
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 					lowerCamelCase		(  a__  ):
					UpperCAmelCase__    :							Tuple									=		42
					UpperCAmelCase__    :							Optional[Any]									=		jnp.floataa
					UpperCAmelCase__    :							List[Any]									=		True





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





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







class 					lowerCamelCase		(  a__  ):
					UpperCAmelCase__    :							Any									=		FlaxBigBirdForNaturalQuestionsModule



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							Optional[Any]:
				"""simple docstring"""

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

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







@dataclass
class 					lowerCamelCase		:
					UpperCAmelCase__    :							str									=		"google/bigbird-roberta-base"
					UpperCAmelCase__    :							int									=		30_00
					UpperCAmelCase__    :							int									=		1_05_00

					UpperCAmelCase__    :							Optional[Any]									=		1_28
					UpperCAmelCase__    :							Union[str, Any]									=		3

					UpperCAmelCase__    :							str									=		1
					UpperCAmelCase__    :							str									=		5

					# tx_args
					UpperCAmelCase__    :							Optional[Any]									=		3e-5
					UpperCAmelCase__    :							str									=		0.0
					UpperCAmelCase__    :							Any									=		2_00_00
					UpperCAmelCase__    :							int									=		0.0095

					UpperCAmelCase__    :							List[str]									=		"bigbird-roberta-natural-questions"
					UpperCAmelCase__    :							Tuple									=		"training-expt"
					UpperCAmelCase__    :							Any									=		"data/nq-training.jsonl"
					UpperCAmelCase__    :							Tuple									=		"data/nq-validation.jsonl"





					def 		UpperCAmelCase(self				:   str       )						->						Optional[int]:
									os.makedirs(self.base_dir    ,   exist_ok=_A       )
									snake_case								= os.path.join(self.base_dir    ,   self.save_dir       )
									snake_case								= self.batch_size_per_device * jax.device_count()







@dataclass
class 					lowerCamelCase		:
					UpperCAmelCase__    :							int									=		42
					UpperCAmelCase__    :							Optional[int]									=		40_96  # no dynamic padding on TPUs





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





					def 		UpperCAmelCase(self				:   int    ,   _A				:   Any       )						->						Tuple:
									snake_case								= self.fetch_inputs(features["input_ids"]       )
									snake_case								= {
									    "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				:   Dict    ,   _A				:   list       )						->						Dict:
									snake_case								= [self._fetch_inputs(_A       ) for ids in input_ids]
									return zip(*_A       )





					def 		UpperCAmelCase(self				:   str    ,   _A				:   list       )						->						Dict:
									snake_case								= [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 						lowercase_      (       A__			,		A__			,		A__=None		)							->							Dict:
				"""simple docstring"""

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



@partial(jax.pmap			,		axis_name="batch"		)
def 						lowercase_      (       A__			,		A__			,		**A__		)							->							int:
				"""simple docstring"""

				def loss_fn(A__		):
								snake_case								= model_inputs.pop("start_labels"		)
								snake_case								= model_inputs.pop("end_labels"		)
								snake_case								= model_inputs.pop("pooled_labels"		)

								snake_case								= state.apply_fn(**_lowerCamelCase			,		params=_lowerCamelCase			,		dropout_rng=_lowerCamelCase			,		train=_lowerCamelCase		)
								snake_case								= outputs

								return state.loss_fn(
								    _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		)

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

				snake_case								= state.apply_gradients(grads=_lowerCamelCase		)
				return state, metrics, new_drp_rng



@partial(jax.pmap			,		axis_name="batch"		)
def 						lowercase_      (       A__			,		**A__		)							->							List[str]:
				"""simple docstring"""

				snake_case								= model_inputs.pop("start_labels"		)
				snake_case								= model_inputs.pop("end_labels"		)
				snake_case								= model_inputs.pop("pooled_labels"		)

				snake_case								= state.apply_fn(**_lowerCamelCase			,		params=state.params			,		train=_lowerCamelCase		)
				snake_case								= outputs

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







class 					lowerCamelCase		(  train_state.TrainState  ):
					UpperCAmelCase__    :							Tuple									=		struct.field(pytree_node=a__  )







@dataclass
class 					lowerCamelCase		:
					UpperCAmelCase__    :							Optional[int]									=		42
					UpperCAmelCase__    :							Any									=		42
					UpperCAmelCase__    :							List[Any]									=		42
					UpperCAmelCase__    :							str									=		42
					UpperCAmelCase__    :							Dict									=		42
					UpperCAmelCase__    :							Optional[int]									=		42
					UpperCAmelCase__    :							Tuple									=		None





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





					def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   List[Any]    ,   _A				:   Optional[int]       )						->						str:
									snake_case								= self.args
									snake_case								= len(_A       ) // args.batch_size

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

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





					def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict    ,   _A				:   Optional[int]       )						->						Any:
									snake_case								= 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 						lowercase_      (       A__			,		A__		)							->							Tuple:
				"""simple docstring"""

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

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

				snake_case								= joblib.load(os.path.join(_lowerCamelCase			,		"args.joblib"		)		)
				snake_case								= joblib.load(os.path.join(_lowerCamelCase			,		"data_collator.joblib"		)		)

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

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



def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							Dict:
				"""simple docstring"""

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





def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__		)							->							Optional[int]:
				"""simple docstring"""

				def weight_decay_mask(A__		):
								snake_case								= traverse_util.flatten_dict(_lowerCamelCase		)
								snake_case								= {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
								return traverse_util.unflatten_dict(_lowerCamelCase		)

				snake_case								= scheduler_fn(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase		)

				snake_case								= optax.adamw(learning_rate=_lowerCamelCase			,		weight_decay=_lowerCamelCase			,		mask=_lowerCamelCase		)
				return tx, lr



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 						lowercase_      (       A__		)							->							bool:
    """simple docstring"""

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


if __name__ == "__main__":
   print(f"{solution() = }")

