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

			_A       =					load_dataset("json", data_files=args.tr_data_path)["train"]
			_A       =					load_dataset("json", data_files=args.val_data_path)["train"]

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

			if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
						_A       =					tr_dataset.shuffle().select(range(8_00_00))
						_A       =					val_dataset.shuffle().select(range(80_00))

			print(tr_dataset)
			print(val_dataset)

			_A       =					FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			_A       =					BigBirdTokenizerFast.from_pretrained(args.model_id)
			_A       =					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

			_A       =					{
			    "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,
			}
			_A,				_A       =					build_tx(**tx_args)

			_A       =					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,
			)

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





class      _lowerCAmelCase :




				def __init__( self					)    ->				None:
							# numbermap keys are the number and its values are lists of indexes sorted
							# in ascending order
							lowerCAmelCase_      =     {}
							# indexmap keys are an index and it's values are the number at that index
							lowerCAmelCase_      =     {}




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_delete() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == item:
													array.pop(_UpperCamelCase					)
													return array
										elif array[mid] < item:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1
							raise ValueError(
							    "Either the item is not in the array or the array was unsorted"					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				list[int]:
							if isinstance(_UpperCamelCase    ,    (range, str)					):
										lowerCAmelCase_      =     list(_UpperCamelCase					)
							elif not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
										raise TypeError(
										    "binary_search_insert() only accepts either a list, range or str"					)

							lowerCAmelCase_      =     0
							lowerCAmelCase_      =     len(_UpperCamelCase					) - 1

							while low <= high:
										lowerCAmelCase_      =     (low + high) // 2
										if array[mid] == index:
													# If the item already exists in the array,
													# insert it after the existing item
													array.insert(mid + 1    ,    _UpperCamelCase					)
													return array
										elif array[mid] < index:
													lowerCAmelCase_      =     mid + 1
										else:
													lowerCAmelCase_      =     mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
							array.insert(_UpperCamelCase    ,    _UpperCamelCase					)
							return array




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				None:
							# Remove previous index
							if index in self.indexmap:
										lowerCAmelCase_      =     self.indexmap[index]
										if len(self.numbermap[n]					) == 1:
													del self.numbermap[n]
										else:
													lowerCAmelCase_      =     self.binary_search_delete(self.numbermap[n]    ,    _UpperCamelCase					)

        # Set new index
							lowerCAmelCase_      =     number

							# Number not seen before or empty so insert number value
							if number not in self.numbermap:
										lowerCAmelCase_      =     [index]

							# Here we need to perform a binary search insertion in order to insert
							# The item in the correct place
							else:
										lowerCAmelCase_      =     self.binary_search_insert(
										    self.numbermap[number]    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							return self.numbermap.get(_UpperCamelCase    ,    [-1]					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



