


'''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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







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







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







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







class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    FlaxBigBirdForNaturalQuestionsModule







def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







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

	__magic_name__	:   Tuple					     =					partial(lowerCAmelCase		,		reduction=jnp.mean		)
	__magic_name__	:   Union[str, Any]					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   int					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Optional[int]					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      str				   =    "google/bigbird-roberta-base"
		A_     :      int				   =    30_00
		A_     :      int				   =    1_05_00

		A_     :      int				   =    1_28
		A_     :      int				   =    3

		A_     :      int				   =    1
		A_     :      int				   =    5

		# tx_args
		A_     :      float				   =    3E-5
		A_     :      float				   =    0.0
		A_     :      int				   =    2_00_00
		A_     :      float				   =    0.0095

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







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







@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      int
		A_     :      int				   =    40_96  # no dynamic padding on TPUs







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







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Dict      )  ->			Tuple:
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.fetch_inputs(features['input_ids']      )
			__magic_name__	:   List[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        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						list      )  ->			Optional[int]:
			__magic_name__	:   Any					     =					[self._fetch_inputs(_A      ) for ids in input_ids]
			return zip(*_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						list      )  ->			Any:
			__magic_name__	:   List[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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     List[str]=None		):





	"""simple docstring"""







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





@partial(jax.pmap		,		axis_name='batch'		)
def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Union[str, Any]		,		**lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	def loss_fn(lowerCAmelCase    :     List[Any]		):
		__magic_name__	:   int					     =					model_inputs.pop('start_labels'		)
		__magic_name__	:   Any					     =					model_inputs.pop('end_labels'		)
		__magic_name__	:   Any					     =					model_inputs.pop('pooled_labels'		)

		__magic_name__	:   Optional[int]					     =					state.apply_fn(**lowerCAmelCase		,		params=lowerCAmelCase		,		dropout_rng=lowerCAmelCase		,		train=lowerCAmelCase		)
		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Dict					     =					outputs

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

	__magic_name__     ,    __magic_name__	:   List[Any]					     =					jax.random.split(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					jax.value_and_grad(lowerCAmelCase		)
	__magic_name__     ,    __magic_name__	:   List[str]					     =					grad_fn(state.params		)
	__magic_name__	:   Union[str, Any]					     =					jax.lax.pmean({'loss': loss}		,		axis_name='batch'		)
	__magic_name__	:   List[str]					     =					jax.lax.pmean(lowerCAmelCase		,		'batch'		)

	__magic_name__	:   Union[str, Any]					     =					state.apply_gradients(grads=lowerCAmelCase		)
	return state, metrics, new_drp_rng





@partial(jax.pmap		,		axis_name='batch'		)
def lowerCamelCase      (      lowerCAmelCase    :     Any		,		**lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					model_inputs.pop('start_labels'		)
	__magic_name__	:   Tuple					     =					model_inputs.pop('end_labels'		)
	__magic_name__	:   Any					     =					model_inputs.pop('pooled_labels'		)

	__magic_name__	:   Any					     =					state.apply_fn(**lowerCAmelCase		,		params=state.params		,		train=lowerCAmelCase		)
	__magic_name__     ,    __magic_name__     ,    __magic_name__	:   str					     =					outputs

	__magic_name__	:   str					     =					state.loss_fn(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Any					     =					jax.lax.pmean({'loss': loss}		,		axis_name='batch'		)
	return metrics
class _lowerCamelCase	(						train_state.TrainState							):



		'''simple docstring'''







		A_     :      Callable				   =    struct.field(pytree_node=lowercase__							)







@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      Args
		A_     :      Callable
		A_     :      Callable
		A_     :      Callable
		A_     :      Callable
		A_     :      wandb
		A_     :      Callable				   =    None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						List[Any]=None      )  ->			int:
			__magic_name__	:   Dict					     =					model.params
			__magic_name__	:   List[Any]					     =					TrainState.create(
			    apply_fn=model.__call__  ,					params=_A  ,					tx=_A  ,					loss_fn=_A  ,					)
			if ckpt_dir is not None:
				__magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__	:   Tuple					     =					restore_checkpoint(_A  ,					_A      )
				__magic_name__	:   Optional[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,
				}
				__magic_name__     ,    __magic_name__	:   int					     =					build_tx(**_A      )
				__magic_name__	:   Optional[Any]					     =					train_state.TrainState(
				    step=_A  ,					apply_fn=model.__call__  ,					params=_A  ,					tx=_A  ,					opt_state=_A  ,					)
				__magic_name__	:   Optional[Any]					     =					args
				__magic_name__	:   str					     =					data_collator
				__magic_name__	:   Any					     =					lr
				__magic_name__	:   List[str]					     =					params
			__magic_name__	:   str					     =					jax_utils.replicate(_A      )
			return state







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Dict  ,					_A		:						Dict  ,					_A		:						str      )  ->			Tuple:
			__magic_name__	:   Any					     =					self.args
			__magic_name__	:   Dict					     =					len(_A      ) // args.batch_size

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

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







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						List[str]  ,					_A		:						List[Any]      )  ->			int:
			__magic_name__	:   Dict					     =					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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







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

	with open(os.path.join(lowerCAmelCase		,		'opt_state.msgpack'		)		,		'rb'		) as f:
		__magic_name__	:   List[str]					     =					from_bytes(state.opt_state		,		f.read()		)

	__magic_name__	:   Union[str, Any]					     =					joblib.load(os.path.join(lowerCAmelCase		,		'args.joblib'		)		)
	__magic_name__	:   Optional[Any]					     =					joblib.load(os.path.join(lowerCAmelCase		,		'data_collator.joblib'		)		)

	with open(os.path.join(lowerCAmelCase		,		'training_state.json'		)		,		'r'		) as f:
		__magic_name__	:   Any					     =					json.load(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					training_state['step']

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







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

	__magic_name__	:   List[Any]					     =					scheduler_fn(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

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



'''simple docstring'''

import socket





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					socket.socket(socket.AF_INET		,		socket.SOCK_STREAM		)
	__magic_name__	:   Union[str, Any]					     =					socket.gethostname()
	__magic_name__	:   int					     =					1_2312

	sock.connect((host, port)		)
	sock.send(b'Hello server!'		)

	with open('Received_file'		,		'wb'		) as out_file:
		print('File opened'		)
		print('Receiving data...'		)
		while True:
			__magic_name__	:   Optional[int]					     =					sock.recv(1024		)
			if not data:
				break
			out_file.write(lowerCAmelCase		)

	print('Successfully received the file'		)
	sock.close()
	print('Connection closed'		)


if __name__ == "__main__":
							main()