												UpperCAmelCase_					=	True
												print(f"{ctx} has nans"      )
						if torch.isinf(__UpperCAmelCase      ).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()				,   100      )]
												if naaa.numel() > 0:
																		print(f"{ctx}:  n100={naaa.numel()}"      )
												UpperCAmelCase_					=	var[torch.ge(var.abs()				,   1000      )]
												if naaaa.numel() > 0:
																		print(f"{ctx}: n1000={naaaa.numel()}"      )
												UpperCAmelCase_					=	var[torch.ge(var.abs()				,   1_0000      )]
												if naaaaa.numel() > 0:
																		print(f"{ctx}: n10000={naaaaa.numel()}"      )

						if 0:
												print(f"min={var.min():9.2e} max={var.max():9.2e}"      )

						if 0:
												print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"      )

						return detected







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		Optional[Any]										="underflow_overflow"
						UpperCamelCase__ :		Tuple										="tpu_metrics_debug"












"""simple docstring"""






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
    print('''#################### AVAILABLE DEVICES ####################''')
    print(jax.devices())
    print('''###########################################################''')

    # setup for wandb sweep
    lowerCAmelCase_      =				Args()
    lowerCAmelCase_      =				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
    lowerCAmelCase_      =				dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase_      =				replace(args, **wandb_args)
    lowerCAmelCase_      =				args.base_dir + '''-''' + wandb.run.id
    lowerCAmelCase_      =				replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

    # drop extra batch for now
    lowerCAmelCase_      =				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase_      =				tr_dataset.shuffle().select(indices)
    lowerCAmelCase_      =				range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase_      =				val_dataset.shuffle().select(indices)

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''lr''': args.lr,
        '''init_lr''': args.init_lr,
        '''warmup_steps''': args.warmup_steps,
        '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
        '''weight_decay''': args.weight_decay,
    }
    lowerCAmelCase_      ,					lowerCAmelCase_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				Trainer(
        args=args,
        data_collator=data_collator,
        model_save_fn=model.save_pretrained,
        train_step_fn=train_step,
        val_step_fn=val_step,
        logger=logger,
        scheduler_fn=lr,
    )

    lowerCAmelCase_      =				None
    lowerCAmelCase_      =				trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
    try:
        trainer.train(state, tr_dataset, val_dataset)
    except KeyboardInterrupt:
        print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

    print('''SAVING WEIGHTS IN `final-weights`''')
    lowerCAmelCase_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






