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__						(					A__					):
					A	       =							42
					A	       =							jnp.floataa
					A	       =							True

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							super().setup()
							SCREAMING_SNAKE_CASE_  :		Any				      =    nn.Dense(5,dtype=self.dtype    )






					def __call__(    self      :       Any,*_A      :       str,**_A      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().__call__(*_A,**_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.cls(outputs[2]    )
							return outputs[:2] + (cls_out,)







class 		a__						(					A__					):
					A	       =							FlaxBigBirdForNaturalQuestionsModule







def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		str				      =    partial(lowerCAmelCase   , reduction=jnp.mean	)
		SCREAMING_SNAKE_CASE_  :		str				      =    cross_entropy(lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		str				      =    cross_entropy(lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    cross_entropy(lowerCAmelCase   , lowerCAmelCase	)
		return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 		a__						:
					A	       =							"google/bigbird-roberta-base"
					A	       =							3000
					A	       =							10500

					A	       =							128
					A	       =							3

					A	       =							1
					A	       =							5

					# tx_args
					A	       =							3E-5
					A	       =							0.0
					A	       =							20000
					A	       =							0.0095

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							os.makedirs(self.base_dir,exist_ok=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    os.path.join(self.base_dir,self.save_dir    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.batch_size_per_device * jax.device_count()







@dataclass
class 		a__						:
					A	       =							42
					A	       =							4096  # no dynamic padding on TPUs

					def __call__(    self      :       str,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.collate_fn(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    jax.tree_util.tree_map(_A,_A    )
							return batch

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.fetch_inputs(features["input_ids"]    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {
							    "input_ids": jnp.array(_A,dtype=jnp.intaa    ),
							    "attention_mask": jnp.array(_A,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      :       Tuple,_A      :       list    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [self._fetch_inputs(_A    ) for ids in input_ids]
							return zip(*_A    )






					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       list    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [1 for _ in range(len(_A    )    )]
							while len(_A    ) < self.max_length:
									input_ids.append(self.pad_id    )
									attention_mask.append(0    )
							return input_ids, attention_mask







def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Any=None	):
		"""simple docstring"""







		if seed is not None:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dataset.shuffle(seed=lowerCAmelCase	)
		for i in range(len(lowerCAmelCase	) // batch_size	):
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    dataset[i * batch_size : (i + 1) * batch_size]
				yield dict(lowerCAmelCase	)




@partial(jax.pmap   , axis_name="batch"	)
def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[Any]   , **lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		def loss_fn(lowerCAmelCase							:   List[Any]	):
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model_inputs.pop("start_labels"	)
				SCREAMING_SNAKE_CASE_  :		Dict				      =    model_inputs.pop("end_labels"	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    model_inputs.pop("pooled_labels"	)

				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    state.apply_fn(**lowerCAmelCase   , params=lowerCAmelCase   , dropout_rng=lowerCAmelCase   , train=lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    outputs

				return state.loss_fn(
				    lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , )

		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    jax.random.split(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    jax.value_and_grad(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    grad_fn(state.params	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    jax.lax.pmean({"loss": loss}   , axis_name="batch"	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    jax.lax.pmean(lowerCAmelCase   , "batch"	)

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




@partial(jax.pmap   , axis_name="batch"	)
def       _snake_case	(   lowerCAmelCase							:   Dict   , **lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model_inputs.pop("start_labels"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_inputs.pop("end_labels"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_inputs.pop("pooled_labels"	)

		SCREAMING_SNAKE_CASE_  :		str				      =    state.apply_fn(**lowerCAmelCase   , params=state.params   , train=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    outputs

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    state.loss_fn(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    jax.lax.pmean({"loss": loss}   , axis_name="batch"	)
		return metrics






class 		a__						(					train_state.TrainState					):
					A	       =							struct.field(pytree_node=A__					)







@dataclass
class 		a__						:
					A	       =							42
					A	       =							42
					A	       =							42
					A	       =							42
					A	       =							42
					A	       =							42
					A	       =							None

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any],_A      :       int,_A      :       Union[str, Any],_A      :       Dict=None    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       Optional[int],_A      :       List[Any],_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.args
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    len(_A    ) // args.batch_size

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

													SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.evaluate(_A,_A    )
													SCREAMING_SNAKE_CASE_  :		int				      =    {
													    "step": state_step.item(),
													    "eval_loss": eval_loss.item(),
													    "tr_loss": tr_loss,
													    "lr": lr.item(),
													}
													tqdm.write(str(_A    )    )
													self.logger.log(_A,commit=_A    )

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

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Dict,_A      :       Optional[Any]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       Union[str, Any],_A      :       Optional[Any]    ):







							"""simple docstring"""






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







def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		print(f'RESTORING CHECKPOINT FROM {save_dir}'   , end=" ... "	)
		with open(os.path.join(lowerCAmelCase   , "flax_model.msgpack"	)   , "rb"	) as f:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    from_bytes(state.params   , f.read()	)

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

		SCREAMING_SNAKE_CASE_  :		str				      =    joblib.load(os.path.join(lowerCAmelCase   , "args.joblib"	)	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    joblib.load(os.path.join(lowerCAmelCase   , "data_collator.joblib"	)	)

		with open(os.path.join(lowerCAmelCase   , "training_state.json"	)   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    json.load(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    training_state["step"]

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




def       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    num_train_steps - warmup_steps
		SCREAMING_SNAKE_CASE_  :		str				      =    optax.linear_schedule(init_value=lowerCAmelCase   , end_value=lowerCAmelCase   , transition_steps=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    optax.linear_schedule(init_value=lowerCAmelCase   , end_value=1E-7   , transition_steps=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    optax.join_schedules(schedules=[warmup_fn, decay_fn]   , boundaries=[warmup_steps]	)
		return lr







def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		def weight_decay_mask(lowerCAmelCase							:   Dict	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    traverse_util.flatten_dict(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
				return traverse_util.unflatten_dict(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    scheduler_fn(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    optax.adamw(learning_rate=lowerCAmelCase   , weight_decay=lowerCAmelCase   , mask=lowerCAmelCase	)
		return tx, lr

from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
