
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     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			jnp.floataa
     lowerCAmelCase_							   =			True



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




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




     def __call__(				self							:							Tuple     ,						*_A							:							List[Any]     ,						**_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		super().__call__(*_A     ,						**_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.cls(outputs[2] )
       return outputs[:2] + (cls_out,)






class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			FlaxBigBirdForNaturalQuestionsModule






def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  def cross_entropy(snake_case       ,			snake_case       ,			snake_case=None							):
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		logits.shape[-1]
    __SCREAMING_SNAKE_CASE      :	Any					=		(labels[..., None] == jnp.arange(snake_case							)[None]).astype('''f4'''							)
    __SCREAMING_SNAKE_CASE      :	str					=		jax.nn.log_softmax(snake_case       ,			axis=-1							)
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		-jnp.sum(labels * logits       ,			axis=-1							)
    if reduction is not None:
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		reduction(snake_case							)
    return loss

  __SCREAMING_SNAKE_CASE      :	List[str]					=		partial(snake_case       ,			reduction=jnp.mean							)
  __SCREAMING_SNAKE_CASE      :	int					=		cross_entropy(snake_case       ,			snake_case							)
  __SCREAMING_SNAKE_CASE      :	Any					=		cross_entropy(snake_case       ,			snake_case							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		cross_entropy(snake_case       ,			snake_case							)
  return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class     __UpperCamelCase :






     """simple docstring"""







     lowerCAmelCase_							   =			"google/bigbird-roberta-base"
     lowerCAmelCase_							   =			30_00
     lowerCAmelCase_							   =			1_05_00

     lowerCAmelCase_							   =			1_28
     lowerCAmelCase_							   =			3

     lowerCAmelCase_							   =			1
     lowerCAmelCase_							   =			5

     # tx_args
     lowerCAmelCase_							   =			3E-5
     lowerCAmelCase_							   =			0.0
     lowerCAmelCase_							   =			2_00_00
     lowerCAmelCase_							   =			0.0095

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



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       os.makedirs(self.base_dir     ,						exist_ok=_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		os.path.join(self.base_dir     ,						self.save_dir )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.batch_size_per_device * jax.device_count()






@dataclass
class     __UpperCamelCase :






     """simple docstring"""







     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			40_96  # no dynamic padding on TPUs



     def __call__(				self							:							List[str]     ,						_A							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.collate_fn(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		jax.tree_util.tree_map(_A     ,						_A )
       return batch



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		self.fetch_inputs(features['''input_ids'''] )
       __SCREAMING_SNAKE_CASE      :	Union[str, 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  UpperCAmelCase__							(				self							:							Any     ,						_A							:							list ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[self._fetch_inputs(_A ) for ids in input_ids]
       return zip(*_A )




     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							list ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[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 							a__					(							snake_case       ,			snake_case       ,			snake_case=None							):





  """simple docstring"""
  if seed is not None:
    __SCREAMING_SNAKE_CASE      :	Any					=		dataset.shuffle(seed=snake_case							)
  for i in range(len(snake_case							) // batch_size							):
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(snake_case							)


@partial(jax.pmap       ,			axis_name='''batch'''							)
def 							a__					(							snake_case       ,			snake_case       ,			**snake_case							):





  """simple docstring"""
  def loss_fn(snake_case							):
    __SCREAMING_SNAKE_CASE      :	Tuple					=		model_inputs.pop('''start_labels'''							)
    __SCREAMING_SNAKE_CASE      :	str					=		model_inputs.pop('''end_labels'''							)
    __SCREAMING_SNAKE_CASE      :	List[str]					=		model_inputs.pop('''pooled_labels'''							)

    __SCREAMING_SNAKE_CASE      :	Tuple					=		state.apply_fn(**snake_case       ,			params=snake_case       ,			dropout_rng=snake_case       ,			train=snake_case							)
    __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		outputs

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

  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		jax.random.split(snake_case							)
  __SCREAMING_SNAKE_CASE      :	int					=		jax.value_and_grad(snake_case							)
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		grad_fn(state.params							)
  __SCREAMING_SNAKE_CASE      :	Tuple					=		jax.lax.pmean({'''loss''': loss}       ,			axis_name='''batch'''							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		jax.lax.pmean(snake_case       ,			'''batch'''							)

  __SCREAMING_SNAKE_CASE      :	Any					=		state.apply_gradients(grads=snake_case							)
  return state, metrics, new_drp_rng


@partial(jax.pmap       ,			axis_name='''batch'''							)
def 							a__					(							snake_case       ,			**snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model_inputs.pop('''start_labels'''							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		model_inputs.pop('''end_labels'''							)
  __SCREAMING_SNAKE_CASE      :	Any					=		model_inputs.pop('''pooled_labels'''							)

  __SCREAMING_SNAKE_CASE      :	Dict					=		state.apply_fn(**snake_case       ,			params=state.params       ,			train=snake_case							)
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		outputs

  __SCREAMING_SNAKE_CASE      :	int					=		state.loss_fn(snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		jax.lax.pmean({'''loss''': loss}       ,			axis_name='''batch'''							)
  return metrics





class     __UpperCamelCase (							train_state.TrainState       ):






     """simple docstring"""







     lowerCAmelCase_							   =			struct.field(pytree_node=lowerCAmelCase__       )






@dataclass
class     __UpperCamelCase :






     """simple docstring"""







     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			42
     lowerCAmelCase_							   =			None



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							Optional[Any]     ,						_A							:							str     ,						_A							:							Optional[int]     ,						_A							:							List[str]=None ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						_A							:							List[str]     ,						_A							:							int     ,						_A							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.args
       __SCREAMING_SNAKE_CASE      :	Tuple					=		len(_A ) // args.batch_size

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

             __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.evaluate(_A     ,						_A )
             __SCREAMING_SNAKE_CASE      :	Union[str, 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  UpperCAmelCase__							(				self							:							int     ,						_A							:							Any     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		get_batched_dataset(_A     ,						self.args.batch_size )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		len(_A ) // self.args.batch_size
       __SCREAMING_SNAKE_CASE      :	Any					=		jnp.array(0     ,						dtype=jnp.floataa )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		0
       for batch in tqdm(_A     ,						total=_A     ,						desc='''Evaluating ... ''' ):
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.data_collator(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.val_step_fn(_A     ,						**_A )
         running_loss += jax_utils.unreplicate(metrics['''loss'''] )
         i += 1
       return running_loss / i




     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							Optional[Any]     ,						_A							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		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 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  print(F'''RESTORING CHECKPOINT FROM {save_dir}'''       ,			end=''' ... '''							)
  with open(os.path.join(snake_case       ,			'''flax_model.msgpack'''							)       ,			'''rb'''							) as f:
    __SCREAMING_SNAKE_CASE      :	str					=		from_bytes(state.params       ,			f.read()							)

  with open(os.path.join(snake_case       ,			'''opt_state.msgpack'''							)       ,			'''rb'''							) as f:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		from_bytes(state.opt_state       ,			f.read()							)

  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		joblib.load(os.path.join(snake_case       ,			'''args.joblib'''							)							)
  __SCREAMING_SNAKE_CASE      :	List[str]					=		joblib.load(os.path.join(snake_case       ,			'''data_collator.joblib'''							)							)

  with open(os.path.join(snake_case       ,			'''training_state.json'''							)       ,			'''r'''							) as f:
    __SCREAMING_SNAKE_CASE      :	str					=		json.load(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		training_state['''step''']

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


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Tuple					=		num_train_steps - warmup_steps
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		optax.linear_schedule(init_value=snake_case       ,			end_value=snake_case       ,			transition_steps=snake_case							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		optax.linear_schedule(init_value=snake_case       ,			end_value=1E-7       ,			transition_steps=snake_case							)
  __SCREAMING_SNAKE_CASE      :	List[str]					=		optax.join_schedules(schedules=[warmup_fn, decay_fn]       ,			boundaries=[warmup_steps]							)
  return lr


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  def weight_decay_mask(snake_case							):
    __SCREAMING_SNAKE_CASE      :	int					=		traverse_util.flatten_dict(snake_case							)
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		{k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
    return traverse_util.unflatten_dict(snake_case							)

  __SCREAMING_SNAKE_CASE      :	List[str]					=		scheduler_fn(snake_case       ,			snake_case       ,			snake_case       ,			snake_case							)

  __SCREAMING_SNAKE_CASE      :	Any					=		optax.adamw(learning_rate=snake_case       ,			weight_decay=snake_case       ,			mask=snake_case							)
  return tx, lr


import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


if __name__ == "__main__":
 print(solution())
