





'''simple docstring'''


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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					42
 __lowerCamelCase						:							int         =					jnp.floataa
 __lowerCamelCase						:							Any         =					True




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     super().setup()
     _lowerCAmelCase				=      nn.Dense(5			,     dtype=self.dtype		)





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







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					FlaxBigBirdForNaturalQuestionsModule







def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    def cross_entropy(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					Any=None    ):
        _lowerCAmelCase				=      logits.shape[-1]
        _lowerCAmelCase				=      (labels[..., None] == jnp.arange(a__    )[None]).astype("f4"    )
        _lowerCAmelCase				=      jax.nn.log_softmax(a__		,       axis=-1    )
        _lowerCAmelCase				=      -jnp.sum(labels * logits		,       axis=-1    )
        if reduction is not None:
            _lowerCAmelCase				=      reduction(a__    )
        return loss

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

@dataclass
class    lowerCAmelCase_		:
 __lowerCamelCase						:							int         =					"google/bigbird-roberta-base"
 __lowerCamelCase						:							Tuple         =					3_000
 __lowerCamelCase						:							Tuple         =					10_500

 __lowerCamelCase						:							str         =					128
 __lowerCamelCase						:							Optional[Any]         =					3

 __lowerCamelCase						:							int         =					1
 __lowerCamelCase						:							Optional[int]         =					5

 # tx_args
 __lowerCamelCase						:							Optional[Any]         =					3e-5
 __lowerCamelCase						:							List[Any]         =					0.0
 __lowerCamelCase						:							Dict         =					20_000
 __lowerCamelCase						:							str         =					0.0_0_9_5

 __lowerCamelCase						:							Optional[int]         =					"bigbird-roberta-natural-questions"
 __lowerCamelCase						:							Tuple         =					"training-expt"
 __lowerCamelCase						:							Optional[Any]         =					"data/nq-training.jsonl"
 __lowerCamelCase						:							Union[str, Any]         =					"data/nq-validation.jsonl"




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     os.makedirs(self.base_dir			,     exist_ok=lowercase_		)
     _lowerCAmelCase				=      os.path.join(self.base_dir			,     self.save_dir		)
     _lowerCAmelCase				=      self.batch_size_per_device * jax.device_count()







@dataclass
class    lowerCAmelCase_		:
 __lowerCamelCase						:							Optional[Any]         =					42
 __lowerCamelCase						:							Dict         =					4_096  # no dynamic padding on TPUs




 def __call__(	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      self.collate_fn(lowercase_		)
     _lowerCAmelCase				=      jax.tree_util.tree_map(lowercase_			,     lowercase_		)
     return batch




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.fetch_inputs(features["input_ids"]		)
     _lowerCAmelCase				=      {
         "input_ids": jnp.array(lowercase_			,     dtype=jnp.intaa		),
         "attention_mask": jnp.array(lowercase_			,     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 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase				=      [self._fetch_inputs(lowercase_		) for ids in input_ids]
     return zip(*lowercase_		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      [1 for _ in range(len(lowercase_		)		)]
     while len(lowercase_		) < self.max_length:
         input_ids.append(self.pad_id		)
         attention_mask.append(0		)
     return input_ids, attention_mask







def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]=None    ):



    '''simple docstring'''





    if seed is not None:
        _lowerCAmelCase				=      dataset.shuffle(seed=a__    )
    for i in range(len(a__    ) // batch_size    ):
        _lowerCAmelCase				=      dataset[i * batch_size : (i + 1) * batch_size]
        yield dict(a__    )


@partial(jax.pmap		,       axis_name="batch"    )
def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Dict		,       **SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    def loss_fn(SCREAMING_SNAKE_CASE_  :					int    ):
        _lowerCAmelCase				=      model_inputs.pop("start_labels"    )
        _lowerCAmelCase				=      model_inputs.pop("end_labels"    )
        _lowerCAmelCase				=      model_inputs.pop("pooled_labels"    )

        _lowerCAmelCase				=      state.apply_fn(**a__		,       params=a__		,       dropout_rng=a__		,       train=a__    )
        _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      outputs

        return state.loss_fn(
            a__		,       a__		,       a__		,       a__		,       a__		,       a__		,       )

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

    _lowerCAmelCase				=      state.apply_gradients(grads=a__    )
    return state, metrics, new_drp_rng


@partial(jax.pmap		,       axis_name="batch"    )
def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]		,       **SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      model_inputs.pop("start_labels"    )
    _lowerCAmelCase				=      model_inputs.pop("end_labels"    )
    _lowerCAmelCase				=      model_inputs.pop("pooled_labels"    )

    _lowerCAmelCase				=      state.apply_fn(**a__		,       params=state.params		,       train=a__    )
    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      outputs

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

class    lowerCAmelCase_		(		train_state.TrainState		):
 __lowerCamelCase						:							List[str]         =					struct.field(pytree_node=__magic_name__		)







@dataclass
class    lowerCAmelCase_		:
 __lowerCamelCase						:							Optional[int]         =					42
 __lowerCamelCase						:							Any         =					42
 __lowerCamelCase						:							Any         =					42
 __lowerCamelCase						:							Tuple         =					42
 __lowerCamelCase						:							Any         =					42
 __lowerCamelCase						:							Union[str, Any]         =					42
 __lowerCamelCase						:							str         =					None




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




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase				=      self.args
     _lowerCAmelCase				=      len(lowercase_		) // args.batch_size

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

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

             if i % args.save_steps == 0:
                 self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}'''			,     state=lowercase_		)




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





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[Any]:
     _lowerCAmelCase				=      jax_utils.unreplicate(lowercase_		)
     print(f'''SAVING CHECKPOINT IN {save_dir}'''			,     end=" ... "		)
     self.model_save_fn(lowercase_			,     params=state.params		)
     with open(os.path.join(lowercase_			,     "opt_state.msgpack"		)			,     "wb"		) as f:
         f.write(to_bytes(state.opt_state		)		)
     joblib.dump(self.args			,     os.path.join(lowercase_			,     "args.joblib"		)		)
     joblib.dump(self.data_collator			,     os.path.join(lowercase_			,     "data_collator.joblib"		)		)
     with open(os.path.join(lowercase_			,     "training_state.json"		)			,     "w"		) as f:
         json.dump({"step": state.step.item()}			,     lowercase_		)
     print("DONE"		)







def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





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

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

    _lowerCAmelCase				=      joblib.load(os.path.join(a__		,       "args.joblib"    )    )
    _lowerCAmelCase				=      joblib.load(os.path.join(a__		,       "data_collator.joblib"    )    )

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

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


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      num_train_steps - warmup_steps
    _lowerCAmelCase				=      optax.linear_schedule(init_value=a__		,       end_value=a__		,       transition_steps=a__    )
    _lowerCAmelCase				=      optax.linear_schedule(init_value=a__		,       end_value=1e-7		,       transition_steps=a__    )
    _lowerCAmelCase				=      optax.join_schedules(schedules=[warmup_fn, decay_fn]		,       boundaries=[warmup_steps]    )
    return lr





def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					List[str]    ):



    '''simple docstring'''





    def weight_decay_mask(SCREAMING_SNAKE_CASE_  :					Dict    ):
        _lowerCAmelCase				=      traverse_util.flatten_dict(a__    )
        _lowerCAmelCase				=      {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
        return traverse_util.unflatten_dict(a__    )

    _lowerCAmelCase				=      scheduler_fn(a__		,       a__		,       a__		,       a__    )

    _lowerCAmelCase				=      optax.adamw(learning_rate=a__		,       weight_decay=a__		,       mask=a__    )
    return tx, lr











'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


