

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		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  BigBirdConfig
					UpperCAmelCase__:  jnp.dtype    				=			jnp.floataa
					UpperCAmelCase__:  bool    				=			True






					def     __A     (					self     ):
						super().setup()
						A__				:       Optional[int]	  =						nn.Dense(5       ,			dtype=self.dtype     )






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



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Tuple    				=			FlaxBigBirdForNaturalQuestionsModule



def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Optional[int]       , lowercase_:					Any       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Dict   )	-> Dict:
	def cross_entropy(lowercase_:					str       , lowercase_:					Optional[int]       , lowercase_:					Any=None   ):
		A__				:       Dict	  =						logits.shape[-1]
		A__				:       str	  =						(labels[..., None] == jnp.arange(lowercase_   )[None]).astype("""f4"""   )
		A__				:       Dict	  =						jax.nn.log_softmax(lowercase_       , axis=-1   )
		A__				:       Optional[int]	  =						-jnp.sum(labels * logits       , axis=-1   )
		if reduction is not None:
			A__				:       str	  =						reduction(lowercase_   )
		return loss

	A__				:       Any	  =						partial(lowercase_       , reduction=jnp.mean   )
	A__				:       int	  =						cross_entropy(lowercase_       , lowercase_   )
	A__				:       Tuple	  =						cross_entropy(lowercase_       , lowercase_   )
	A__				:       Any	  =						cross_entropy(lowercase_       , lowercase_   )
	return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			"google/bigbird-roberta-base"
					UpperCAmelCase__:  int    				=			30_00
					UpperCAmelCase__:  int    				=			1_05_00

					UpperCAmelCase__:  int    				=			1_28
					UpperCAmelCase__:  int    				=			3

					UpperCAmelCase__:  int    				=			1
					UpperCAmelCase__:  int    				=			5

					# tx_args
					UpperCAmelCase__:  float    				=			3E-5
					UpperCAmelCase__:  float    				=			0.0
					UpperCAmelCase__:  int    				=			2_00_00
					UpperCAmelCase__:  float    				=			0.00_95

					UpperCAmelCase__:  str    				=			"bigbird-roberta-natural-questions"
					UpperCAmelCase__:  str    				=			"training-expt"
					UpperCAmelCase__:  str    				=			"data/nq-training.jsonl"
					UpperCAmelCase__:  str    				=			"data/nq-validation.jsonl"






					def     __A     (					self     ):
						os.makedirs(self.base_dir       ,			exist_ok=A__     )
						A__				:       List[Any]	  =						os.path.join(self.base_dir       ,			self.save_dir     )
						A__				:       List[Any]	  =						self.batch_size_per_device * jax.device_count()



@dataclass
class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  int
					UpperCAmelCase__:  int    				=			40_96  # no dynamic padding on TPUs






					def __call__(					self       ,			A__     ):
						A__				:       Optional[Any]	  =						self.collate_fn(A__     )
						A__				:       Any	  =						jax.tree_util.tree_map(A__       ,			A__     )
						return batch






					def     __A     (					self       ,			A__     ):
						A__  ,  A__				:       List[Any]	  =						self.fetch_inputs(features["""input_ids"""]     )
						A__				:       Optional[Any]	  =						{
						    """input_ids""": jnp.array(A__       ,			dtype=jnp.intaa     ),
						    """attention_mask""": jnp.array(A__       ,			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     __A     (					self       ,			A__     ):
						A__				:       List[Any]	  =						[self._fetch_inputs(A__     ) for ids in input_ids]
						return zip(*A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       List[Any]	  =						[1 for _ in range(len(A__     )     )]
						while len(A__     ) < self.max_length:
							input_ids.append(self.pad_id     )
							attention_mask.append(0     )
						return input_ids, attention_mask



def 		UpperCamelCase   (lowercase_:					Optional[int]       , lowercase_:					List[str]       , lowercase_:					Optional[Any]=None   )	-> Optional[int]:
	if seed is not None:
		A__				:       int	  =						dataset.shuffle(seed=lowercase_   )
	for i in range(len(lowercase_   ) // batch_size   ):
		A__				:       int	  =						dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(lowercase_   )

@partial(jax.pmap       , axis_name="""batch"""   )
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Union[str, Any]       , **lowercase_:					Union[str, Any]   )	-> List[Any]:
	def loss_fn(lowercase_:					Optional[int]   ):
		A__				:       int	  =						model_inputs.pop("""start_labels"""   )
		A__				:       Optional[Any]	  =						model_inputs.pop("""end_labels"""   )
		A__				:       Optional[int]	  =						model_inputs.pop("""pooled_labels"""   )

		A__				:       List[Any]	  =						state.apply_fn(**lowercase_       , params=lowercase_       , dropout_rng=lowercase_       , train=lowercase_   )
		A__  ,  A__  ,  A__				:       str	  =						outputs

		return state.loss_fn(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , )

	A__  ,  A__				:       int	  =						jax.random.split(lowercase_   )
	A__				:       Optional[Any]	  =						jax.value_and_grad(lowercase_   )
	A__  ,  A__				:       int	  =						grad_fn(state.params   )
	A__				:       List[Any]	  =						jax.lax.pmean({"""loss""": loss}       , axis_name="""batch"""   )
	A__				:       Optional[Any]	  =						jax.lax.pmean(lowercase_       , """batch"""   )

	A__				:       str	  =						state.apply_gradients(grads=lowercase_   )
	return state, metrics, new_drp_rng

@partial(jax.pmap       , axis_name="""batch"""   )
def 		UpperCamelCase   (lowercase_:					str       , **lowercase_:					Optional[Any]   )	-> List[str]:
	A__				:       Dict	  =						model_inputs.pop("""start_labels"""   )
	A__				:       str	  =						model_inputs.pop("""end_labels"""   )
	A__				:       List[str]	  =						model_inputs.pop("""pooled_labels"""   )

	A__				:       Tuple	  =						state.apply_fn(**lowercase_       , params=state.params       , train=lowercase_   )
	A__  ,  A__  ,  A__				:       Optional[int]	  =						outputs

	A__				:       List[str]	  =						state.loss_fn(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )
	A__				:       List[str]	  =						jax.lax.pmean({"""loss""": loss}       , axis_name="""batch"""   )
	return metrics



class        _a		(train_state.TrainState							):


					'''simple docstring'''






					UpperCAmelCase__:  Callable    				=			struct.field(pytree_node=__magic_name__							)



@dataclass
class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  Args
					UpperCAmelCase__:  Callable
					UpperCAmelCase__:  Callable
					UpperCAmelCase__:  Callable
					UpperCAmelCase__:  Callable
					UpperCAmelCase__:  wandb
					UpperCAmelCase__:  Callable    				=			None






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__=None     ):
						A__				:       str	  =						model.params
						A__				:       Optional[int]	  =						TrainState.create(
						    apply_fn=model.__call__       ,			params=A__       ,			tx=A__       ,			loss_fn=A__       ,			)
						if ckpt_dir is not None:
							A__  ,  A__  ,  A__  ,  A__  ,  A__				:       Union[str, Any]	  =						restore_checkpoint(A__       ,			A__     )
							A__				:       Optional[Any]	  =						{
							    """lr""": args.lr,
							    """init_lr""": args.init_lr,
							    """warmup_steps""": args.warmup_steps,
							    """num_train_steps""": num_train_steps,
							    """weight_decay""": args.weight_decay,
							}
							A__  ,  A__				:       Any	  =						build_tx(**A__     )
							A__				:       List[str]	  =						train_state.TrainState(
							    step=A__       ,			apply_fn=model.__call__       ,			params=A__       ,			tx=A__       ,			opt_state=A__       ,			)
							A__				:       Dict	  =						args
							A__				:       Optional[int]	  =						data_collator
							A__				:       Tuple	  =						lr
							A__				:       Optional[int]	  =						params
						A__				:       List[Any]	  =						jax_utils.replicate(A__     )
						return state






					def     __A     (					self       ,			A__       ,			A__       ,			A__     ):
						A__				:       int	  =						self.args
						A__				:       str	  =						len(A__     ) // args.batch_size

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

									A__				:       Union[str, Any]	  =						self.evaluate(A__       ,			A__     )
									A__				:       Optional[Any]	  =						{
									    """step""": state_step.item(),
									    """eval_loss""": eval_loss.item(),
									    """tr_loss""": tr_loss,
									    """lr""": lr.item(),
									}
									tqdm.write(str(A__     )     )
									self.logger.log(A__       ,			commit=A__     )

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






					def     __A     (					self       ,			A__       ,			A__     ):
						A__				:       Dict	  =						get_batched_dataset(A__       ,			self.args.batch_size     )
						A__				:       Union[str, Any]	  =						len(A__     ) // self.args.batch_size
						A__				:       List[Any]	  =						jnp.array(0       ,			dtype=jnp.floataa     )
						A__				:       str	  =						0
						for batch in tqdm(A__       ,			total=A__       ,			desc="""Evaluating ... """     ):
							A__				:       int	  =						self.data_collator(A__     )
							A__				:       List[Any]	  =						self.val_step_fn(A__       ,			**A__     )
							running_loss += jax_utils.unreplicate(metrics["""loss"""]     )
							i += 1
						return running_loss / i






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



def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					str   )	-> List[str]:
	print(f"""RESTORING CHECKPOINT FROM {save_dir}"""       , end=""" ... """   )
	with open(os.path.join(lowercase_       , """flax_model.msgpack"""   )       , """rb"""   ) as f:
		A__				:       Union[str, Any]	  =						from_bytes(state.params       , f.read()   )

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

	A__				:       Union[str, Any]	  =						joblib.load(os.path.join(lowercase_       , """args.joblib"""   )   )
	A__				:       List[Any]	  =						joblib.load(os.path.join(lowercase_       , """data_collator.joblib"""   )   )

	with open(os.path.join(lowercase_       , """training_state.json"""   )       , """r"""   ) as f:
		A__				:       List[Any]	  =						json.load(lowercase_   )
	A__				:       List[Any]	  =						training_state["""step"""]

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

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					Any       , lowercase_:					Tuple       , lowercase_:					Any   )	-> List[Any]:
	A__				:       int	  =						num_train_steps - warmup_steps
	A__				:       str	  =						optax.linear_schedule(init_value=lowercase_       , end_value=lowercase_       , transition_steps=lowercase_   )
	A__				:       Dict	  =						optax.linear_schedule(init_value=lowercase_       , end_value=1E-7       , transition_steps=lowercase_   )
	A__				:       str	  =						optax.join_schedules(schedules=[warmup_fn, decay_fn]       , boundaries=[warmup_steps]   )
	return lr


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

	A__				:       Tuple	  =						scheduler_fn(lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	A__				:       Dict	  =						optax.adamw(learning_rate=lowercase_       , weight_decay=lowercase_       , mask=lowercase_   )
	return tx, lr






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



