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__    (		snake_case					):



  """simple docstring"""






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






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




    '''simple docstring'''




    super().setup()
    __UpperCAmelCase						    =       nn.Dense(5				,				dtype=self.dtype   )






  def __call__(				self:			Union[str, Any]				,				*__lowerCAmelCase:			int				,				**__lowerCAmelCase:			Tuple   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       super().__call__(*__lowerCAmelCase				,				**__lowerCAmelCase   )
    __UpperCAmelCase						    =       self.cls(outputs[2]   )
    return outputs[:2] + (cls_out,)






class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	int    		= FlaxBigBirdForNaturalQuestionsModule






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

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


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	str    		= "google/bigbird-roberta-base"
  lowerCAmelCase__		:	int    		= 3000
  lowerCAmelCase__		:	int    		= 1_0500

  lowerCAmelCase__		:	int    		= 128
  lowerCAmelCase__		:	int    		= 3

  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	int    		= 5

  # tx_args
  lowerCAmelCase__		:	float    		= 3E-5
  lowerCAmelCase__		:	float    		= 0.0
  lowerCAmelCase__		:	int    		= 2_0000
  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 	_UpperCAmelCase				(				self:			Tuple   ) -> Optional[Any]:




    '''simple docstring'''




    os.makedirs(self.base_dir				,				exist_ok=__lowerCAmelCase   )
    __UpperCAmelCase						    =       os.path.join(self.base_dir				,				self.save_dir   )
    __UpperCAmelCase						    =       self.batch_size_per_device * jax.device_count()






@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	int
  lowerCAmelCase__		:	int    		= 4096  # no dynamic padding on TPUs






  def __call__(				self:			Any				,				__lowerCAmelCase:			Optional[int]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.collate_fn(__lowerCAmelCase   )
    __UpperCAmelCase						    =       jax.tree_util.tree_map(__lowerCAmelCase				,				__lowerCAmelCase   )
    return batch






  def 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			Optional[int]   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.fetch_inputs(features["input_ids"]   )
    __UpperCAmelCase						    =       {
        "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 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			list   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       [self._fetch_inputs(__lowerCAmelCase   ) for ids in input_ids]
    return zip(*__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			list   ) -> Optional[int]:




    '''simple docstring'''




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




@partial(jax.pmap						,	axis_name="batch"					)
def       __lowerCAmelCase      (						A_   : int						,	A_   : List[Any]						,	**A_   : int					)    ->  Optional[Any]:
  def loss_fn(A_   : List[Any]					):
    __UpperCAmelCase						    =       model_inputs.pop("start_labels"					)
    __UpperCAmelCase						    =       model_inputs.pop("end_labels"					)
    __UpperCAmelCase						    =       model_inputs.pop("pooled_labels"					)

    __UpperCAmelCase						    =       state.apply_fn(**A_						,	params=A_						,	dropout_rng=A_						,	train=A_					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       outputs

    return state.loss_fn(
        A_						,	A_						,	A_						,	A_						,	A_						,	A_						,	)

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

  __UpperCAmelCase						    =       state.apply_gradients(grads=A_					)
  return state, metrics, new_drp_rng




@partial(jax.pmap						,	axis_name="batch"					)
def       __lowerCAmelCase      (						A_   : Any						,	**A_   : Tuple					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       model_inputs.pop("start_labels"					)
  __UpperCAmelCase						    =       model_inputs.pop("end_labels"					)
  __UpperCAmelCase						    =       model_inputs.pop("pooled_labels"					)

  __UpperCAmelCase						    =       state.apply_fn(**A_						,	params=state.params						,	train=A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       outputs

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


class 			UpperCAmelCase__    (		train_state.TrainState					):



  """simple docstring"""






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






@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






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






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Tuple=None   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       model.params
    __UpperCAmelCase						    =       TrainState.create(
        apply_fn=model.__call__				,				params=__lowerCAmelCase				,				tx=__lowerCAmelCase				,				loss_fn=__lowerCAmelCase				,				)
    if ckpt_dir is not None:
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       restore_checkpoint(__lowerCAmelCase				,				__lowerCAmelCase   )
      __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(**__lowerCAmelCase   )
      __UpperCAmelCase						    =       train_state.TrainState(
          step=__lowerCAmelCase				,				apply_fn=model.__call__				,				params=__lowerCAmelCase				,				tx=__lowerCAmelCase				,				opt_state=__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       args
      __UpperCAmelCase						    =       data_collator
      __UpperCAmelCase						    =       lr
      __UpperCAmelCase						    =       params
    __UpperCAmelCase						    =       jax_utils.replicate(__lowerCAmelCase   )
    return state






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			int   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.args
    __UpperCAmelCase						    =       len(__lowerCAmelCase   ) // args.batch_size

    __UpperCAmelCase						    =       jax.random.PRNGKey(0   )
    __UpperCAmelCase						    =       jax.random.split(__lowerCAmelCase				,				jax.device_count()   )
    for epoch in range(args.max_epochs   ):
      __UpperCAmelCase						    =       jnp.array(0				,				dtype=jnp.floataa   )
      __UpperCAmelCase						    =       get_batched_dataset(__lowerCAmelCase				,				args.batch_size				,				seed=__lowerCAmelCase   )
      __UpperCAmelCase						    =       0
      for batch in tqdm(__lowerCAmelCase				,				total=__lowerCAmelCase				,				desc=F'''Running EPOCH-{epoch}'''   ):
        __UpperCAmelCase						    =       self.data_collator(__lowerCAmelCase   )
        __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       self.train_step_fn(__lowerCAmelCase				,				__lowerCAmelCase				,				**__lowerCAmelCase   )
        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(__lowerCAmelCase				,				__lowerCAmelCase   )
          __UpperCAmelCase						    =       {
              "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 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			Optional[Any]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       get_batched_dataset(__lowerCAmelCase				,				self.args.batch_size   )
    __UpperCAmelCase						    =       len(__lowerCAmelCase   ) // self.args.batch_size
    __UpperCAmelCase						    =       jnp.array(0				,				dtype=jnp.floataa   )
    __UpperCAmelCase						    =       0
    for batch in tqdm(__lowerCAmelCase				,				total=__lowerCAmelCase				,				desc="Evaluating ... "   ):
      __UpperCAmelCase						    =       self.data_collator(__lowerCAmelCase   )
      __UpperCAmelCase						    =       self.val_step_fn(__lowerCAmelCase				,				**__lowerCAmelCase   )
      running_loss += jax_utils.unreplicate(metrics["loss"]   )
      i += 1
    return running_loss / i






  def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[Any]   ) -> List[Any]:




    '''simple docstring'''




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

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

  __UpperCAmelCase						    =       joblib.load(os.path.join(A_						,	"args.joblib"					)					)
  __UpperCAmelCase						    =       joblib.load(os.path.join(A_						,	"data_collator.joblib"					)					)

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

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




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





def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : str						,	A_   : Dict						,	A_   : Any						,	A_   : Any					)    ->  Optional[Any]:
  def weight_decay_mask(A_   : str					):
    __UpperCAmelCase						    =       traverse_util.flatten_dict(A_					)
    __UpperCAmelCase						    =       {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
    return traverse_util.unflatten_dict(A_					)

  __UpperCAmelCase						    =       scheduler_fn(A_						,	A_						,	A_						,	A_					)

  __UpperCAmelCase						    =       optax.adamw(learning_rate=A_						,	weight_decay=A_						,	mask=A_					)
  return tx, lr




from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  @staticmethod
  @abstractmethod
  def 	_UpperCAmelCase				(				__lowerCAmelCase:			ArgumentParser   ) -> Tuple:




    '''simple docstring'''




    raise NotImplementedError()






  @abstractmethod
  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[Any]:




    '''simple docstring'''




    raise NotImplementedError()



