
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        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		BigBirdConfig
      _A    :		jnp.dtype     						=  jnp.floataa
      _A    :		bool     						=  True
      def A_      (      self			):
          super().setup()
          snake_case__    =					nn.Dense(5		,						dtype=self.dtype			)



      def __call__(      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					super().__call__(*lowerCamelCase		,						**lowerCamelCase			)
          snake_case__    =					self.cls(outputs[2]			)
          return outputs[:2] + (cls_out,)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		List[str]     						=  FlaxBigBirdForNaturalQuestionsModule





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

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




@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		str     						=  "google/bigbird-roberta-base"
      _A    :		int     						=  30_00
      _A    :		int     						=  1_05_00

      _A    :		int     						=  1_28
      _A    :		int     						=  3

      _A    :		int     						=  1
      _A    :		int     						=  5

      # tx_args
      _A    :		float     						=  3e-5
      _A    :		float     						=  0.0
      _A    :		int     						=  2_00_00
      _A    :		float     						=  0.0_0_9_5

      _A    :		str     						=  "bigbird-roberta-natural-questions"
      _A    :		str     						=  "training-expt"
      _A    :		str     						=  "data/nq-training.jsonl"
      _A    :		str     						=  "data/nq-validation.jsonl"
      def A_      (      self			):
          os.makedirs(self.base_dir		,						exist_ok=lowerCamelCase			)
          snake_case__    =					os.path.join(self.base_dir		,						self.save_dir			)
          snake_case__    =					self.batch_size_per_device * jax.device_count()





@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		int
      _A    :		int     						=  40_96  # no dynamic padding on TPUs
      def __call__(      self		,						lowerCamelCase			):
          snake_case__    =					self.collate_fn(lowerCamelCase			)
          snake_case__    =					jax.tree_util.tree_map(lowerCamelCase		,						lowerCamelCase			)
          return batch
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    ,       snake_case__    =					self.fetch_inputs(features["input_ids"]			)
          snake_case__    =					{
              "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 A_      (      self		,						lowerCamelCase			):
          snake_case__    =					[self._fetch_inputs(lowerCamelCase			) for ids in input_ids]
          return zip(*lowerCamelCase			)



      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					[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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=None				):
    if seed is not None:
        snake_case__    =					dataset.shuffle(seed=__lowerCAmelCase				)
    for i in range(len(__lowerCAmelCase				) // batch_size				):
        snake_case__    =					dataset[i * batch_size : (i + 1) * batch_size]
        yield dict(__lowerCAmelCase				)





@partial(jax.pmap     ,		axis_name="batch"				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		**__lowerCAmelCase				):
    def loss_fn(__lowerCAmelCase				):
        snake_case__    =					model_inputs.pop("start_labels"				)
        snake_case__    =					model_inputs.pop("end_labels"				)
        snake_case__    =					model_inputs.pop("pooled_labels"				)

        snake_case__    =					state.apply_fn(**__lowerCAmelCase     ,		params=__lowerCAmelCase     ,		dropout_rng=__lowerCAmelCase     ,		train=__lowerCAmelCase				)
        snake_case__    ,       snake_case__    ,       snake_case__    =					outputs

        return state.loss_fn(
            __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		)

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

    snake_case__    =					state.apply_gradients(grads=__lowerCAmelCase				)
    return state, metrics, new_drp_rng





@partial(jax.pmap     ,		axis_name="batch"				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		**__lowerCAmelCase				):
    snake_case__    =					model_inputs.pop("start_labels"				)
    snake_case__    =					model_inputs.pop("end_labels"				)
    snake_case__    =					model_inputs.pop("pooled_labels"				)

    snake_case__    =					state.apply_fn(**__lowerCAmelCase     ,		params=state.params     ,		train=__lowerCAmelCase				)
    snake_case__    ,       snake_case__    ,       snake_case__    =					outputs

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




class        _SCREAMING_SNAKE_CASE	(   train_state.TrainState			):
      _A    :		Callable     						=  struct.field(pytree_node=__UpperCamelCase			)





@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		Args
      _A    :		Callable
      _A    :		Callable
      _A    :		Callable
      _A    :		Callable
      _A    :		wandb
      _A    :		Callable     						=  None
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase=None			):
          snake_case__    =					model.params
          snake_case__    =					TrainState.create(
              apply_fn=model.__call__		,						params=lowerCamelCase		,						tx=lowerCamelCase		,						loss_fn=lowerCamelCase		,						)
          if ckpt_dir is not None:
              snake_case__    ,       snake_case__    ,       snake_case__    ,       snake_case__    ,       snake_case__    =					restore_checkpoint(lowerCamelCase		,						lowerCamelCase			)
              snake_case__    =					{
                  "lr": args.lr,
                  "init_lr": args.init_lr,
                  "warmup_steps": args.warmup_steps,
                  "num_train_steps": num_train_steps,
                  "weight_decay": args.weight_decay,
              }
              snake_case__    ,       snake_case__    =					build_tx(**lowerCamelCase			)
              snake_case__    =					train_state.TrainState(
                  step=lowerCamelCase		,						apply_fn=model.__call__		,						params=lowerCamelCase		,						tx=lowerCamelCase		,						opt_state=lowerCamelCase		,						)
              snake_case__    =					args
              snake_case__    =					data_collator
              snake_case__    =					lr
              snake_case__    =					params
          snake_case__    =					jax_utils.replicate(lowerCamelCase			)
          return state
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.args
          snake_case__    =					len(lowerCamelCase			) // args.batch_size

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

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



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    print(F"""RESTORING CHECKPOINT FROM {save_dir}"""     ,		end=" ... "				)
    with open(os.path.join(__lowerCAmelCase     ,		"flax_model.msgpack"				)     ,		"rb"				) as f:
        snake_case__    =					from_bytes(state.params     ,		f.read()				)

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

    snake_case__    =					joblib.load(os.path.join(__lowerCAmelCase     ,		"args.joblib"				)				)
    snake_case__    =					joblib.load(os.path.join(__lowerCAmelCase     ,		"data_collator.joblib"				)				)

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

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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					num_train_steps - warmup_steps
    snake_case__    =					optax.linear_schedule(init_value=__lowerCAmelCase     ,		end_value=__lowerCAmelCase     ,		transition_steps=__lowerCAmelCase				)
    snake_case__    =					optax.linear_schedule(init_value=__lowerCAmelCase     ,		end_value=1E-7     ,		transition_steps=__lowerCAmelCase				)
    snake_case__    =					optax.join_schedules(schedules=[warmup_fn, decay_fn]     ,		boundaries=[warmup_steps]				)
    return lr





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

    snake_case__    =					scheduler_fn(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)

    snake_case__    =					optax.adamw(learning_rate=__lowerCAmelCase     ,		weight_decay=__lowerCAmelCase     ,		mask=__lowerCAmelCase				)
    return tx, lr





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



