
'''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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		BigBirdConfig
			lowerCamelCase			:		jnp.dtype	      =			jnp.floataa
			lowerCamelCase			:		bool	      =			True

			def        UpperCAmelCase__ (self      ):
								super().setup()
								lowerCamelCase_					:      Tuple     =		nn.Dense(5			,   dtype=self.dtype      )






			def __call__(self			,   *A			,   **A      ):
								lowerCamelCase_					:      Optional[int]     =		super().__call__(*A			,   **A      )
								lowerCamelCase_					:      str     =		self.cls(outputs[2]      )
								return outputs[:2] + (cls_out,)

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Union[str, Any]	      =			FlaxBigBirdForNaturalQuestionsModule




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

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

					lowerCamelCase_					:      Union[str, Any]     =		partial(_lowercase			,       reduction=jnp.mean				)
					lowerCamelCase_					:      Union[str, Any]     =		cross_entropy(_lowercase			,       _lowercase				)
					lowerCamelCase_					:      Any     =		cross_entropy(_lowercase			,       _lowercase				)
					lowerCamelCase_					:      Tuple     =		cross_entropy(_lowercase			,       _lowercase				)
					return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class 	__lowercase				:
			lowerCamelCase			:		str	      =			"google/bigbird-roberta-base"
			lowerCamelCase			:		int	      =			3000
			lowerCamelCase			:		int	      =			1_0500

			lowerCamelCase			:		int	      =			128
			lowerCamelCase			:		int	      =			3

			lowerCamelCase			:		int	      =			1
			lowerCamelCase			:		int	      =			5

			# tx_args
			lowerCamelCase			:		float	      =			3e-5
			lowerCamelCase			:		float	      =			0.0
			lowerCamelCase			:		int	      =			2_0000
			lowerCamelCase			:		float	      =			0.0_0_9_5

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

			def        UpperCAmelCase__ (self      ):
								os.makedirs(self.base_dir			,   exist_ok=A      )
								lowerCamelCase_					:      Dict     =		os.path.join(self.base_dir			,   self.save_dir      )
								lowerCamelCase_					:      int     =		self.batch_size_per_device * jax.device_count()

@dataclass
class 	__lowercase				:
			lowerCamelCase			:		int
			lowerCamelCase			:		int	      =			4096  # no dynamic padding on TPUs

			def __call__(self			,   A      ):
								lowerCamelCase_					:      Any     =		self.collate_fn(A      )
								lowerCamelCase_					:      List[str]     =		jax.tree_util.tree_map(A			,   A      )
								return batch

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.fetch_inputs(features['''input_ids''']      )
								lowerCamelCase_					:      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			,   A      ):
								lowerCamelCase_					:      str     =		[self._fetch_inputs(A      ) for ids in input_ids]
								return zip(*A      )






			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      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 lowercase_	(   _lowercase			,       _lowercase			,       _lowercase=None				)     ->							List[str]:
					'''simple docstring'''

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




@partial(jax.pmap			,       axis_name='''batch'''				)
def lowercase_	(   _lowercase			,       _lowercase			,       **_lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					def loss_fn(_lowercase				):
										lowerCamelCase_					:      str     =		model_inputs.pop('''start_labels'''				)
										lowerCamelCase_					:      int     =		model_inputs.pop('''end_labels'''				)
										lowerCamelCase_					:      Optional[Any]     =		model_inputs.pop('''pooled_labels'''				)

										lowerCamelCase_					:      Tuple     =		state.apply_fn(**_lowercase			,       params=_lowercase			,       dropout_rng=_lowercase			,       train=_lowercase				)
										lowerCamelCase_					:      Dict     =		outputs

										return state.loss_fn(
										    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )

					lowerCamelCase_					:      Dict     =		jax.random.split(_lowercase				)
					lowerCamelCase_					:      Dict     =		jax.value_and_grad(_lowercase				)
					lowerCamelCase_					:      List[Any]     =		grad_fn(state.params				)
					lowerCamelCase_					:      Union[str, Any]     =		jax.lax.pmean({'''loss''': loss}			,       axis_name='''batch'''				)
					lowerCamelCase_					:      Optional[Any]     =		jax.lax.pmean(_lowercase			,       '''batch'''				)

					lowerCamelCase_					:      Optional[Any]     =		state.apply_gradients(grads=_lowercase				)
					return state, metrics, new_drp_rng




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

					lowerCamelCase_					:      Dict     =		model_inputs.pop('''start_labels'''				)
					lowerCamelCase_					:      int     =		model_inputs.pop('''end_labels'''				)
					lowerCamelCase_					:      List[str]     =		model_inputs.pop('''pooled_labels'''				)

					lowerCamelCase_					:      int     =		state.apply_fn(**_lowercase			,       params=state.params			,       train=_lowercase				)
					lowerCamelCase_					:      int     =		outputs

					lowerCamelCase_					:      int     =		state.loss_fn(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
					lowerCamelCase_					:      int     =		jax.lax.pmean({'''loss''': loss}			,       axis_name='''batch'''				)
					return metrics

class 	__lowercase				(  train_state.TrainState    ):
			lowerCamelCase			:		Callable	      =			struct.field(pytree_node=_lowercase    )

@dataclass
class 	__lowercase				:
			lowerCamelCase			:		Args
			lowerCamelCase			:		Callable
			lowerCamelCase			:		Callable
			lowerCamelCase			:		Callable
			lowerCamelCase			:		Callable
			lowerCamelCase			:		wandb
			lowerCamelCase			:		Callable	      =			None

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A=None      ):
								lowerCamelCase_					:      Union[str, Any]     =		model.params
								lowerCamelCase_					:      Tuple     =		TrainState.create(
								    apply_fn=model.__call__			,   params=A			,   tx=A			,   loss_fn=A			,   )
								if ckpt_dir is not None:
													lowerCamelCase_					:      Tuple     =		restore_checkpoint(A			,   A      )
													lowerCamelCase_					:      Union[str, 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,
													}
													lowerCamelCase_					:      Optional[Any]     =		build_tx(**A      )
													lowerCamelCase_					:      int     =		train_state.TrainState(
													    step=A			,   apply_fn=model.__call__			,   params=A			,   tx=A			,   opt_state=A			,   )
													lowerCamelCase_					:      Optional[int]     =		args
													lowerCamelCase_					:      Union[str, Any]     =		data_collator
													lowerCamelCase_					:      str     =		lr
													lowerCamelCase_					:      Optional[int]     =		params
								lowerCamelCase_					:      Tuple     =		jax_utils.replicate(A      )
								return state

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      int     =		self.args
								lowerCamelCase_					:      Optional[Any]     =		len(A      ) // args.batch_size

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

																							lowerCamelCase_					:      Any     =		self.evaluate(A			,   A      )
																							lowerCamelCase_					:      List[Any]     =		{
																							    '''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			,   A			,   A      ):
								lowerCamelCase_					:      Any     =		get_batched_dataset(A			,   self.args.batch_size      )
								lowerCamelCase_					:      str     =		len(A      ) // self.args.batch_size
								lowerCamelCase_					:      List[str]     =		jnp.array(0			,   dtype=jnp.floataa      )
								lowerCamelCase_					:      Tuple     =		0
								for batch in tqdm(A			,   total=A			,   desc='''Evaluating ... '''      ):
													lowerCamelCase_					:      List[Any]     =		self.data_collator(A      )
													lowerCamelCase_					:      Union[str, Any]     =		self.val_step_fn(A			,   **A      )
													running_loss += jax_utils.unreplicate(metrics['''loss''']      )
													i += 1
								return running_loss / i






			def        UpperCAmelCase__ (self			,   A			,   A      ):
								lowerCamelCase_					:      Any     =		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_	(   _lowercase			,       _lowercase				)     ->							List[str]:
					'''simple docstring'''

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

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

					lowerCamelCase_					:      Dict     =		joblib.load(os.path.join(_lowercase			,       '''args.joblib'''				)				)
					lowerCamelCase_					:      Optional[int]     =		joblib.load(os.path.join(_lowercase			,       '''data_collator.joblib'''				)				)

					with open(os.path.join(_lowercase			,       '''training_state.json'''				)			,       '''r'''				) as f:
										lowerCamelCase_					:      List[str]     =		json.load(_lowercase				)
					lowerCamelCase_					:      Union[str, Any]     =		training_state['''step''']

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		num_train_steps - warmup_steps
					lowerCamelCase_					:      str     =		optax.linear_schedule(init_value=_lowercase			,       end_value=_lowercase			,       transition_steps=_lowercase				)
					lowerCamelCase_					:      Optional[int]     =		optax.linear_schedule(init_value=_lowercase			,       end_value=1e-7			,       transition_steps=_lowercase				)
					lowerCamelCase_					:      Tuple     =		optax.join_schedules(schedules=[warmup_fn, decay_fn]			,       boundaries=[warmup_steps]				)
					return lr






def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

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

					lowerCamelCase_					:      Optional[int]     =		scheduler_fn(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

					lowerCamelCase_					:      Dict     =		optax.adamw(learning_rate=_lowercase			,       weight_decay=_lowercase			,       mask=_lowercase				)
					return tx, lr








'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if (direction == 1 and array[indexa] > array[indexa]) or (
					    direction == 0 and array[indexa] < array[indexa]
					):
										lowerCamelCase_,      lowerCamelCase_					:      str     =		array[indexa], array[indexa]




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      int     =		int(length / 2				)
										for i in range(_lowercase			,       low + middle				):
															comp_and_swap(_lowercase			,       _lowercase			,       i + middle			,       _lowercase				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
										bitonic_merge(_lowercase			,       low + middle			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      Dict     =		int(length / 2				)
										bitonic_sort(_lowercase			,       _lowercase			,       _lowercase			,       1				)
										bitonic_sort(_lowercase			,       low + middle			,       _lowercase			,       0				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)


if __name__ == "__main__":
					__lowercase							:   Any					=     input('''Enter numbers separated by a comma:\n''').strip()
					__lowercase							:   Tuple					=     [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=''', ''')


