import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
      import torch


UpperCAmelCase_    		=				logging.get_logger(__name__)





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=21					,   lowerCAmelCase__=[]					,   lowerCAmelCase__=None							):
            _A=						model
            _A=						trace_batch_nums
            _A=						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
            _A=						collections.deque([]					,   lowerCAmelCase__							)
            _A=						[]
            _A=						0
            _A=						0
            _A=						False
            _A=						'                 '

            self.analyse_model()

            self.register_forward_hook()



     def       a__	(       self					,   lowerCAmelCase__=None							):
            if frame is not None:
                   self.expand_frame(lowerCAmelCase__							)
            self.frames.append('\n'.join(self.frame							)							)
            _A=						[]  # start a new frame



     def       a__	(       self					,   lowerCAmelCase__							):
            self.frame.append(lowerCAmelCase__							)



     def       a__	(       self							):
            print('\n'.join(self.frames							)							)
            _A=						[]



     def       a__	(       self							):
            _A=						[]



     def       a__	(       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'							)
            _A=						[]



     def       a__	(       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
            _A=						{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					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            if torch.is_tensor(lowerCAmelCase__							):
                   self.expand_frame(get_abs_min_max(lowerCAmelCase__					,   lowerCAmelCase__							)							)
                   if detect_overflow(lowerCAmelCase__					,   lowerCAmelCase__							):
                          _A=						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							):
            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							):
            self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"							)



     def       a__	(       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       a__	(       self							):
            self.model.apply(self._register_forward_hook							)



     def       a__	(       self					,   lowerCAmelCase__							):
            module.register_forward_hook(self.forward_hook							)





     def       a__	(       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

            _A=						False

            _A=						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
                   _A=						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 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    int:




       '''simple docstring'''






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

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    str:




       '''simple docstring'''






       _A=						False
       if torch.isnan(lowerCAmelCase_ ).any().item():
              _A=						True
              print(F"{ctx} has nans" )
       if torch.isinf(lowerCAmelCase_ ).any().item():
              _A=						True
              print(F"{ctx} has infs" )

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              _A=						var[torch.ge(var.abs()							,       1_00 )]
              if naaa.numel() > 0:
                     print(F"{ctx}:  n100={naaa.numel()}" )
              _A=						var[torch.ge(var.abs()							,       10_00 )]
              if naaaa.numel() > 0:
                     print(F"{ctx}: n1000={naaaa.numel()}" )
              _A=						var[torch.ge(var.abs()							,       1_00_00 )]
              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   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  Any		="""underflow_overflow"""
     _SCREAMING_SNAKE_CASE  :  Optional[int]		="""tpu_metrics_debug"""
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
      UpperCAmelCase_    		=				Args()
      UpperCAmelCase_    		=				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
      UpperCAmelCase_    		=				dict(logger.config)
      del wandb_args["batch_size"]
      UpperCAmelCase_    		=				replace(args, **wandb_args)
      UpperCAmelCase_    		=				args.base_dir + '''-''' + wandb.run.id
      UpperCAmelCase_    		=				replace(args, base_dir=base_dir)
      print(args)

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

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

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

      print(tr_dataset)
      print(val_dataset)

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

      UpperCAmelCase_    		=				{
          '''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,
      }
      UpperCAmelCase_		,     UpperCAmelCase_    		=				build_tx(**tx_args)

      UpperCAmelCase_    		=				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,
      )

      UpperCAmelCase_    		=				None
      UpperCAmelCase_    		=				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`''')
      UpperCAmelCase_    		=				jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)