


"""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_       (_UpperCamelCase				):
					UpperCAmelCase__ :   BigBirdConfig
					UpperCAmelCase__ :   jnp.dtype   				=      jnp.floataa
					UpperCAmelCase__ :   bool   				=      True
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Union[str, Any]:
												super().setup()
												a__					=					nn.Dense(5      ,dtype=self.dtype				)



					def __call__(				self       :Dict      ,*__snake_case       :Optional[int]      ,**__snake_case       :Optional[Any]				) ->   Any:
												a__					=					super().__call__(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE				)
												a__					=					self.cls(outputs[2]				)
												return outputs[:2] + (cls_out,)







class 	snake_case_       (_UpperCamelCase				):
					UpperCAmelCase__ :   List[Any]   				=      FlaxBigBirdForNaturalQuestionsModule






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

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







@dataclass
class 	snake_case_       :
					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   				=      0.0_0_9_5

					UpperCAmelCase__ :   str   				=      "bigbird-roberta-natural-questions"
					UpperCAmelCase__ :   str   				=      "training-expt"
					UpperCAmelCase__ :   str   				=      "data/nq-training.jsonl"
					UpperCAmelCase__ :   str   				=      "data/nq-validation.jsonl"
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												os.makedirs(self.base_dir      ,exist_ok=_SCREAMING_SNAKE_CASE				)
												a__					=					os.path.join(self.base_dir      ,self.save_dir				)
												a__					=					self.batch_size_per_device * jax.device_count()







@dataclass
class 	snake_case_       :
					UpperCAmelCase__ :   int
					UpperCAmelCase__ :   int   				=      4_0_9_6  # no dynamic padding on TPUs
					def __call__(				self       :Tuple      ,__snake_case       :int				) ->   Dict:
												a__					=					self.collate_fn(_SCREAMING_SNAKE_CASE				)
												a__					=					jax.tree_util.tree_map(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE				)
												return batch
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Optional[int]				) ->   Any:
												a__				, a__					=					self.fetch_inputs(features['input_ids']				)
												a__					=					{
												    'input_ids': jnp.array(_SCREAMING_SNAKE_CASE      ,dtype=jnp.intaa				),
												    'attention_mask': jnp.array(_SCREAMING_SNAKE_CASE      ,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       :int      ,__snake_case       :List[Any]				) ->   Dict:
												a__					=					[self._fetch_inputs(_SCREAMING_SNAKE_CASE				) for ids in input_ids]
												return zip(*_SCREAMING_SNAKE_CASE				)



					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :List[Any]				) ->   str:
												a__					=					[1 for _ in range(len(_SCREAMING_SNAKE_CASE				)				)]
												while len(_SCREAMING_SNAKE_CASE				) < self.max_length:
																			input_ids.append(self.pad_id				)
																			attention_mask.append(0				)
												return input_ids, attention_mask






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict=None       ):
							if seed is not None:
														a__					=					dataset.shuffle(seed=_UpperCamelCase       )
							for i in range(len(_UpperCamelCase       ) // batch_size       ):
														a__					=					dataset[i * batch_size : (i + 1) * batch_size]
														yield dict(_UpperCamelCase       )






@partial(jax.pmap ,   axis_name='batch'       )
def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Optional[Any] ,   **__lowerCAmelCase							:    Optional[int]       ):
							def loss_fn(__lowerCAmelCase							:    Tuple       ):
														a__					=					model_inputs.pop('start_labels'       )
														a__					=					model_inputs.pop('end_labels'       )
														a__					=					model_inputs.pop('pooled_labels'       )

														a__					=					state.apply_fn(**_UpperCamelCase ,   params=_UpperCamelCase ,   dropout_rng=_UpperCamelCase ,   train=_UpperCamelCase       )
														a__				, a__				, a__					=					outputs

														return state.loss_fn(
														    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   )

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

							a__					=					state.apply_gradients(grads=_UpperCamelCase       )
							return state, metrics, new_drp_rng






@partial(jax.pmap ,   axis_name='batch'       )
def 							__lowercase       (			__lowerCAmelCase							:    str ,   **__lowerCAmelCase							:    str       ):
							a__					=					model_inputs.pop('start_labels'       )
							a__					=					model_inputs.pop('end_labels'       )
							a__					=					model_inputs.pop('pooled_labels'       )

							a__					=					state.apply_fn(**_UpperCamelCase ,   params=state.params ,   train=_UpperCamelCase       )
							a__				, a__				, a__					=					outputs

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







class 	snake_case_       (train_state.TrainState				):
					UpperCAmelCase__ :   Callable   				=      struct.field(pytree_node=_UpperCamelCase				)







@dataclass
class 	snake_case_       :
					UpperCAmelCase__ :   Args
					UpperCAmelCase__ :   Callable
					UpperCAmelCase__ :   Callable
					UpperCAmelCase__ :   Callable
					UpperCAmelCase__ :   Callable
					UpperCAmelCase__ :   wandb
					UpperCAmelCase__ :   Callable   				=      None
					def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :Tuple      ,__snake_case       :List[Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :int=None				) ->   Union[str, Any]:
												a__					=					model.params
												a__					=					TrainState.create(
												    apply_fn=model.__call__      ,params=_SCREAMING_SNAKE_CASE      ,tx=_SCREAMING_SNAKE_CASE      ,loss_fn=_SCREAMING_SNAKE_CASE      ,)
												if ckpt_dir is not None:
																			a__				, a__				, a__				, a__				, a__					=					restore_checkpoint(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE				)
																			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(**_SCREAMING_SNAKE_CASE				)
																			a__					=					train_state.TrainState(
																			    step=_SCREAMING_SNAKE_CASE      ,apply_fn=model.__call__      ,params=_SCREAMING_SNAKE_CASE      ,tx=_SCREAMING_SNAKE_CASE      ,opt_state=_SCREAMING_SNAKE_CASE      ,)
																			a__					=					args
																			a__					=					data_collator
																			a__					=					lr
																			a__					=					params
												a__					=					jax_utils.replicate(_SCREAMING_SNAKE_CASE				)
												return state
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Optional[int]      ,__snake_case       :Any      ,__snake_case       :Optional[Any]				) ->   Dict:
												a__					=					self.args
												a__					=					len(_SCREAMING_SNAKE_CASE				) // args.batch_size

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(_SCREAMING_SNAKE_CASE      ,jax.device_count()				)
												for epoch in range(args.max_epochs				):
																			a__					=					jnp.array(0      ,dtype=jnp.floataa				)
																			a__					=					get_batched_dataset(_SCREAMING_SNAKE_CASE      ,args.batch_size      ,seed=_SCREAMING_SNAKE_CASE				)
																			a__					=					0
																			for batch in tqdm(_SCREAMING_SNAKE_CASE      ,total=_SCREAMING_SNAKE_CASE      ,desc=F'Running EPOCH-{epoch}'				):
																										a__					=					self.data_collator(_SCREAMING_SNAKE_CASE				)
																										a__				, a__				, a__					=					self.train_step_fn(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE				)
																										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(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE				)
																																	a__					=					{
																																	    'step': state_step.item(),
																																	    'eval_loss': eval_loss.item(),
																																	    'tr_loss': tr_loss,
																																	    'lr': lr.item(),
																																	}
																																	tqdm.write(str(_SCREAMING_SNAKE_CASE				)				)
																																	self.logger.log(_SCREAMING_SNAKE_CASE      ,commit=_SCREAMING_SNAKE_CASE				)

																										if i % args.save_steps == 0:
																																	self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'      ,state=_SCREAMING_SNAKE_CASE				)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Optional[Any]      ,__snake_case       :Any				) ->   Union[str, Any]:
												a__					=					get_batched_dataset(_SCREAMING_SNAKE_CASE      ,self.args.batch_size				)
												a__					=					len(_SCREAMING_SNAKE_CASE				) // self.args.batch_size
												a__					=					jnp.array(0      ,dtype=jnp.floataa				)
												a__					=					0
												for batch in tqdm(_SCREAMING_SNAKE_CASE      ,total=_SCREAMING_SNAKE_CASE      ,desc='Evaluating ... '				):
																			a__					=					self.data_collator(_SCREAMING_SNAKE_CASE				)
																			a__					=					self.val_step_fn(_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE				)
																			running_loss += jax_utils.unreplicate(metrics['loss']				)
																			i += 1
												return running_loss / i



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






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
							print(F'RESTORING CHECKPOINT FROM {save_dir}' ,   end=' ... '       )
							with open(os.path.join(_UpperCamelCase ,   'flax_model.msgpack'       ) ,   'rb'       ) as f:
														a__					=					from_bytes(state.params ,   f.read()       )

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

							a__					=					joblib.load(os.path.join(_UpperCamelCase ,   'args.joblib'       )       )
							a__					=					joblib.load(os.path.join(_UpperCamelCase ,   'data_collator.joblib'       )       )

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

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






def 							__lowercase       (			__lowerCAmelCase							:    Tuple ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					num_train_steps - warmup_steps
							a__					=					optax.linear_schedule(init_value=_UpperCamelCase ,   end_value=_UpperCamelCase ,   transition_steps=_UpperCamelCase       )
							a__					=					optax.linear_schedule(init_value=_UpperCamelCase ,   end_value=1E-7 ,   transition_steps=_UpperCamelCase       )
							a__					=					optax.join_schedules(schedules=[warmup_fn, decay_fn] ,   boundaries=[warmup_steps]       )
							return lr






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

							a__					=					scheduler_fn(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase       )

							a__					=					optax.adamw(learning_rate=_UpperCamelCase ,   weight_decay=_UpperCamelCase ,   mask=_UpperCamelCase       )
							return tx, lr








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														a__				, a__					=					array[indexa], array[indexa]






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														for i in range(__lowerCAmelCase ,   low + middle       ):
																					comp_and_swap(__lowerCAmelCase ,   __lowerCAmelCase ,   i + middle ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														bitonic_sort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   1       )
														bitonic_sort(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   0       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				input('''Enter numbers separated by a comma:\n''').strip()
						snake_case							:	Optional[int]					      =				[int(item.strip()) for item in user_input.split(''',''')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('''\nSorted array in ascending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('''Sorted array in descending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')




