"""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  UpperCamelCase	(    __lowerCamelCase						):
   lowercase							=						42
   lowercase							=						jnp.floataa
   lowercase							=						True
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


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







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


        '''simple docstring'''


        lowercase_			:       int									=			super().__call__(*UpperCamelCase_  ,**UpperCamelCase_    )
        lowercase_			:       Optional[int]									=			self.cls(outputs[2]    )
        return outputs[:2] + (cls_out,)







class  UpperCamelCase	(    __lowerCamelCase						):
   lowercase							=						FlaxBigBirdForNaturalQuestionsModule






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]   ):



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

     lowercase_			:       Tuple									=			partial(A__  ,    reduction=jnp.mean   )
     lowercase_			:       List[Any]									=			cross_entropy(A__  ,    A__   )
     lowercase_			:       Any									=			cross_entropy(A__  ,    A__   )
     lowercase_			:       int									=			cross_entropy(A__  ,    A__   )
     return (start_loss + end_loss + pooled_loss) / 3







@dataclass
class  UpperCamelCase	:
   lowercase							=						"google/bigbird-roberta-base"
   lowercase							=						3_0_0_0
   lowercase							=						1_0_5_0_0

   lowercase							=						1_2_8
   lowercase							=						3

   lowercase							=						1
   lowercase							=						5

   # tx_args
   lowercase							=						3e-5
   lowercase							=						0.0
   lowercase							=						2_0_0_0_0
   lowercase							=						0.0_095

   lowercase							=						"bigbird-roberta-natural-questions"
   lowercase							=						"training-expt"
   lowercase							=						"data/nq-training.jsonl"
   lowercase							=						"data/nq-validation.jsonl"
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        os.makedirs(self.base_dir  ,exist_ok=UpperCamelCase_    )
        lowercase_			:       Dict									=			os.path.join(self.base_dir  ,self.save_dir    )
        lowercase_			:       List[Any]									=			self.batch_size_per_device * jax.device_count()







@dataclass
class  UpperCamelCase	:
   lowercase							=						42
   lowercase							=						4_0_9_6  # no dynamic padding on TPUs
   def __call__(    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.collate_fn(UpperCamelCase_    )
        lowercase_			:       Optional[Any]									=			jax.tree_util.tree_map(UpperCamelCase_  ,UpperCamelCase_    )
        return batch
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       List[str]									=			self.fetch_inputs(features['input_ids']    )
        lowercase_			:       List[str]									=			{
            '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     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			[self._fetch_inputs(UpperCamelCase_    ) for ids in input_ids]
        return zip(*UpperCamelCase_    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			[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__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None   ):



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



@partial(jax.pmap  ,    axis_name='batch'   )
def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    **__SCREAMING_SNAKE_CASE							:  Any   ):



     def loss_fn(__SCREAMING_SNAKE_CASE							:  Optional[int]   ):
          lowercase_			:       int									=			model_inputs.pop('start_labels'   )
          lowercase_			:       int									=			model_inputs.pop('end_labels'   )
          lowercase_			:       Tuple									=			model_inputs.pop('pooled_labels'   )

          lowercase_			:       Dict									=			state.apply_fn(**A__  ,    params=A__  ,    dropout_rng=A__  ,    train=A__   )
          lowercase_							,						lowercase_							,						lowercase_			:       int									=			outputs

          return state.loss_fn(
              A__  ,    A__  ,    A__  ,    A__  ,    A__  ,    A__  ,    )

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

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



@partial(jax.pmap  ,    axis_name='batch'   )
def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    **__SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       Any									=			model_inputs.pop('start_labels'   )
     lowercase_			:       Dict									=			model_inputs.pop('end_labels'   )
     lowercase_			:       Dict									=			model_inputs.pop('pooled_labels'   )

     lowercase_			:       Union[str, Any]									=			state.apply_fn(**A__  ,    params=state.params  ,    train=A__   )
     lowercase_							,						lowercase_							,						lowercase_			:       str									=			outputs

     lowercase_			:       Dict									=			state.loss_fn(A__  ,    A__  ,    A__  ,    A__  ,    A__  ,    A__   )
     lowercase_			:       List[str]									=			jax.lax.pmean({'loss': loss}  ,    axis_name='batch'   )
     return metrics







class  UpperCamelCase	(    train_state.TrainState						):
   lowercase							=						struct.field(pytree_node=__lowerCamelCase						)







@dataclass
class  UpperCamelCase	:
   lowercase							=						42
   lowercase							=						42
   lowercase							=						42
   lowercase							=						42
   lowercase							=						42
   lowercase							=						42
   lowercase							=						None
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase=None    )			->   List[str]:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.args
        lowercase_			:       str									=			len(UpperCamelCase_    ) // args.batch_size

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

                       lowercase_			:       Union[str, Any]									=			self.evaluate(UpperCamelCase_  ,UpperCamelCase_    )
                       lowercase_			:       List[Any]									=			{
                           '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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			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__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  List[str]   ):
     print(F'''RESTORING CHECKPOINT FROM {save_dir}'''  ,    end=' ... '   )
     with open(os.path.join(A__  ,    'flax_model.msgpack'   )  ,    'rb'   ) as f:
          lowercase_			:       Optional[int]									=			from_bytes(state.params  ,    f.read()   )

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

     lowercase_			:       Dict									=			joblib.load(os.path.join(A__  ,    'args.joblib'   )   )
     lowercase_			:       str									=			joblib.load(os.path.join(A__  ,    'data_collator.joblib'   )   )

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

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_			:       str									=			num_train_steps - warmup_steps
     lowercase_			:       Optional[int]									=			optax.linear_schedule(init_value=A__  ,    end_value=A__  ,    transition_steps=A__   )
     lowercase_			:       str									=			optax.linear_schedule(init_value=A__  ,    end_value=1E-7  ,    transition_steps=A__   )
     lowercase_			:       Tuple									=			optax.join_schedules(schedules=[warmup_fn, decay_fn]  ,    boundaries=[warmup_steps]   )
     return lr







def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]   ):



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

     lowercase_			:       Optional[int]									=			scheduler_fn(A__  ,    A__  ,    A__  ,    A__   )

     lowercase_			:       Union[str, Any]									=			optax.adamw(learning_rate=A__  ,    weight_decay=A__  ,    mask=A__   )
     return tx, lr


"""simple docstring"""







import numpy as np



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.array   ):
     return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()

