"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__					       =	{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__					       =	['MvpTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__					       =	[
     'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MvpForCausalLM',
     'MvpForConditionalGeneration',
     'MvpForQuestionAnswering',
     'MvpForSequenceClassification',
     'MvpModel',
     'MvpPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
 from .tokenization_mvp import MvpTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mvp_fast import MvpTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mvp import (
      MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
      MvpForCausalLM,
      MvpForConditionalGeneration,
      MvpForQuestionAnswering,
      MvpForSequenceClassification,
      MvpModel,
      MvpPreTrainedModel,
  )

else:
 import sys

 lowercase__					       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



"""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    __snake_case       (     __lowerCAmelCase				):
      a__        =		42
      a__        =		jnp.floataa
      a__        =		True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        super().setup()
        a__:     int										=	nn.Dense(5    ,			dtype=self.dtype)



      def __call__(						self    ,			*lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	super().__call__(*lowercase    ,			**lowercase)
        a__:     str										=	self.cls(outputs[2])
        return outputs[:2] + (cls_out,)







class    __snake_case       (     __lowerCAmelCase				):
      a__        =		FlaxBigBirdForNaturalQuestionsModule







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

  a__:     Tuple										=	partial(_SCREAMING_SNAKE_CASE		,     reduction=jnp.mean   )
  a__:     List[str]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Union[str, Any]										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Any										=	cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class    __snake_case       :
      a__        =		"google/bigbird-roberta-base"
      a__        =		3000
      a__        =		1_0500

      a__        =		128
      a__        =		3

      a__        =		1
      a__        =		5

      # tx_args
      a__        =		3e-5
      a__        =		0.0
      a__        =		2_0000
      a__        =		0.0095

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



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





        '''simple docstring'''





        os.makedirs(self.base_dir    ,			exist_ok=lowercase)
        a__:     str										=	os.path.join(self.base_dir    ,			self.save_dir)
        a__:     List[str]										=	self.batch_size_per_device * jax.device_count()







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		4096  # no dynamic padding on TPUs



      def __call__(						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.collate_fn(lowercase)
        a__:     Optional[int]										=	jax.tree_util.tree_map(lowercase    ,			lowercase)
        return batch



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__							,    a__:     Dict										=	self.fetch_inputs(features['input_ids'])
        a__:     List[Any]										=	{
            'input_ids': jnp.array(lowercase    ,			dtype=jnp.intaa),
            'attention_mask': jnp.array(lowercase    ,			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    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	[self._fetch_inputs(lowercase) for ids in input_ids]
        return zip(*lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Union[str, Any]										=	[1 for _ in range(len(lowercase))]
        while len(lowercase) < self.max_length:
          input_ids.append(self.pad_id)
          attention_mask.append(0)
        return input_ids, attention_mask







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:
  if seed is not None:
    a__:     int										=	dataset.shuffle(seed=_SCREAMING_SNAKE_CASE   )
  for i in range(len(_SCREAMING_SNAKE_CASE   ) // batch_size   ):
    a__:     Union[str, Any]										=	dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(_SCREAMING_SNAKE_CASE   )







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Any:
  def loss_fn(_SCREAMING_SNAKE_CASE   ):
    a__:     str										=	model_inputs.pop('start_labels'   )
    a__:     Dict										=	model_inputs.pop('end_labels'   )
    a__:     Optional[int]										=	model_inputs.pop('pooled_labels'   )

    a__:     Optional[Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=_SCREAMING_SNAKE_CASE		,     dropout_rng=_SCREAMING_SNAKE_CASE		,     train=_SCREAMING_SNAKE_CASE   )
    a__							,    a__							,    a__:     Optional[int]										=	outputs

    return state.loss_fn(
        _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     )

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

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







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     Optional[int]										=	model_inputs.pop('start_labels'   )
  a__:     int										=	model_inputs.pop('end_labels'   )
  a__:     Dict										=	model_inputs.pop('pooled_labels'   )

  a__:     Union[str, Any]										=	state.apply_fn(**_SCREAMING_SNAKE_CASE		,     params=state.params		,     train=_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     int										=	outputs

  a__:     Optional[int]										=	state.loss_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	jax.lax.pmean({'loss': loss}		,     axis_name='batch'   )
  return metrics




class    __snake_case       (     train_state.TrainState				):
      a__        =		struct.field(pytree_node=__lowerCAmelCase				)







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		42
      a__        =		None



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	self.args
        a__:     str										=	len(lowercase) // args.batch_size

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

              a__:     List[Any]										=	self.evaluate(lowercase    ,			lowercase)
              a__:     List[str]										=	{
                  'step': state_step.item(),
                  'eval_loss': eval_loss.item(),
                  'tr_loss': tr_loss,
                  'lr': lr.item(),
              }
              tqdm.write(str(lowercase))
              self.logger.log(lowercase    ,			commit=lowercase)

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  print(F'RESTORING CHECKPOINT FROM {save_dir}'		,     end=' ... '   )
  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'flax_model.msgpack'   )		,     'rb'   ) as f:
    a__:     int										=	from_bytes(state.params		,     f.read()   )

  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'opt_state.msgpack'   )		,     'rb'   ) as f:
    a__:     Optional[Any]										=	from_bytes(state.opt_state		,     f.read()   )

  a__:     Optional[Any]										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'args.joblib'   )   )
  a__:     int										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'data_collator.joblib'   )   )

  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'training_state.json'   )		,     'r'   ) as f:
    a__:     Any										=	json.load(_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	training_state['step']

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







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  def weight_decay_mask(_SCREAMING_SNAKE_CASE   ):
    a__:     List[Any]										=	traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE   )
    a__:     List[str]										=	{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
    return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	scheduler_fn(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     Any										=	optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE		,     weight_decay=_SCREAMING_SNAKE_CASE		,     mask=_SCREAMING_SNAKE_CASE   )
  return tx, lr


