
"""simple docstring"""




from collections.abc import Sequence
from queue import Queue

class 		a__						:
   def __init__(			self	,    _a	,    _a	,    _a	,    _a=None	,    _a=None				):
          lowercase    :				Any       		=							start
          lowercase    :				Tuple       		=							end
          lowercase    :				List[Any]       		=							val
          lowercase    :				Any       		=							(start + end) // 2
          lowercase    :				Tuple       		=							left
          lowercase    :				Tuple       		=							right

   def __repr__(			self				):
          return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""


class 		a__						:
   def __init__(			self	,    _a	,    _a				):
          lowercase    :				Optional[Any]       		=							collection
          lowercase    :				int       		=							function
          if self.collection:
                 lowercase    :				Optional[Any]       		=							self._build_tree(0	,    len(_a				) - 1				)
   def 			__magic_name__			(			self	,    _a	,    _a				):
          self._update_tree(self.root	,    _a	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a				):
          return self._query_range(self.root	,    _a	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a				):





          if start == end:
                 return SegmentTreeNode(_a	,    _a	,    self.collection[start]				)
          lowercase    :				Any       		=							(start + end) // 2
          lowercase    :				List[Any]       		=							self._build_tree(_a	,    _a				)
          lowercase    :				int       		=							self._build_tree(mid + 1	,    _a				)
          return SegmentTreeNode(_a	,    _a	,    self.fn(left.val	,    right.val				)	,    _a	,    _a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):





          if node.start == i and node.end == i:
                 lowercase    :				Optional[Any]       		=							val
                 return
          if i <= node.mid:
                 self._update_tree(node.left	,    _a	,    _a				)
          else:
                 self._update_tree(node.right	,    _a	,    _a				)
          lowercase    :				str       		=							self.fn(node.left.val	,    node.right.val				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):





          if node.start == i and node.end == j:
                 return node.val

          if i <= node.mid:
                 if j <= node.mid:
                        # range in left child tree
                        return self._query_range(node.left	,    _a	,    _a				)
                 else:
                        # range in left child tree and right child tree
                        return self.fn(
                            self._query_range(node.left	,    _a	,    node.mid				)	,    self._query_range(node.right	,    node.mid + 1	,    _a				)	,    )
          else:
                 # range in right child tree
                 return self._query_range(node.right	,    _a	,    _a				)

   def 			__magic_name__			(			self				):





          if self.root is not None:
                 lowercase    :				int       		=							Queue()
                 queue.put(self.root				)
                 while not queue.empty():
                        lowercase    :				int       		=							queue.get()
                        yield node

                        if node.left is not None:
                               queue.put(node.left				)

                        if node.right is not None:
                               queue.put(node.right				)


if __name__ == "__main__":
   import operator

   for fn in [operator.add, max, min]:
      print("""*""" * 50)
      _A			:       str   		=       SegmentTree([2, 1, 5, 3, 4], fn)
      for node in arr.traverse():
         print(node)
      print()

      arr.update(1, 5)
      for node in arr.traverse():
         print(node)
      print()

      print(arr.query_range(3, 4))  # 7
      print(arr.query_range(2, 2))  # 5
      print(arr.query_range(1, 3))  # 13
      print()



"""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    __snake_case       (     __lowerCAmelCase				):
      a__        =		42
      a__        =		jnp.floataa
      a__        =		True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        super().setup()
        a__:     int										=	nn.Dense(5    ,			dtype=self.dtype)



      def __call__(						self    ,			*lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	super().__call__(*lowercase    ,			**lowercase)
        a__:     str										=	self.cls(outputs[2])
        return outputs[:2] + (cls_out,)







class    __snake_case       (     __lowerCAmelCase				):
      a__        =		FlaxBigBirdForNaturalQuestionsModule







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

  a__:     Tuple										=	partial(_SCREAMING_SNAKE_CASE		,     reduction=jnp.mean   )
  a__:     List[str]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Union[str, Any]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Any										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class    __snake_case       :
      a__        =		"google/bigbird-roberta-base"
      a__        =		3000
      a__        =		1_0500

      a__        =		128
      a__        =		3

      a__        =		1
      a__        =		5

      # tx_args
      a__        =		3e-5
      a__        =		0.0
      a__        =		2_0000
      a__        =		0.0095

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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        os.makedirs(self.base_dir    ,			exist_ok=lowercase)
        a__:     str										=	os.path.join(self.base_dir    ,			self.save_dir)
        a__:     List[str]										=	self.batch_size_per_device * jax.device_count()







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		4096  # no dynamic padding on TPUs



      def __call__(						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.collate_fn(lowercase)
        a__:     Optional[int]										=	jax.tree_util.tree_map(lowercase    ,			lowercase)
        return batch



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__							,    a__:     Dict										=	self.fetch_inputs(features['input_ids'])
        a__:     List[Any]										=	{
            '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   lowerCamelCase_   (						self    ,			lowercase)	->			List[str]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





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







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Any:
  def loss_fn(_SCREAMING_SNAKE_CASE   ):
    a__:     str										=	model_inputs.pop('start_labels'   )
    a__:     Dict										=	model_inputs.pop('end_labels'   )
    a__:     Optional[int]										=	model_inputs.pop('pooled_labels'   )

    a__:     Optional[Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=_SCREAMING_SNAKE_CASE		,     dropout_rng=_SCREAMING_SNAKE_CASE		,     train=_SCREAMING_SNAKE_CASE   )
    a__							,    a__							,    a__:     Optional[int]										=	outputs

    return state.loss_fn(
        _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     )

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

  a__:     Union[str, Any]										=	state.apply_gradients(grads=_SCREAMING_SNAKE_CASE   )
  return state, metrics, new_drp_rng







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     Optional[int]										=	model_inputs.pop('start_labels'   )
  a__:     int										=	model_inputs.pop('end_labels'   )
  a__:     Dict										=	model_inputs.pop('pooled_labels'   )

  a__:     Union[str, Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=state.params		,     train=_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     int										=	outputs

  a__:     Optional[int]										=	state.loss_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	jax.lax.pmean({'loss': loss}		,     axis_name='batch'   )
  return metrics




class    __snake_case       (     train_state.TrainState				):
      a__        =		struct.field(pytree_node=__lowerCAmelCase				)







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		None



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	model.params
        a__:     Any										=	TrainState.create(
            apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			loss_fn=lowercase    ,			)
        if ckpt_dir is not None:
          a__							,    a__							,    a__							,    a__							,    a__:     Any										=	restore_checkpoint(lowercase    ,			lowercase)
          a__:     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__:     str										=	build_tx(**lowercase)
          a__:     Optional[Any]										=	train_state.TrainState(
              step=lowercase    ,			apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			opt_state=lowercase    ,			)
          a__:     int										=	args
          a__:     Union[str, Any]										=	data_collator
          a__:     Any										=	lr
          a__:     Dict										=	params
        a__:     Tuple										=	jax_utils.replicate(lowercase)
        return state



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	self.args
        a__:     str										=	len(lowercase) // args.batch_size

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

              a__:     List[Any]										=	self.evaluate(lowercase    ,			lowercase)
              a__:     List[str]										=	{
                  '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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	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		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  print(F'RESTORING CHECKPOINT FROM {save_dir}'		,     end=' ... '   )
  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'flax_model.msgpack'   )		,     'rb'   ) as f:
    a__:     int										=	from_bytes(state.params		,     f.read()   )

  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'opt_state.msgpack'   )		,     'rb'   ) as f:
    a__:     Optional[Any]										=	from_bytes(state.opt_state		,     f.read()   )

  a__:     Optional[Any]										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'args.joblib'   )   )
  a__:     int										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'data_collator.joblib'   )   )

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

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__:     str										=	num_train_steps - warmup_steps
  a__:     str										=	optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE		,     end_value=_SCREAMING_SNAKE_CASE		,     transition_steps=_SCREAMING_SNAKE_CASE   )
  a__:     List[Any]										=	optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE		,     end_value=1e-7		,     transition_steps=_SCREAMING_SNAKE_CASE   )
  a__:     int										=	optax.join_schedules(schedules=[warmup_fn, decay_fn]		,     boundaries=[warmup_steps]   )
  return lr







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

  a__:     List[str]										=	scheduler_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     Any										=	optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE		,     weight_decay=_SCREAMING_SNAKE_CASE		,     mask=_SCREAMING_SNAKE_CASE   )
  return tx, lr


