'''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 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    jnp.floataa
       SCREAMING_SNAKE_CASE							=    True

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


       def __call__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Any  =					super().__call__(*__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    int  =					self.cls(outputs[2] )
        return outputs[:2] + (cls_out,)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    FlaxBigBirdForNaturalQuestionsModule


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

 lowercase__				:    List[Any]  =					partial(UpperCAmelCase		,     reduction=jnp.mean     )
 lowercase__				:    str  =					cross_entropy(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Optional[Any]  =					cross_entropy(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Optional[Any]  =					cross_entropy(UpperCAmelCase		,     UpperCAmelCase     )
 return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "google/bigbird-roberta-base"
       SCREAMING_SNAKE_CASE							=    3_0_0_0
       SCREAMING_SNAKE_CASE							=    1_0_5_0_0

       SCREAMING_SNAKE_CASE							=    1_2_8
       SCREAMING_SNAKE_CASE							=    3

       SCREAMING_SNAKE_CASE							=    1
       SCREAMING_SNAKE_CASE							=    5

       # tx_args
       SCREAMING_SNAKE_CASE							=    3e-5
       SCREAMING_SNAKE_CASE							=    0.0
       SCREAMING_SNAKE_CASE							=    2_0_0_0_0
       SCREAMING_SNAKE_CASE							=    0.0095

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

       def _lowerCAmelCase(						self )  ->  Dict:
        os.makedirs(self.base_dir	,   exist_ok=__lowerCAmelCase )
        lowercase__				:    List[str]  =					os.path.join(self.base_dir	,   self.save_dir )
        lowercase__				:    Optional[int]  =					self.batch_size_per_device * jax.device_count()



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    4_0_9_6  # no dynamic padding on TPUs

       def __call__(						self	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    str  =					self.collate_fn(__lowerCAmelCase )
        lowercase__				:    int  =					jax.tree_util.tree_map(__lowerCAmelCase	,   __lowerCAmelCase )
        return batch

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


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


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None     ):
 if seed is not None:
  lowercase__				:    Dict  =					dataset.shuffle(seed=UpperCAmelCase     )
 for i in range(len(UpperCAmelCase     ) // batch_size     ):
  lowercase__				:    int  =					dataset[i * batch_size : (i + 1) * batch_size]
  yield dict(UpperCAmelCase     )
@partial(jax.pmap		,     axis_name='''batch'''     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase     ):
 def loss_fn(UpperCAmelCase     ):
  lowercase__				:    List[Any]  =					model_inputs.pop('''start_labels'''     )
  lowercase__				:    Union[str, Any]  =					model_inputs.pop('''end_labels'''     )
  lowercase__				:    Any  =					model_inputs.pop('''pooled_labels'''     )

  lowercase__				:    Any  =					state.apply_fn(**UpperCAmelCase		,     params=UpperCAmelCase		,     dropout_rng=UpperCAmelCase		,     train=UpperCAmelCase     )
  lowercase__ ,							lowercase__ ,							lowercase__				:    Any  =					outputs

  return state.loss_fn(
      UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )

 lowercase__ ,							lowercase__				:    str  =					jax.random.split(UpperCAmelCase     )
 lowercase__				:    List[str]  =					jax.value_and_grad(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    List[str]  =					grad_fn(state.params     )
 lowercase__				:    str  =					jax.lax.pmean({'''loss''': loss}		,     axis_name='''batch'''     )
 lowercase__				:    Optional[int]  =					jax.lax.pmean(UpperCAmelCase		,     '''batch'''     )

 lowercase__				:    Optional[Any]  =					state.apply_gradients(grads=UpperCAmelCase     )
 return state, metrics, new_drp_rng
@partial(jax.pmap		,     axis_name='''batch'''     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     **UpperCAmelCase     ):
 lowercase__				:    List[Any]  =					model_inputs.pop('''start_labels'''     )
 lowercase__				:    Optional[Any]  =					model_inputs.pop('''end_labels'''     )
 lowercase__				:    Union[str, Any]  =					model_inputs.pop('''pooled_labels'''     )

 lowercase__				:    Optional[Any]  =					state.apply_fn(**UpperCAmelCase		,     params=state.params		,     train=UpperCAmelCase     )
 lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					outputs

 lowercase__				:    Tuple  =					state.loss_fn(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Dict  =					jax.lax.pmean({'''loss''': loss}		,     axis_name='''batch'''     )
 return metrics



class 			UpperCAmelCase  (						train_state.TrainState					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    struct.field(pytree_node=a__					)



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    None

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  Dict:
        lowercase__				:    Tuple  =					model.params
        lowercase__				:    List[str]  =					TrainState.create(
            apply_fn=model.__call__	,   params=__lowerCAmelCase	,   tx=__lowerCAmelCase	,   loss_fn=__lowerCAmelCase	,   )
        if ckpt_dir is not None:
         lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    str  =					restore_checkpoint(__lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    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,
         }
         lowercase__ ,							lowercase__				:    List[Any]  =					build_tx(**__lowerCAmelCase )
         lowercase__				:    Optional[int]  =					train_state.TrainState(
             step=__lowerCAmelCase	,   apply_fn=model.__call__	,   params=__lowerCAmelCase	,   tx=__lowerCAmelCase	,   opt_state=__lowerCAmelCase	,   )
         lowercase__				:    Dict  =					args
         lowercase__				:    int  =					data_collator
         lowercase__				:    Dict  =					lr
         lowercase__				:    str  =					params
        lowercase__				:    str  =					jax_utils.replicate(__lowerCAmelCase )
        return state

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:
        lowercase__				:    Any  =					self.args
        lowercase__				:    Tuple  =					len(__lowerCAmelCase ) // args.batch_size

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

           lowercase__				:    Any  =					self.evaluate(__lowerCAmelCase	,   __lowerCAmelCase )
           lowercase__				:    int  =					{
               '''step''': state_step.item(),
               '''eval_loss''': eval_loss.item(),
               '''tr_loss''': tr_loss,
               '''lr''': lr.item(),
           }
           tqdm.write(str(__lowerCAmelCase ) )
           self.logger.log(__lowerCAmelCase	,   commit=__lowerCAmelCase )

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

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


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


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

 with open(os.path.join(UpperCAmelCase		,     '''opt_state.msgpack'''     )		,     '''rb'''     ) as f:
  lowercase__				:    Dict  =					from_bytes(state.opt_state		,     f.read()     )

 lowercase__				:    Dict  =					joblib.load(os.path.join(UpperCAmelCase		,     '''args.joblib'''     )     )
 lowercase__				:    Optional[Any]  =					joblib.load(os.path.join(UpperCAmelCase		,     '''data_collator.joblib'''     )     )

 with open(os.path.join(UpperCAmelCase		,     '''training_state.json'''     )		,     '''r'''     ) as f:
  lowercase__				:    Optional[Any]  =					json.load(UpperCAmelCase     )
 lowercase__				:    int  =					training_state['''step''']

 print('''DONE'''     )
 return params, opt_state, step, args, data_collator
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Union[str, Any]  =					num_train_steps - warmup_steps
 lowercase__				:    Union[str, Any]  =					optax.linear_schedule(init_value=UpperCAmelCase		,     end_value=UpperCAmelCase		,     transition_steps=UpperCAmelCase     )
 lowercase__				:    List[str]  =					optax.linear_schedule(init_value=UpperCAmelCase		,     end_value=1E-7		,     transition_steps=UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					optax.join_schedules(schedules=[warmup_fn, decay_fn]		,     boundaries=[warmup_steps]     )
 return lr





def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 def weight_decay_mask(UpperCAmelCase     ):
  lowercase__				:    List[str]  =					traverse_util.flatten_dict(UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					{k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
  return traverse_util.unflatten_dict(UpperCAmelCase     )

 lowercase__				:    Dict  =					scheduler_fn(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

 lowercase__				:    int  =					optax.adamw(learning_rate=UpperCAmelCase		,     weight_decay=UpperCAmelCase		,     mask=UpperCAmelCase     )
 return tx, lr


'''simple docstring'''

from __future__ import annotations

from random import random



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = None )  ->  str:
        lowercase__				:    Any  =					value
        lowercase__				:    Union[str, Any]  =					random()
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None

       def __repr__(						self )  ->  str:
        from pprint import pformat

        if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
        else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}	,   indent=1 )


       def __str__(						self )  ->  str:
        lowercase__				:    Any  =					str(self.value ) + ''' '''
        lowercase__				:    str  =					str(self.left or '''''' )
        lowercase__				:    List[str]  =					str(self.right or '''''' )
        return value + left + right


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if root is None:  # None tree is split into 2 Nones
  return None, None
 elif root.value is None:
  return None, None
 else:
  if value < root.value:
   lowercase__ ,							lowercase__				:    int  =					split(root.left		,     UpperCAmelCase     )
   return left, root
  else:
   lowercase__ ,							lowercase__				:    Optional[Any]  =					split(root.right		,     UpperCAmelCase     )
   return root, right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if (not left) or (not right):  # If one node is None, return the other
  return left or right
 elif left.prior < right.prior:
  lowercase__				:    Any  =					merge(left.right		,     UpperCAmelCase     )
  return left
 else:
  lowercase__				:    Dict  =					merge(UpperCAmelCase		,     right.left     )
  return right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					Node(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Tuple  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(merge(UpperCAmelCase		,     UpperCAmelCase     )		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Dict  =					split(UpperCAmelCase		,     value - 1     )
 lowercase__ ,							lowercase__				:    int  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not root:  # None
  return
 else:
  inorder(root.left     )
  print(root.value		,     end=''','''     )
  inorder(root.right     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 for arg in args.split():
  if arg[0] == "+":
   lowercase__				:    Tuple  =					insert(UpperCAmelCase		,     int(arg[1:]     )     )

  elif arg[0] == "-":
   lowercase__				:    Union[str, Any]  =					erase(UpperCAmelCase		,     int(arg[1:]     )     )

  else:
   print('''Unknown command'''     )

 return root
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					None
 print(
     '''enter numbers to create a tree, + value to add value into treap, '''
     '''- value to erase all nodes with value. \'q\' to quit. '''     )

 lowercase__				:    Dict  =					input()
 while args != "q":
  lowercase__				:    Any  =					interact_treap(UpperCAmelCase		,     UpperCAmelCase     )
  print(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					input()

 print('''good by!'''     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

