


"""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 						_A  (  _a  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    List[str]):
      super().setup()
      a						:		Optional[Any]      							=			nn.Dense(5				, dtype=self.dtype)






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






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Union[str, Any]	=       FlaxBigBirdForNaturalQuestionsModule






def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







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

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






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							str	=       "google/bigbird-roberta-base"
    UpperCAmelCase       :							int	=       3_0_0_0
    UpperCAmelCase       :							int	=       1_0_5_0_0

    UpperCAmelCase       :							int	=       1_2_8
    UpperCAmelCase       :							int	=       3

    UpperCAmelCase       :							int	=       1
    UpperCAmelCase       :							int	=       5

    # tx_args
    UpperCAmelCase       :							float	=       3E-5
    UpperCAmelCase       :							float	=       0.0
    UpperCAmelCase       :							int	=       2_0_0_0_0
    UpperCAmelCase       :							float	=       0.0095

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






    def 	__snake_case							(					self				:    Tuple):
      os.makedirs(self.base_dir				, exist_ok=__UpperCAmelCase)
      a						:		Tuple      							=			os.path.join(self.base_dir				, self.save_dir)
      a						:		Any      							=			self.batch_size_per_device * jax.device_count()






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							int
    UpperCAmelCase       :							int	=       4_0_9_6  # no dynamic padding on TPUs






    def __call__(					self				:    Tuple				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		Optional[int]      							=			self.collate_fn(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			jax.tree_util.tree_map(__UpperCAmelCase				, __UpperCAmelCase)
      return batch






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Tuple):
      a							,		a						:		Union[str, Any]      							=			self.fetch_inputs(features["input_ids"])
      a						:		Dict      							=			{
          "input_ids": jnp.array(__UpperCAmelCase				, dtype=jnp.intaa),
          "attention_mask": jnp.array(__UpperCAmelCase				, 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				:    List[str]				, __UpperCAmelCase				:    list):
      a						:		Optional[int]      							=			[self._fetch_inputs(__UpperCAmelCase) for ids in input_ids]
      return zip(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    list):
      a						:		List[Any]      							=			[1 for _ in range(len(__UpperCAmelCase))]
      while len(__UpperCAmelCase) < self.max_length:
        input_ids.append(self.pad_id)
        attention_mask.append(0)
      return input_ids, attention_mask






def      lowercase			(				A_  ,   A_  ,   A_=None       )->      Tuple:
  '''simple docstring'''







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


@partial(jax.pmap  ,   axis_name="batch"       )
def      lowercase			(				A_  ,   A_  ,   **A_       )->      List[str]:
  '''simple docstring'''







  def loss_fn(A_       ):
    a						:		Tuple      							=			model_inputs.pop("start_labels"       )
    a						:		List[str]      							=			model_inputs.pop("end_labels"       )
    a						:		Union[str, Any]      							=			model_inputs.pop("pooled_labels"       )

    a						:		Tuple      							=			state.apply_fn(**A_  ,   params=A_  ,   dropout_rng=A_  ,   train=A_       )
    a							,		a							,		a						:		List[Any]      							=			outputs

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

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

  a						:		List[Any]      							=			state.apply_gradients(grads=A_       )
  return state, metrics, new_drp_rng


@partial(jax.pmap  ,   axis_name="batch"       )
def      lowercase			(				A_  ,   **A_       )->      str:
  '''simple docstring'''







  a						:		Dict      							=			model_inputs.pop("start_labels"       )
  a						:		str      							=			model_inputs.pop("end_labels"       )
  a						:		Any      							=			model_inputs.pop("pooled_labels"       )

  a						:		Any      							=			state.apply_fn(**A_  ,   params=state.params  ,   train=A_       )
  a							,		a							,		a						:		str      							=			outputs

  a						:		Dict      							=			state.loss_fn(A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_       )
  a						:		List[str]      							=			jax.lax.pmean({"loss": loss}  ,   axis_name="batch"       )
  return metrics






class 						_A  (  train_state.TrainState  ):







    """simple docstring"""




    UpperCAmelCase       :							Callable	=       struct.field(pytree_node=_a  )






@dataclass
class 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							Args
    UpperCAmelCase       :							Callable
    UpperCAmelCase       :							Callable
    UpperCAmelCase       :							Callable
    UpperCAmelCase       :							Callable
    UpperCAmelCase       :							wandb
    UpperCAmelCase       :							Callable	=       None






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






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		Optional[int]      							=			self.args
      a						:		int      							=			len(__UpperCAmelCase) // args.batch_size

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

            a						:		int      							=			self.evaluate(__UpperCAmelCase				, __UpperCAmelCase)
            a						:		int      							=			{
                "step": state_step.item(),
                "eval_loss": eval_loss.item(),
                "tr_loss": tr_loss,
                "lr": lr.item(),
            }
            tqdm.write(str(__UpperCAmelCase))
            self.logger.log(__UpperCAmelCase				, commit=__UpperCAmelCase)

          if i % args.save_steps == 0:
            self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}'''				, state=__UpperCAmelCase)






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






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






def      lowercase			(				A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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

  with open(os.path.join(A_  ,   "opt_state.msgpack"       )  ,   "rb"       ) as f:
    a						:		List[str]      							=			from_bytes(state.opt_state  ,   f.read()       )

  a						:		Any      							=			joblib.load(os.path.join(A_  ,   "args.joblib"       )       )
  a						:		Tuple      							=			joblib.load(os.path.join(A_  ,   "data_collator.joblib"       )       )

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

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


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      List[str]:
  '''simple docstring'''







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







def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      List[Any]:
  '''simple docstring'''







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

  a						:		Dict      							=			scheduler_fn(A_  ,   A_  ,   A_  ,   A_       )

  a						:		str      							=			optax.adamw(learning_rate=A_  ,   weight_decay=A_  ,   mask=A_       )
  return tx, lr










"""simple docstring"""







import unittest

from knapsack import knapsack as k






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    List[Any]):
      a						:		str      							=			0
      a						:		Optional[int]      							=			[0]
      a						:		Union[str, Any]      							=			[0]
      a						:		Any      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 0)

      a						:		List[str]      							=			[60]
      a						:		str      							=			[10]
      a						:		Optional[int]      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 0)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			3
      a						:		str      							=			[1, 2, 3]
      a						:		Tuple      							=			[3, 2, 1]
      a						:		Any      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 5)






    def 	__snake_case							(					self				:    Tuple):
      a						:		int      							=			50
      a						:		List[Any]      							=			[60, 100, 120]
      a						:		Optional[int]      							=			[10, 20, 30]
      a						:		str      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 220)


if __name__ == "__main__":
 unittest.main()






