




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						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: BigBirdConfig
      lowerCAmelCase__			: jnp.dtype    =					jnp.floataa
      lowerCAmelCase__			: bool    =					True







      def       snake_case	(     self					: List[str]				):
          super().setup()
          __UpperCamelCase  		=						nn.Dense(5 ,       dtype=self.dtype				)







      def __call__(     self					: Optional[int] ,       *snake_case					: List[str] ,       **snake_case					: Any				):
          __UpperCamelCase  		=						super().__call__(*snake_case ,       **snake_case				)
          __UpperCamelCase  		=						self.cls(outputs[2]				)
          return outputs[:2] + (cls_out,)







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Tuple    =					FlaxBigBirdForNaturalQuestionsModule







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


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

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

@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: str    =					"google/bigbird-roberta-base"
      lowerCAmelCase__			: int    =					30_00
      lowerCAmelCase__			: int    =					1_05_00

      lowerCAmelCase__			: int    =					1_28
      lowerCAmelCase__			: int    =					3

      lowerCAmelCase__			: int    =					1
      lowerCAmelCase__			: int    =					5

      # tx_args
      lowerCAmelCase__			: float    =					3E-5
      lowerCAmelCase__			: float    =					0.0
      lowerCAmelCase__			: int    =					2_00_00
      lowerCAmelCase__			: float    =					0.0_095

      lowerCAmelCase__			: str    =					"bigbird-roberta-natural-questions"
      lowerCAmelCase__			: str    =					"training-expt"
      lowerCAmelCase__			: str    =					"data/nq-training.jsonl"
      lowerCAmelCase__			: str    =					"data/nq-validation.jsonl"







      def       snake_case	(     self					: List[Any]				):
          os.makedirs(self.base_dir ,       exist_ok=snake_case				)
          __UpperCamelCase  		=						os.path.join(self.base_dir ,       self.save_dir				)
          __UpperCamelCase  		=						self.batch_size_per_device * jax.device_count()







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: int
      lowerCAmelCase__			: int    =					40_96  # no dynamic padding on TPUs







      def __call__(     self					: Any ,       snake_case					: int				):
          __UpperCamelCase  		=						self.collate_fn(snake_case				)
          __UpperCamelCase  		=						jax.tree_util.tree_map(snake_case ,       snake_case				)
          return batch







      def       snake_case	(     self					: List[str] ,       snake_case					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.fetch_inputs(features['''input_ids''']				)
          __UpperCamelCase  		=						{
              '''input_ids''': jnp.array(snake_case ,       dtype=jnp.intaa				),
              '''attention_mask''': jnp.array(snake_case ,       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					: Dict ,       snake_case					: list				):
          __UpperCamelCase  		=						[self._fetch_inputs(snake_case				) for ids in input_ids]
          return zip(*snake_case				)







      def       snake_case	(     self					: List[str] ,       snake_case					: list				):
          __UpperCamelCase  		=						[1 for _ in range(len(snake_case				)				)]
          while len(snake_case				) < self.max_length:
              input_ids.append(self.pad_id				)
              attention_mask.append(0				)
          return input_ids, attention_mask







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	List[str]:

    """simple docstring"""


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




@partial(jax.pmap					,     axis_name='''batch'''      )
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     **lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    def loss_fn(lowercase_      ):
        __UpperCamelCase  		=						model_inputs.pop('''start_labels'''      )
        __UpperCamelCase  		=						model_inputs.pop('''end_labels'''      )
        __UpperCamelCase  		=						model_inputs.pop('''pooled_labels'''      )

        __UpperCamelCase  		=						state.apply_fn(**lowercase_					,     params=lowercase_					,     dropout_rng=lowercase_					,     train=lowercase_      )
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						outputs

        return state.loss_fn(
            lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )

    __UpperCamelCase		,      __UpperCamelCase  		=						jax.random.split(lowercase_      )
    __UpperCamelCase  		=						jax.value_and_grad(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						grad_fn(state.params      )
    __UpperCamelCase  		=						jax.lax.pmean({'''loss''': loss}					,     axis_name='''batch'''      )
    __UpperCamelCase  		=						jax.lax.pmean(lowercase_					,     '''batch'''      )

    __UpperCamelCase  		=						state.apply_gradients(grads=lowercase_      )
    return state, metrics, new_drp_rng




@partial(jax.pmap					,     axis_name='''batch'''      )
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     **lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						model_inputs.pop('''start_labels'''      )
    __UpperCamelCase  		=						model_inputs.pop('''end_labels'''      )
    __UpperCamelCase  		=						model_inputs.pop('''pooled_labels'''      )

    __UpperCamelCase  		=						state.apply_fn(**lowercase_					,     params=state.params					,     train=lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						outputs

    __UpperCamelCase  		=						state.loss_fn(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						jax.lax.pmean({'''loss''': loss}					,     axis_name='''batch'''      )
    return metrics

class  _lowerCamelCase						(			train_state.TrainState   ):

      """simple docstring"""

      lowerCAmelCase__			: Callable    =					struct.field(pytree_node=_SCREAMING_SNAKE_CASE   )







@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: Args
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: Callable
      lowerCAmelCase__			: wandb
      lowerCAmelCase__			: Callable    =					None







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







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[str] ,       snake_case					: Dict ,       snake_case					: Any				):
          __UpperCamelCase  		=						self.args
          __UpperCamelCase  		=						len(snake_case				) // args.batch_size

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

                      __UpperCamelCase  		=						self.evaluate(snake_case ,       snake_case				)
                      __UpperCamelCase  		=						{
                          '''step''': state_step.item(),
                          '''eval_loss''': eval_loss.item(),
                          '''tr_loss''': tr_loss,
                          '''lr''': lr.item(),
                      }
                      tqdm.write(str(snake_case				)				)
                      self.logger.log(snake_case ,       commit=snake_case				)

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







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







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







def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


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

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

    __UpperCamelCase  		=						joblib.load(os.path.join(lowercase_					,     '''args.joblib'''      )      )
    __UpperCamelCase  		=						joblib.load(os.path.join(lowercase_					,     '''data_collator.joblib'''      )      )

    with open(os.path.join(lowercase_					,     '''training_state.json'''      )					,     '''r'''      ) as f:
        __UpperCamelCase  		=						json.load(lowercase_      )
    __UpperCamelCase  		=						training_state['''step''']

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


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

    __UpperCamelCase  		=						scheduler_fn(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						optax.adamw(learning_rate=lowercase_					,     weight_decay=lowercase_					,     mask=lowercase_      )
    return tx, lr












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






