
'''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 ):
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		jnp.floataa
    lowerCAmelCase_				    =		True



    def   lowerCamelCase_  (	self      :       Any     ):
          super().setup()
          _lowerCamelCase				:   str								=   nn.Dense(5,dtype=self.dtype     )



    def __call__(	self      :       Optional[int],*__A      :       Optional[int],**__A      :       Union[str, Any]     ):
          _lowerCamelCase				:   Optional[int]								=   super().__call__(*__A,**__A     )
          _lowerCamelCase				:   int								=   self.cls(outputs[2]     )
          return outputs[:2] + (cls_out,)


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		FlaxBigBirdForNaturalQuestionsModule







def 		A_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Dict							):



      """simple docstring"""




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

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

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		"google/bigbird-roberta-base"
    lowerCAmelCase_				    =		3000
    lowerCAmelCase_				    =		10500

    lowerCAmelCase_				    =		128
    lowerCAmelCase_				    =		3

    lowerCAmelCase_				    =		1
    lowerCAmelCase_				    =		5

    # tx_args
    lowerCAmelCase_				    =		3E-5
    lowerCAmelCase_				    =		0.0
    lowerCAmelCase_				    =		20000
    lowerCAmelCase_				    =		0.0_0_9_5

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



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          os.makedirs(self.base_dir,exist_ok=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   os.path.join(self.base_dir,self.save_dir     )
          _lowerCamelCase				:   Any								=   self.batch_size_per_device * jax.device_count()


@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		4096  # no dynamic padding on TPUs



    def __call__(	self      :       Union[str, Any],__A      :       List[str]     ):
          _lowerCamelCase				:   Dict								=   self.collate_fn(__A     )
          _lowerCamelCase				:   int								=   jax.tree_util.tree_map(__A,__A     )
          return batch



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       Optional[Any]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   self.fetch_inputs(features["input_ids"]     )
          _lowerCamelCase				:   Dict								=   {
              "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   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       list     ):
          _lowerCamelCase				:   str								=   [self._fetch_inputs(__A     ) for ids in input_ids]
          return zip(*__A     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       list     ):
          _lowerCamelCase				:   str								=   [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_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Dict=None							):



      """simple docstring"""




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







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



      """simple docstring"""




      def loss_fn(_lowerCAmelCase   :    str							):
            _lowerCamelCase				:   List[Any]								=   model_inputs.pop("start_labels"							)
            _lowerCamelCase				:   Union[str, Any]								=   model_inputs.pop("end_labels"							)
            _lowerCamelCase				:   Optional[int]								=   model_inputs.pop("pooled_labels"							)

            _lowerCamelCase				:   List[str]								=   state.apply_fn(**_lowerCAmelCase				,	params=_lowerCAmelCase				,	dropout_rng=_lowerCAmelCase				,	train=_lowerCAmelCase							)
            _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   outputs

            return state.loss_fn(
                _lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	)

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

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







@partial(jax.pmap				,	axis_name="batch"							)
def 		A_       (	_lowerCAmelCase   :    int				,	**_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[Any]								=   model_inputs.pop("start_labels"							)
      _lowerCamelCase				:   Any								=   model_inputs.pop("end_labels"							)
      _lowerCamelCase				:   Any								=   model_inputs.pop("pooled_labels"							)

      _lowerCamelCase				:   Optional[int]								=   state.apply_fn(**_lowerCAmelCase				,	params=state.params				,	train=_lowerCAmelCase							)
      _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   outputs

      _lowerCamelCase				:   List[str]								=   state.loss_fn(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   jax.lax.pmean({"loss": loss}				,	axis_name="batch"							)
      return metrics

class  UpperCAmelCase__						(       train_state.TrainState ):
    lowerCAmelCase_				    =		struct.field(pytree_node=A )


@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		None



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



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       int,__A      :       Dict,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   str								=   self.args
          _lowerCamelCase				:   Union[str, Any]								=   len(__A     ) // args.batch_size

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

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



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Optional[Any],__A      :       Union[str, Any]     ):
          _lowerCamelCase				:   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_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    List[str]							):



      """simple docstring"""




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

      with open(os.path.join(_lowerCAmelCase				,	"opt_state.msgpack"							)				,	"rb"							) as f:
            _lowerCamelCase				:   List[Any]								=   from_bytes(state.opt_state				,	f.read()							)

      _lowerCamelCase				:   Optional[int]								=   joblib.load(os.path.join(_lowerCAmelCase				,	"args.joblib"							)							)
      _lowerCamelCase				:   List[Any]								=   joblib.load(os.path.join(_lowerCAmelCase				,	"data_collator.joblib"							)							)

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

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







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   num_train_steps - warmup_steps
      _lowerCamelCase				:   Dict								=   optax.linear_schedule(init_value=_lowerCAmelCase				,	end_value=_lowerCAmelCase				,	transition_steps=_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   optax.linear_schedule(init_value=_lowerCAmelCase				,	end_value=1E-7				,	transition_steps=_lowerCAmelCase							)
      _lowerCamelCase				:   List[Any]								=   optax.join_schedules(schedules=[warmup_fn, decay_fn]				,	boundaries=[warmup_steps]							)
      return lr







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




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

      _lowerCamelCase				:   int								=   scheduler_fn(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)

      _lowerCamelCase				:   Optional[Any]								=   optax.adamw(learning_rate=_lowerCAmelCase				,	weight_decay=_lowerCAmelCase				,	mask=_lowerCAmelCase							)
      return tx, lr

'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))