
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  ):




	'''simple docstring'''


	_UpperCamelCase	:	BigBirdConfig
	_UpperCamelCase	:	jnp.dtype									=			jnp.floataa
	_UpperCamelCase	:	bool									=			True





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().setup()
		lowercase	    =       nn.Dense(5						,       dtype=self.dtype			)





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







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			FlaxBigBirdForNaturalQuestionsModule






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

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







@dataclass
class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	str									=			"google/bigbird-roberta-base"
	_UpperCamelCase	:	int									=			3000
	_UpperCamelCase	:	int									=			1_0500

	_UpperCamelCase	:	int									=			128
	_UpperCamelCase	:	int									=			3

	_UpperCamelCase	:	int									=			1
	_UpperCamelCase	:	int									=			5

	# tx_args
	_UpperCamelCase	:	float									=			3e-5
	_UpperCamelCase	:	float									=			0.0
	_UpperCamelCase	:	int									=			2_0000
	_UpperCamelCase	:	float									=			0.0095

	_UpperCamelCase	:	str									=			"bigbird-roberta-natural-questions"
	_UpperCamelCase	:	str									=			"training-expt"
	_UpperCamelCase	:	str									=			"data/nq-training.jsonl"
	_UpperCamelCase	:	str									=			"data/nq-validation.jsonl"





	def       SCREAMING_SNAKE_CASE__		(			self			):
		os.makedirs(self.base_dir						,       exist_ok=snake_case			)
		lowercase	    =       os.path.join(self.base_dir						,       self.save_dir			)
		lowercase	    =       self.batch_size_per_device * jax.device_count()







@dataclass
class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	int
	_UpperCamelCase	:	int									=			4096  # no dynamic padding on TPUs





	def __call__(			self						,       snake_case			):
		lowercase	    =       self.collate_fn(snake_case			)
		lowercase	    =       jax.tree_util.tree_map(snake_case						,       snake_case			)
		return batch





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase    ,   lowercase	    =       self.fetch_inputs(features['input_ids']			)
		lowercase	    =       {
		    'input_ids': jnp.array(snake_case						,       dtype=jnp.intaa			),
		    'attention_mask': jnp.array(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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       [self._fetch_inputs(snake_case			) for ids in input_ids]
		return zip(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       [1 for _ in range(len(snake_case			)			)]
		while len(snake_case			) < self.max_length:
			input_ids.append(self.pad_id			)
			attention_mask.append(0			)
		return input_ids, attention_mask






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None				):
	if seed is not None:
		lowercase	    =       dataset.shuffle(seed=__SCREAMING_SNAKE_CASE				)
	for i in range(len(__SCREAMING_SNAKE_CASE				) // batch_size				):
		lowercase	    =       dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(__SCREAMING_SNAKE_CASE				)






@partial(jax.pmap					,				axis_name='batch'				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				**__SCREAMING_SNAKE_CASE				):
	def loss_fn(__SCREAMING_SNAKE_CASE				):
		lowercase	    =       model_inputs.pop('start_labels'				)
		lowercase	    =       model_inputs.pop('end_labels'				)
		lowercase	    =       model_inputs.pop('pooled_labels'				)

		lowercase	    =       state.apply_fn(**__SCREAMING_SNAKE_CASE					,				params=__SCREAMING_SNAKE_CASE					,				dropout_rng=__SCREAMING_SNAKE_CASE					,				train=__SCREAMING_SNAKE_CASE				)
		lowercase    ,   lowercase    ,   lowercase	    =       outputs

		return state.loss_fn(
		    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				)

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

	lowercase	    =       state.apply_gradients(grads=__SCREAMING_SNAKE_CASE				)
	return state, metrics, new_drp_rng






@partial(jax.pmap					,				axis_name='batch'				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				**__SCREAMING_SNAKE_CASE				):
	lowercase	    =       model_inputs.pop('start_labels'				)
	lowercase	    =       model_inputs.pop('end_labels'				)
	lowercase	    =       model_inputs.pop('pooled_labels'				)

	lowercase	    =       state.apply_fn(**__SCREAMING_SNAKE_CASE					,				params=state.params					,				train=__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase    ,   lowercase	    =       outputs

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







class 			A_  (     train_state.TrainState  ):




	'''simple docstring'''


	_UpperCamelCase	:	Callable									=			struct.field(pytree_node=__lowerCamelCase  )







@dataclass
class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	Args
	_UpperCamelCase	:	Callable
	_UpperCamelCase	:	Callable
	_UpperCamelCase	:	Callable
	_UpperCamelCase	:	Callable
	_UpperCamelCase	:	wandb
	_UpperCamelCase	:	Callable									=			None





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case=None			):
		lowercase	    =       model.params
		lowercase	    =       TrainState.create(
		    apply_fn=model.__call__						,       params=snake_case						,       tx=snake_case						,       loss_fn=snake_case						,       )
		if ckpt_dir is not None:
			lowercase    ,   lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       restore_checkpoint(snake_case						,       snake_case			)
			lowercase	    =       {
			    '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	    =       build_tx(**snake_case			)
			lowercase	    =       train_state.TrainState(
			    step=snake_case						,       apply_fn=model.__call__						,       params=snake_case						,       tx=snake_case						,       opt_state=snake_case						,       )
			lowercase	    =       args
			lowercase	    =       data_collator
			lowercase	    =       lr
			lowercase	    =       params
		lowercase	    =       jax_utils.replicate(snake_case			)
		return state





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       self.args
		lowercase	    =       len(snake_case			) // args.batch_size

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

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

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





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





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






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

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

	lowercase	    =       joblib.load(os.path.join(__SCREAMING_SNAKE_CASE					,				'args.joblib'				)				)
	lowercase	    =       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:
		lowercase	    =       json.load(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       training_state['step']

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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       num_train_steps - warmup_steps
	lowercase	    =       optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE					,				end_value=__SCREAMING_SNAKE_CASE					,				transition_steps=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE					,				end_value=1e-7					,				transition_steps=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       optax.join_schedules(schedules=[warmup_fn, decay_fn]					,				boundaries=[warmup_steps]				)
	return lr






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

	lowercase	    =       scheduler_fn(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

	lowercase	    =       optax.adamw(learning_rate=__SCREAMING_SNAKE_CASE					,				weight_decay=__SCREAMING_SNAKE_CASE					,				mask=__SCREAMING_SNAKE_CASE				)
	return tx, lr





'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       if len(A			) == 1:
              return True
       lowercase__      =					series[1] - series[0]
       for index in range(len(A			) - 1			):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A			)			->			float:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       lowercase__      =					0
       for val in series:
              answer += val
       return answer / len(A			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




