



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 (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       jnp.floataa
	SCREAMING_SNAKE_CASE       =       True



	def 		UpperCamelCase							(						self							:    List[str])->							Optional[int]:
						super().setup()
						__lowerCAmelCase	   =nn.Dense(5				,	dtype=self.dtype)



	def __call__(						self							:    List[Any]				,	*snake_case_							:    str				,	**snake_case_							:    List[str])->							str:
						__lowerCAmelCase	   =super().__call__(*snake_case_				,	**snake_case_)
						__lowerCAmelCase	   =self.cls(outputs[2])
						return outputs[:2] + (cls_out,)






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       FlaxBigBirdForNaturalQuestionsModule






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

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






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       "google/bigbird-roberta-base"
	SCREAMING_SNAKE_CASE       =       3000
	SCREAMING_SNAKE_CASE       =       1_0500

	SCREAMING_SNAKE_CASE       =       128
	SCREAMING_SNAKE_CASE       =       3

	SCREAMING_SNAKE_CASE       =       1
	SCREAMING_SNAKE_CASE       =       5

	# tx_args
	SCREAMING_SNAKE_CASE       =       3E-5
	SCREAMING_SNAKE_CASE       =       0.0
	SCREAMING_SNAKE_CASE       =       2_0000
	SCREAMING_SNAKE_CASE       =       0.0095

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



	def 		UpperCamelCase							(						self							:    int)->							str:
						os.makedirs(self.base_dir				,	exist_ok=snake_case_)
						__lowerCAmelCase	   =os.path.join(self.base_dir				,	self.save_dir)
						__lowerCAmelCase	   =self.batch_size_per_device * jax.device_count()






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       4096  # no dynamic padding on TPUs



	def __call__(						self							:    int				,	snake_case_							:    int)->							Optional[Any]:
						__lowerCAmelCase	   =self.collate_fn(snake_case_)
						__lowerCAmelCase	   =jax.tree_util.tree_map(snake_case_				,	snake_case_)
						return batch



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    Optional[Any])->							Dict:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.fetch_inputs(features["""input_ids"""])
						__lowerCAmelCase	   ={
						    """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 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    list)->							Dict:
						__lowerCAmelCase	   =[self._fetch_inputs(snake_case_) for ids in input_ids]
						return zip(*snake_case_)



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    list)->							List[Any]:
						__lowerCAmelCase	   =[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 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    List[str]=None	)  ->						Any:
					if seed is not None:
										__lowerCAmelCase	   =dataset.shuffle(seed=__lowerCamelCase	)
					for i in range(len(__lowerCamelCase	) // batch_size	):
										__lowerCAmelCase	   =dataset[i * batch_size : (i + 1) * batch_size]
										yield dict(__lowerCamelCase	)






@partial(jax.pmap				,   axis_name="""batch"""	)
def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    Tuple				,   **__lowerCamelCase	:    List[str]	)  ->						Union[str, Any]:
					def loss_fn(__lowerCamelCase	:    List[str]	):
										__lowerCAmelCase	   =model_inputs.pop("""start_labels"""	)
										__lowerCAmelCase	   =model_inputs.pop("""end_labels"""	)
										__lowerCAmelCase	   =model_inputs.pop("""pooled_labels"""	)

										__lowerCAmelCase	   =state.apply_fn(**__lowerCamelCase				,   params=__lowerCamelCase				,   dropout_rng=__lowerCamelCase				,   train=__lowerCamelCase	)
										__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =outputs

										return state.loss_fn(
										    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   )

					__lowerCAmelCase   ,	__lowerCAmelCase	   =jax.random.split(__lowerCamelCase	)
					__lowerCAmelCase	   =jax.value_and_grad(__lowerCamelCase	)
					__lowerCAmelCase   ,	__lowerCAmelCase	   =grad_fn(state.params	)
					__lowerCAmelCase	   =jax.lax.pmean({"""loss""": loss}				,   axis_name="""batch"""	)
					__lowerCAmelCase	   =jax.lax.pmean(__lowerCamelCase				,   """batch"""	)

					__lowerCAmelCase	   =state.apply_gradients(grads=__lowerCamelCase	)
					return state, metrics, new_drp_rng






@partial(jax.pmap				,   axis_name="""batch"""	)
def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple				,   **__lowerCamelCase	:    Optional[Any]	)  ->						Dict:
					__lowerCAmelCase	   =model_inputs.pop("""start_labels"""	)
					__lowerCAmelCase	   =model_inputs.pop("""end_labels"""	)
					__lowerCAmelCase	   =model_inputs.pop("""pooled_labels"""	)

					__lowerCAmelCase	   =state.apply_fn(**__lowerCamelCase				,   params=state.params				,   train=__lowerCamelCase	)
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =outputs

					__lowerCAmelCase	   =state.loss_fn(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =jax.lax.pmean({"""loss""": loss}				,   axis_name="""batch"""	)
					return metrics






class 						__a (     train_state.TrainState				):
	SCREAMING_SNAKE_CASE       =       struct.field(pytree_node=SCREAMING_SNAKE_CASE				)






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       None



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



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Any				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int])->							Optional[Any]:
						__lowerCAmelCase	   =self.args
						__lowerCAmelCase	   =len(snake_case_) // args.batch_size

						__lowerCAmelCase	   =jax.random.PRNGKey(0)
						__lowerCAmelCase	   =jax.random.split(snake_case_				,	jax.device_count())
						for epoch in range(args.max_epochs):
											__lowerCAmelCase	   =jnp.array(0				,	dtype=jnp.floataa)
											__lowerCAmelCase	   =get_batched_dataset(snake_case_				,	args.batch_size				,	seed=snake_case_)
											__lowerCAmelCase	   =0
											for batch in tqdm(snake_case_				,	total=snake_case_				,	desc=F"""Running EPOCH-{epoch}"""):
																__lowerCAmelCase	   =self.data_collator(snake_case_)
																__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =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:
																					__lowerCAmelCase	   =jax_utils.unreplicate(state.step)
																					__lowerCAmelCase	   =running_loss.item() / i
																					__lowerCAmelCase	   =self.scheduler_fn(state_step - 1)

																					__lowerCAmelCase	   =self.evaluate(snake_case_				,	snake_case_)
																					__lowerCAmelCase	   ={
																					    """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 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Any				,	snake_case_							:    Optional[Any])->							List[Any]:
						__lowerCAmelCase	   =get_batched_dataset(snake_case_				,	self.args.batch_size)
						__lowerCAmelCase	   =len(snake_case_) // self.args.batch_size
						__lowerCAmelCase	   =jnp.array(0				,	dtype=jnp.floataa)
						__lowerCAmelCase	   =0
						for batch in tqdm(snake_case_				,	total=snake_case_				,	desc="""Evaluating ... """):
											__lowerCAmelCase	   =self.data_collator(snake_case_)
											__lowerCAmelCase	   =self.val_step_fn(snake_case_				,	**snake_case_)
											running_loss += jax_utils.unreplicate(metrics["""loss"""])
											i += 1
						return running_loss / i



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =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 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    int	)  ->						Any:
					print(f"""RESTORING CHECKPOINT FROM {save_dir}"""				,   end=""" ... """	)
					with open(os.path.join(__lowerCamelCase				,   """flax_model.msgpack"""	)				,   """rb"""	) as f:
										__lowerCAmelCase	   =from_bytes(state.params				,   f.read()	)

					with open(os.path.join(__lowerCamelCase				,   """opt_state.msgpack"""	)				,   """rb"""	) as f:
										__lowerCAmelCase	   =from_bytes(state.opt_state				,   f.read()	)

					__lowerCAmelCase	   =joblib.load(os.path.join(__lowerCamelCase				,   """args.joblib"""	)	)
					__lowerCAmelCase	   =joblib.load(os.path.join(__lowerCamelCase				,   """data_collator.joblib"""	)	)

					with open(os.path.join(__lowerCamelCase				,   """training_state.json"""	)				,   """r"""	) as f:
										__lowerCAmelCase	   =json.load(__lowerCamelCase	)
					__lowerCAmelCase	   =training_state["""step"""]

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






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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    Any	)  ->						Optional[Any]:
					def weight_decay_mask(__lowerCamelCase	:    Tuple	):
										__lowerCAmelCase	   =traverse_util.flatten_dict(__lowerCamelCase	)
										__lowerCAmelCase	   ={k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
										return traverse_util.unflatten_dict(__lowerCamelCase	)

					__lowerCAmelCase	   =scheduler_fn(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

					__lowerCAmelCase	   =optax.adamw(learning_rate=__lowerCamelCase				,   weight_decay=__lowerCamelCase				,   mask=__lowerCamelCase	)
					return tx, lr









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[int]	)  ->						int:
					if not numbers:
										return 0

					if not isinstance(__lowerCamelCase				,   (list, tuple)	) or not all(
					    isinstance(__lowerCamelCase				,   __lowerCamelCase	) for number in numbers	):
										raise ValueError("""numbers must be an iterable of integers"""	)

					__lowerCAmelCase	   =__lowerCAmelCase	   =__lowerCAmelCase	   =numbers[0]

					for i in range(1				,   len(__lowerCamelCase	)	):
										# update the maximum and minimum subarray products
										__lowerCAmelCase	   =numbers[i]
										if number < 0:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =min_till_now, max_till_now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   max_till_now * number	)
										__lowerCAmelCase	   =min(__lowerCamelCase				,   min_till_now * number	)

										# update the maximum product found till now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   __lowerCamelCase	)

					return max_prod




