




import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule




class       _A				(		__lowercase     ):
			__a					    =       42
			__a					    =       jnp.floataa
			__a					    =       True






			def 				UpperCAmelCase (    self			):
									super().setup()
									_UpperCAmelCase      =			nn.Dense(5						,		dtype=self.dtype			)






			def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
									_UpperCAmelCase      =			self.cls(outputs[2]			)
									return outputs[:2] + (cls_out,)




class       _A				(		__lowercase     ):
			__a					    =       FlaxBigBirdForNaturalQuestionsModule




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						def cross_entropy(snake_case			,     snake_case			,     snake_case=None       ):
												_UpperCAmelCase      =			logits.shape[-1]
												_UpperCAmelCase      =			(labels[..., None] == jnp.arange(snake_case       )[None]).astype("""f4"""       )
												_UpperCAmelCase      =			jax.nn.log_softmax(snake_case			,     axis=-1       )
												_UpperCAmelCase      =			-jnp.sum(labels * logits			,     axis=-1       )
												if reduction is not None:
																		_UpperCAmelCase      =			reduction(snake_case       )
												return loss

						_UpperCAmelCase      =			partial(snake_case			,     reduction=jnp.mean       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class       _A				:
			__a					    =       """google/bigbird-roberta-base"""
			__a					    =       30_00
			__a					    =       1_05_00

			__a					    =       1_28
			__a					    =       3

			__a					    =       1
			__a					    =       5

			# tx_args
			__a					    =       3e-5
			__a					    =       0.0
			__a					    =       2_00_00
			__a					    =       0.0095

			__a					    =       """bigbird-roberta-natural-questions"""
			__a					    =       """training-expt"""
			__a					    =       """data/nq-training.jsonl"""
			__a					    =       """data/nq-validation.jsonl"""






			def 				UpperCAmelCase (    self			):
									os.makedirs(self.base_dir						,		exist_ok=_SCREAMING_SNAKE_CASE			)
									_UpperCAmelCase      =			os.path.join(self.base_dir						,		self.save_dir			)
									_UpperCAmelCase      =			self.batch_size_per_device * jax.device_count()




@dataclass
class       _A				:
			__a					    =       42
			__a					    =       40_96  # no dynamic padding on TPUs






			def __call__(    self						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			self.collate_fn(_SCREAMING_SNAKE_CASE			)
									_UpperCAmelCase      =			jax.tree_util.tree_map(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
									return batch






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase    ,					_UpperCAmelCase      =			self.fetch_inputs(features["""input_ids"""]			)
									_UpperCAmelCase      =			{
									    """input_ids""": jnp.array(_SCREAMING_SNAKE_CASE						,		dtype=jnp.intaa			),
									    """attention_mask""": jnp.array(_SCREAMING_SNAKE_CASE						,		dtype=jnp.intaa			),
									    """start_labels""": jnp.array(features["""start_token"""]						,		dtype=jnp.intaa			),
									    """end_labels""": jnp.array(features["""end_token"""]						,		dtype=jnp.intaa			),
									    """pooled_labels""": jnp.array(features["""category"""]						,		dtype=jnp.intaa			),
									}
									return batch






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			[self._fetch_inputs(_SCREAMING_SNAKE_CASE			) for ids in input_ids]
									return zip(*_SCREAMING_SNAKE_CASE			)






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			[1 for _ in range(len(_SCREAMING_SNAKE_CASE			)			)]
									while len(_SCREAMING_SNAKE_CASE			) < self.max_length:
															input_ids.append(self.pad_id			)
															attention_mask.append(0			)
									return input_ids, attention_mask




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None       )						->				Dict:
						if seed is not None:
												_UpperCAmelCase      =			dataset.shuffle(seed=snake_case       )
						for i in range(len(snake_case       ) // batch_size       ):
												_UpperCAmelCase      =			dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(snake_case       )




@partial(jax.pmap			,     axis_name="""batch"""       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     **snake_case       )						->				Any:
						def loss_fn(snake_case       ):
												_UpperCAmelCase      =			model_inputs.pop("""start_labels"""       )
												_UpperCAmelCase      =			model_inputs.pop("""end_labels"""       )
												_UpperCAmelCase      =			model_inputs.pop("""pooled_labels"""       )

												_UpperCAmelCase      =			state.apply_fn(**snake_case			,     params=snake_case			,     dropout_rng=snake_case			,     train=snake_case       )
												_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			outputs

												return state.loss_fn(
												    snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     )

						_UpperCAmelCase    ,					_UpperCAmelCase      =			jax.random.split(snake_case       )
						_UpperCAmelCase      =			jax.value_and_grad(snake_case       )
						_UpperCAmelCase    ,					_UpperCAmelCase      =			grad_fn(state.params       )
						_UpperCAmelCase      =			jax.lax.pmean({"""loss""": loss}			,     axis_name="""batch"""       )
						_UpperCAmelCase      =			jax.lax.pmean(snake_case			,     """batch"""       )

						_UpperCAmelCase      =			state.apply_gradients(grads=snake_case       )
						return state, metrics, new_drp_rng




@partial(jax.pmap			,     axis_name="""batch"""       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     **snake_case       )						->				Tuple:
						_UpperCAmelCase      =			model_inputs.pop("""start_labels"""       )
						_UpperCAmelCase      =			model_inputs.pop("""end_labels"""       )
						_UpperCAmelCase      =			model_inputs.pop("""pooled_labels"""       )

						_UpperCAmelCase      =			state.apply_fn(**snake_case			,     params=state.params			,     train=snake_case       )
						_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			outputs

						_UpperCAmelCase      =			state.loss_fn(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
						_UpperCAmelCase      =			jax.lax.pmean({"""loss""": loss}			,     axis_name="""batch"""       )
						return metrics




class       _A				(		train_state.TrainState     ):
			__a					    =       struct.field(pytree_node=__lowercase     )




@dataclass
class       _A				:
			__a					    =       42
			__a					    =       42
			__a					    =       42
			__a					    =       42
			__a					    =       42
			__a					    =       42
			__a					    =       None






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
									_UpperCAmelCase      =			model.params
									_UpperCAmelCase      =			TrainState.create(
									    apply_fn=model.__call__						,		params=_SCREAMING_SNAKE_CASE						,		tx=_SCREAMING_SNAKE_CASE						,		loss_fn=_SCREAMING_SNAKE_CASE						,		)
									if ckpt_dir is not None:
															_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			restore_checkpoint(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
															_UpperCAmelCase      =			{
															    """lr""": args.lr,
															    """init_lr""": args.init_lr,
															    """warmup_steps""": args.warmup_steps,
															    """num_train_steps""": num_train_steps,
															    """weight_decay""": args.weight_decay,
															}
															_UpperCAmelCase    ,					_UpperCAmelCase      =			build_tx(**_SCREAMING_SNAKE_CASE			)
															_UpperCAmelCase      =			train_state.TrainState(
															    step=_SCREAMING_SNAKE_CASE						,		apply_fn=model.__call__						,		params=_SCREAMING_SNAKE_CASE						,		tx=_SCREAMING_SNAKE_CASE						,		opt_state=_SCREAMING_SNAKE_CASE						,		)
															_UpperCAmelCase      =			args
															_UpperCAmelCase      =			data_collator
															_UpperCAmelCase      =			lr
															_UpperCAmelCase      =			params
									_UpperCAmelCase      =			jax_utils.replicate(_SCREAMING_SNAKE_CASE			)
									return state






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			self.args
									_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) // args.batch_size

									_UpperCAmelCase      =			jax.random.PRNGKey(0			)
									_UpperCAmelCase      =			jax.random.split(_SCREAMING_SNAKE_CASE						,		jax.device_count()			)
									for epoch in range(args.max_epochs			):
															_UpperCAmelCase      =			jnp.array(0						,		dtype=jnp.floataa			)
															_UpperCAmelCase      =			get_batched_dataset(_SCREAMING_SNAKE_CASE						,		args.batch_size						,		seed=_SCREAMING_SNAKE_CASE			)
															_UpperCAmelCase      =			0
															for batch in tqdm(_SCREAMING_SNAKE_CASE						,		total=_SCREAMING_SNAKE_CASE						,		desc=F"Running EPOCH-{epoch}"			):
																					_UpperCAmelCase      =			self.data_collator(_SCREAMING_SNAKE_CASE			)
																					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.train_step_fn(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
																					running_loss += jax_utils.unreplicate(metrics["""loss"""]			)
																					i += 1
																					if i % args.logging_steps == 0:
																											_UpperCAmelCase      =			jax_utils.unreplicate(state.step			)
																											_UpperCAmelCase      =			running_loss.item() / i
																											_UpperCAmelCase      =			self.scheduler_fn(state_step - 1			)

																											_UpperCAmelCase      =			self.evaluate(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																											_UpperCAmelCase      =			{
																											    """step""": state_step.item(),
																											    """eval_loss""": eval_loss.item(),
																											    """tr_loss""": tr_loss,
																											    """lr""": lr.item(),
																											}
																											tqdm.write(str(_SCREAMING_SNAKE_CASE			)			)
																											self.logger.log(_SCREAMING_SNAKE_CASE						,		commit=_SCREAMING_SNAKE_CASE			)

																					if i % args.save_steps == 0:
																											self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"						,		state=_SCREAMING_SNAKE_CASE			)






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			get_batched_dataset(_SCREAMING_SNAKE_CASE						,		self.args.batch_size			)
									_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) // self.args.batch_size
									_UpperCAmelCase      =			jnp.array(0						,		dtype=jnp.floataa			)
									_UpperCAmelCase      =			0
									for batch in tqdm(_SCREAMING_SNAKE_CASE						,		total=_SCREAMING_SNAKE_CASE						,		desc="""Evaluating ... """			):
															_UpperCAmelCase      =			self.data_collator(_SCREAMING_SNAKE_CASE			)
															_UpperCAmelCase      =			self.val_step_fn(_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
															running_loss += jax_utils.unreplicate(metrics["""loss"""]			)
															i += 1
									return running_loss / i






			def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
									_UpperCAmelCase      =			jax_utils.unreplicate(_SCREAMING_SNAKE_CASE			)
									print(F"SAVING CHECKPOINT IN {save_dir}"						,		end=""" ... """			)
									self.model_save_fn(_SCREAMING_SNAKE_CASE						,		params=state.params			)
									with open(os.path.join(_SCREAMING_SNAKE_CASE						,		"""opt_state.msgpack"""			)						,		"""wb"""			) as f:
															f.write(to_bytes(state.opt_state			)			)
									joblib.dump(self.args						,		os.path.join(_SCREAMING_SNAKE_CASE						,		"""args.joblib"""			)			)
									joblib.dump(self.data_collator						,		os.path.join(_SCREAMING_SNAKE_CASE						,		"""data_collator.joblib"""			)			)
									with open(os.path.join(_SCREAMING_SNAKE_CASE						,		"""training_state.json"""			)						,		"""w"""			) as f:
															json.dump({"""step""": state.step.item()}						,		_SCREAMING_SNAKE_CASE			)
									print("""DONE"""			)




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Dict:
						print(f"RESTORING CHECKPOINT FROM {save_dir}"			,     end=""" ... """       )
						with open(os.path.join(snake_case			,     """flax_model.msgpack"""       )			,     """rb"""       ) as f:
												_UpperCAmelCase      =			from_bytes(state.params			,     f.read()       )

						with open(os.path.join(snake_case			,     """opt_state.msgpack"""       )			,     """rb"""       ) as f:
												_UpperCAmelCase      =			from_bytes(state.opt_state			,     f.read()       )

						_UpperCAmelCase      =			joblib.load(os.path.join(snake_case			,     """args.joblib"""       )       )
						_UpperCAmelCase      =			joblib.load(os.path.join(snake_case			,     """data_collator.joblib"""       )       )

						with open(os.path.join(snake_case			,     """training_state.json"""       )			,     """r"""       ) as f:
												_UpperCAmelCase      =			json.load(snake_case       )
						_UpperCAmelCase      =			training_state["""step"""]

						print("""DONE"""       )
						return params, opt_state, step, args, data_collator




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			num_train_steps - warmup_steps
						_UpperCAmelCase      =			optax.linear_schedule(init_value=snake_case			,     end_value=snake_case			,     transition_steps=snake_case       )
						_UpperCAmelCase      =			optax.linear_schedule(init_value=snake_case			,     end_value=1E-7			,     transition_steps=snake_case       )
						_UpperCAmelCase      =			optax.join_schedules(schedules=[warmup_fn, decay_fn]			,     boundaries=[warmup_steps]       )
						return lr




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						def weight_decay_mask(snake_case       ):
												_UpperCAmelCase      =			traverse_util.flatten_dict(snake_case       )
												_UpperCAmelCase      =			{k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
												return traverse_util.unflatten_dict(snake_case       )

						_UpperCAmelCase      =			scheduler_fn(snake_case			,     snake_case			,     snake_case			,     snake_case       )

						_UpperCAmelCase      =			optax.adamw(learning_rate=snake_case			,     weight_decay=snake_case			,     mask=snake_case       )
						return tx, lr





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()