




'''simple docstring'''

import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


a__         =     {0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
a__         =     set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))




def    snake_case__     (  a       ,      a=0       ,      a=None	)			->       Any:





			'''simple docstring'''






			return [" ".join(x.split(""" """	)[begin:end]	) for x in answers if len(x.split(""" """	)	) > 1]




def    snake_case__     (  a       ,      a=False	)			->       Dict:





			'''simple docstring'''






			if make_sub_answers:
						# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
						# *e.g.* if the correct answer contains a prefix such as "the", or "a"
						snake_case__          =						(
						    given_answers + get_sub_answers(a       ,      begin=1	) + get_sub_answers(a       ,      end=-1	)
						)
			snake_case__          =						[]
			for answer in given_answers:
						snake_case__          =						answer.replace("""_"""       ,      """ """	).lower()
						snake_case__          =						"""""".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else """ """ for c in alias	)
						answers.append(""" """.join(alias.split()	).strip()	)
			return set(a	)




def    snake_case__     (  a       ,      a       ,      a=1       ,      a=100	)			->       str:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						jax.lax.top_k(a       ,      a	)
			snake_case__    ,			snake_case__          =						jax.lax.top_k(a       ,      a	)

			snake_case__          =						best_end_idx[:, None] - best_start_idx[None, :]
			snake_case__          =						jnp.logical_or(widths < 0       ,      widths > max_size	)
			snake_case__          =						(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
			snake_case__          =						jnp.argmax(a	).item()

			return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def    snake_case__     (  a	)			->       Any:





			'''simple docstring'''






			snake_case__          =						sample["""question"""]["""text"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""token"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""is_html"""]
			snake_case__          =						sample["""annotations"""]["""long_answer"""]
			snake_case__          =						sample["""annotations"""]["""short_answers"""]

			snake_case__          =						""" """.join([context[i] for i in range(len(a	)	) if not is_html[i]]	)

			# 0 - No ; 1 - Yes
			for answer in sample["annotations"]["yes_no_answer"]:
						if answer == 0 or answer == 1:
									return {
									    "question": question,
									    "context": context_string,
									    "short": [],
									    "long": [],
									    "category": "no" if answer == 0 else "yes",
									}

			snake_case__          =						[]
			for s in short_answers:
						short_targets.extend(s["""text"""]	)
			snake_case__          =						list(set(a	)	)

			snake_case__          =						[]
			for s in long_answers:
						if s["start_token"] == -1:
									continue
						snake_case__          =						context[s["""start_token"""] : s["""end_token"""]]
						snake_case__          =						is_html[s["""start_token"""] : s["""end_token"""]]
						snake_case__          =						""" """.join([answer[i] for i in range(len(a	)	) if not html[i]]	)
						if new_answer not in long_targets:
									long_targets.append(a	)

			snake_case__          =						"""long_short""" if len(short_targets + long_targets	) > 0 else """null"""

			return {
			    "question": question,
			    "context": context_string,
			    "short": short_targets,
			    "long": long_targets,
			    "category": category,
			}




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						load_from_disk("""natural-questions-validation"""	)
			snake_case__          =						dataset.map(a	).remove_columns(["""annotations""", """document""", """id"""]	)
			print(a	)

			snake_case__          =						dataset.filter(lambda a	: (len(x["""question"""]	) + len(x["""context"""]	)) < 4 * 4096	)
			snake_case__          =						short_validation_dataset.filter(lambda a	: x["category"] != "null"	)
			short_validation_dataset

			snake_case__          =						"""vasudevgupta/flax-bigbird-natural-questions"""
			snake_case__          =						FlaxBigBirdForNaturalQuestions.from_pretrained(a	)
			snake_case__          =						BigBirdTokenizerFast.from_pretrained(a	)

			@jax.jit
			def forward(*a       ,      **a	):
						snake_case__    ,			snake_case__    ,			snake_case__          =						model(*a       ,      **a	)
						return start_logits, end_logits, jnp.argmax(a       ,      axis=-1	)

			def evaluate(a	):
						# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
						snake_case__          =						tokenizer(
						    example["""question"""]       ,      example["""context"""]       ,      return_tensors="""np"""       ,      max_length=4096       ,      padding="""max_length"""       ,      truncation=a       ,      )

						snake_case__    ,			snake_case__    ,			snake_case__          =						forward(**a	)

						snake_case__          =						CATEGORY_MAPPING[category.item()]

						snake_case__          =						example["""long"""] + example["""short"""]
						if example["category"] in ["yes", "no", "null"]:
									snake_case__          =						[example["""category"""]]
						snake_case__          =						example["""category"""] != """null"""
						# Now target can be: "yes", "no", "null", "list of long & short answers"

						if predicted_category in ["yes", "no", "null"]:
									snake_case__          =						[predicted_category]
									snake_case__          =						example["""output"""] == example["""targets"""]
									snake_case__          =						predicted_category != """null"""
									return example

						snake_case__          =						38 if predicted_category == """short""" else 1024
						snake_case__    ,			snake_case__          =						get_best_valid_start_end_idx(
						    start_scores[0]       ,      end_scores[0]       ,      top_k=8       ,      max_size=a	)

						snake_case__          =						inputs["""input_ids"""][0].tolist()
						snake_case__          =						[tokenizer.decode(input_ids[start_score : end_score + 1]	)]

						snake_case__          =						expand_to_aliases(example["""targets"""]       ,      make_sub_answers=a	)
						snake_case__          =						expand_to_aliases(example["""output"""]	)

						# some preprocessing to both prediction and answer
						snake_case__          =						{"""""".join(a.split()	) for a in answers}
						snake_case__          =						{"""""".join(p.split()	) for p in predictions}
						snake_case__          =						{s for s in predictions if s not in ["""``""", """''""", """`""", """'"""]}

						# if there is a common element, it's a exact match
						snake_case__          =						len(list(answers & predictions	)	) > 0
						snake_case__          =						predicted_category != """null""" and len(a	) > 0

						return example

			snake_case__          =						short_validation_dataset.map(a	)

			snake_case__          =						len(a	)
			snake_case__          =						len(short_validation_dataset.filter(lambda a	: x["match"] == 1	)	)
			print("""EM score:"""       ,      (matched / total) * 100       ,      """%"""	)


if __name__ == "__main__":
		main()




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

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





