





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_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = jnp.floataa
   lowerCAmelCase__        = True




   def _lowerCAmelCase      (self	:List[Any] )->					Tuple:
         super().setup()
         __A          =				nn.Dense(5    ,   dtype=self.dtype )






   def __call__(self	:Optional[Any]    ,   *_UpperCamelCase	:Any    ,   **_UpperCamelCase	:Union[str, Any] )->					Optional[Any]:
         __A          =				super().__call__(*_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				self.cls(outputs[2] )
         return outputs[:2] + (cls_out,)





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = FlaxBigBirdForNaturalQuestionsModule





def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Union[str, Any] )	->						Tuple:







      '''simple docstring'''






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

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





@dataclass
class A_					:
   lowerCAmelCase__        = "google/bigbird-roberta-base"
   lowerCAmelCase__        = 3000
   lowerCAmelCase__        = 10500

   lowerCAmelCase__        = 128
   lowerCAmelCase__        = 3

   lowerCAmelCase__        = 1
   lowerCAmelCase__        = 5

   # tx_args
   lowerCAmelCase__        = 3e-5
   lowerCAmelCase__        = 0.0
   lowerCAmelCase__        = 20000
   lowerCAmelCase__        = 0.0_095

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




   def _lowerCAmelCase      (self	:List[Any] )->					str:
         os.makedirs(self.base_dir    ,   exist_ok=_UpperCamelCase )
         __A          =				os.path.join(self.base_dir    ,   self.save_dir )
         __A          =				self.batch_size_per_device * jax.device_count()





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 4096  # no dynamic padding on TPUs




   def __call__(self	:int    ,   _UpperCamelCase	:Union[str, Any] )->					Optional[int]:
         __A          =				self.collate_fn(_UpperCamelCase )
         __A          =				jax.tree_util.tree_map(_UpperCamelCase    ,   _UpperCamelCase )
         return batch




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:Any )->					Optional[int]:
         __A   ,						__A          =				self.fetch_inputs(features['''input_ids'''] )
         __A          =				{
             '''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 _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:list )->					List[str]:
         __A          =				[self._fetch_inputs(_UpperCamelCase ) for ids in input_ids]
         return zip(*_UpperCamelCase )






   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:list )->					str:
         __A          =				[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 _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str					,				lowerCamelCase:   Dict=None )	->						int:







      '''simple docstring'''






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



@partial(jax.pmap					,				axis_name='''batch''' )
def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Dict					,				**lowerCamelCase:   Tuple )	->						Optional[Any]:







      '''simple docstring'''






      def loss_fn(lowerCamelCase:   str ):
            __A          =				model_inputs.pop('''start_labels''' )
            __A          =				model_inputs.pop('''end_labels''' )
            __A          =				model_inputs.pop('''pooled_labels''' )

            __A          =				state.apply_fn(**lowerCamelCase					,				params=lowerCamelCase					,				dropout_rng=lowerCamelCase					,				train=lowerCamelCase )
            __A   ,						__A   ,						__A          =				outputs

            return state.loss_fn(
                lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				)

      __A   ,						__A          =				jax.random.split(lowerCamelCase )
      __A          =				jax.value_and_grad(lowerCamelCase )
      __A   ,						__A          =				grad_fn(state.params )
      __A          =				jax.lax.pmean({'''loss''': loss}					,				axis_name='''batch''' )
      __A          =				jax.lax.pmean(lowerCamelCase					,				'''batch''' )

      __A          =				state.apply_gradients(grads=lowerCamelCase )
      return state, metrics, new_drp_rng



@partial(jax.pmap					,				axis_name='''batch''' )
def _a				(	lowerCamelCase:   Any					,				**lowerCamelCase:   Optional[int] )	->						int:







      '''simple docstring'''






      __A          =				model_inputs.pop('''start_labels''' )
      __A          =				model_inputs.pop('''end_labels''' )
      __A          =				model_inputs.pop('''pooled_labels''' )

      __A          =				state.apply_fn(**lowerCamelCase					,				params=state.params					,				train=lowerCamelCase )
      __A   ,						__A   ,						__A          =				outputs

      __A          =				state.loss_fn(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      __A          =				jax.lax.pmean({'''loss''': loss}					,				axis_name='''batch''' )
      return metrics





class A_					(			train_state.TrainState   ):
   lowerCAmelCase__        = struct.field(pytree_node=_lowerCamelCase   )





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = None




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Dict=None )->					Dict:
         __A          =				model.params
         __A          =				TrainState.create(
             apply_fn=model.__call__    ,   params=_UpperCamelCase    ,   tx=_UpperCamelCase    ,   loss_fn=_UpperCamelCase    ,   )
         if ckpt_dir is not None:
               __A   ,						__A   ,						__A   ,						__A   ,						__A          =				restore_checkpoint(_UpperCamelCase    ,   _UpperCamelCase )
               __A          =				{
                   '''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          =				build_tx(**_UpperCamelCase )
               __A          =				train_state.TrainState(
                   step=_UpperCamelCase    ,   apply_fn=model.__call__    ,   params=_UpperCamelCase    ,   tx=_UpperCamelCase    ,   opt_state=_UpperCamelCase    ,   )
               __A          =				args
               __A          =				data_collator
               __A          =				lr
               __A          =				params
         __A          =				jax_utils.replicate(_UpperCamelCase )
         return state




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Optional[Any] )->					Any:
         __A          =				self.args
         __A          =				len(_UpperCamelCase ) // args.batch_size

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

                           __A          =				self.evaluate(_UpperCamelCase    ,   _UpperCamelCase )
                           __A          =				{
                               '''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 _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         __A          =				get_batched_dataset(_UpperCamelCase    ,   self.args.batch_size )
         __A          =				len(_UpperCamelCase ) // self.args.batch_size
         __A          =				jnp.array(0    ,   dtype=jnp.floataa )
         __A          =				0
         for batch in tqdm(_UpperCamelCase    ,   total=_UpperCamelCase    ,   desc='''Evaluating ... ''' ):
               __A          =				self.data_collator(_UpperCamelCase )
               __A          =				self.val_step_fn(_UpperCamelCase    ,   **_UpperCamelCase )
               running_loss += jax_utils.unreplicate(metrics['''loss'''] )
               i += 1
         return running_loss / i






   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:str )->					Optional[int]:
         __A          =				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 _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   List[str] )	->						List[str]:







      '''simple docstring'''






      print(F"""RESTORING CHECKPOINT FROM {save_dir}"""					,				end=''' ... ''' )
      with open(os.path.join(lowerCamelCase					,				'''flax_model.msgpack''' )					,				'''rb''' ) as f:
            __A          =				from_bytes(state.params					,				f.read() )

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

      __A          =				joblib.load(os.path.join(lowerCamelCase					,				'''args.joblib''' ) )
      __A          =				joblib.load(os.path.join(lowerCamelCase					,				'''data_collator.joblib''' ) )

      with open(os.path.join(lowerCamelCase					,				'''training_state.json''' )					,				'''r''' ) as f:
            __A          =				json.load(lowerCamelCase )
      __A          =				training_state['''step''']

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



def _a				(	lowerCamelCase:   Dict					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int )	->						Optional[Any]:







      '''simple docstring'''






      __A          =				num_train_steps - warmup_steps
      __A          =				optax.linear_schedule(init_value=lowerCamelCase					,				end_value=lowerCamelCase					,				transition_steps=lowerCamelCase )
      __A          =				optax.linear_schedule(init_value=lowerCamelCase					,				end_value=1e-7					,				transition_steps=lowerCamelCase )
      __A          =				optax.join_schedules(schedules=[warmup_fn, decay_fn]					,				boundaries=[warmup_steps] )
      return lr







def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






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

      __A          =				scheduler_fn(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )

      __A          =				optax.adamw(learning_rate=lowerCamelCase					,				weight_decay=lowerCamelCase					,				mask=lowerCamelCase )
      return tx, lr












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





