

'''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 					A__      (      _snake_case			):
    lowercase						=		42
    lowercase						=		jnp.floataa
    lowercase						=		True



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           super().setup()
           A_						=      nn.Dense(5		,				dtype=self.dtype       )







    def __call__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      super().__call__(*UpperCamelCase__		,				**UpperCamelCase__       )
           A_						=      self.cls(outputs[2]       )
           return outputs[:2] + (cls_out,)


class 					A__      (      _snake_case			):
    lowercase						=		FlaxBigBirdForNaturalQuestionsModule

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

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


@dataclass
class 					A__      :
    lowercase						=		"google/bigbird-roberta-base"
    lowercase						=		3_000
    lowercase						=		10_500

    lowercase						=		128
    lowercase						=		3

    lowercase						=		1
    lowercase						=		5

    # tx_args
    lowercase						=		3e-5
    lowercase						=		0.0
    lowercase						=		20_000
    lowercase						=		0.0095

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



    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           os.makedirs(self.base_dir		,				exist_ok=UpperCamelCase__       )
           A_						=      os.path.join(self.base_dir		,				self.save_dir       )
           A_						=      self.batch_size_per_device * jax.device_count()


@dataclass
class 					A__      :
    lowercase						=		42
    lowercase						=		4_096  # no dynamic padding on TPUs



    def __call__(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      self.collate_fn(UpperCamelCase__       )
           A_						=      jax.tree_util.tree_map(UpperCamelCase__		,				UpperCamelCase__       )
           return batch



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_					,			A_						=      self.fetch_inputs(features["""input_ids"""]       )
           A_						=      {
               """input_ids""": jnp.array(UpperCamelCase__		,				dtype=jnp.intaa       ),
               """attention_mask""": jnp.array(UpperCamelCase__		,				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 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      [self._fetch_inputs(UpperCamelCase__       ) for ids in input_ids]
           return zip(*UpperCamelCase__       )







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      [1 for _ in range(len(UpperCamelCase__       )       )]
           while len(UpperCamelCase__       ) < self.max_length:
                  input_ids.append(self.pad_id       )
                  attention_mask.append(0       )
           return input_ids, attention_mask

def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None				)			->			int:
       if seed is not None:
              A_						=      dataset.shuffle(seed=UpperCAmelCase__				)
       for i in range(len(UpperCAmelCase__				) // batch_size				):
              A_						=      dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(UpperCAmelCase__				)
@partial(jax.pmap,       axis_name="""batch"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       **UpperCAmelCase__				)			->			Any:
       def loss_fn(UpperCAmelCase__				):
              A_						=      model_inputs.pop("""start_labels"""				)
              A_						=      model_inputs.pop("""end_labels"""				)
              A_						=      model_inputs.pop("""pooled_labels"""				)

              A_						=      state.apply_fn(**UpperCAmelCase__,       params=UpperCAmelCase__,       dropout_rng=UpperCAmelCase__,       train=UpperCAmelCase__				)
              A_					,			A_					,			A_						=      outputs

              return state.loss_fn(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

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

       A_						=      state.apply_gradients(grads=UpperCAmelCase__				)
       return state, metrics, new_drp_rng
@partial(jax.pmap,       axis_name="""batch"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       **UpperCAmelCase__				)			->			List[str]:
       A_						=      model_inputs.pop("""start_labels"""				)
       A_						=      model_inputs.pop("""end_labels"""				)
       A_						=      model_inputs.pop("""pooled_labels"""				)

       A_						=      state.apply_fn(**UpperCAmelCase__,       params=state.params,       train=UpperCAmelCase__				)
       A_					,			A_					,			A_						=      outputs

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


class 					A__      (      train_state.TrainState			):
    lowercase						=		struct.field(pytree_node=_snake_case			)


@dataclass
class 					A__      :
    lowercase						=		42
    lowercase						=		42
    lowercase						=		42
    lowercase						=		42
    lowercase						=		42
    lowercase						=		42
    lowercase						=		None



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      int:





           '''simple docstring'''
           A_						=      model.params
           A_						=      TrainState.create(
               apply_fn=model.__call__		,				params=UpperCamelCase__		,				tx=UpperCamelCase__		,				loss_fn=UpperCamelCase__		,				)
           if ckpt_dir is not None:
                  A_					,			A_					,			A_					,			A_					,			A_						=      restore_checkpoint(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      {
                      """lr""": args.lr,
                      """init_lr""": args.init_lr,
                      """warmup_steps""": args.warmup_steps,
                      """num_train_steps""": num_train_steps,
                      """weight_decay""": args.weight_decay,
                  }
                  A_					,			A_						=      build_tx(**UpperCamelCase__       )
                  A_						=      train_state.TrainState(
                      step=UpperCamelCase__		,				apply_fn=model.__call__		,				params=UpperCamelCase__		,				tx=UpperCamelCase__		,				opt_state=UpperCamelCase__		,				)
                  A_						=      args
                  A_						=      data_collator
                  A_						=      lr
                  A_						=      params
           A_						=      jax_utils.replicate(UpperCamelCase__       )
           return state



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      self.args
           A_						=      len(UpperCamelCase__       ) // args.batch_size

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

                                A_						=      self.evaluate(UpperCamelCase__		,				UpperCamelCase__       )
                                A_						=      {
                                    """step""": state_step.item(),
                                    """eval_loss""": eval_loss.item(),
                                    """tr_loss""": tr_loss,
                                    """lr""": lr.item(),
                                }
                                tqdm.write(str(UpperCamelCase__       )       )
                                self.logger.log(UpperCamelCase__		,				commit=UpperCamelCase__       )

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



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      get_batched_dataset(UpperCamelCase__		,				self.args.batch_size       )
           A_						=      len(UpperCamelCase__       ) // self.args.batch_size
           A_						=      jnp.array(0		,				dtype=jnp.floataa       )
           A_						=      0
           for batch in tqdm(UpperCamelCase__		,				total=UpperCamelCase__		,				desc="""Evaluating ... """       ):
                  A_						=      self.data_collator(UpperCamelCase__       )
                  A_						=      self.val_step_fn(UpperCamelCase__		,				**UpperCamelCase__       )
                  running_loss += jax_utils.unreplicate(metrics["""loss"""]       )
                  i += 1
           return running_loss / i







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





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

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

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

       A_						=      joblib.load(os.path.join(UpperCAmelCase__,       """args.joblib"""				)				)
       A_						=      joblib.load(os.path.join(UpperCAmelCase__,       """data_collator.joblib"""				)				)

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

       print("""DONE"""				)
       return params, opt_state, step, args, data_collator
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
       A_						=      num_train_steps - warmup_steps
       A_						=      optax.linear_schedule(init_value=UpperCAmelCase__,       end_value=UpperCAmelCase__,       transition_steps=UpperCAmelCase__				)
       A_						=      optax.linear_schedule(init_value=UpperCAmelCase__,       end_value=1e-7,       transition_steps=UpperCAmelCase__				)
       A_						=      optax.join_schedules(schedules=[warmup_fn, decay_fn],       boundaries=[warmup_steps]				)
       return lr






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

       A_						=      scheduler_fn(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       A_						=      optax.adamw(learning_rate=UpperCAmelCase__,       weight_decay=UpperCAmelCase__,       mask=UpperCAmelCase__				)
       return tx, lr



'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      FileLock(str(tmpdir / """foo.lock"""				)				)
       A_						=      0.01
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     A_						=      time.time()
                     locka.acquire(UpperCAmelCase__				)
                     assert time.time() - _start > timeout







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Dict:
       A_						=      """a""" * 10_00 + """.lock"""
       A_						=      FileLock(str(tmpdir / filename				)				)
       assert locka._lock_file.endswith(""".lock"""				)
       assert not locka._lock_file.endswith(UpperCAmelCase__				)
       assert len(os.path.basename(locka._lock_file				)				) <= 2_55
       A_						=      FileLock(tmpdir / filename				)
       with locka.acquire():
              with pytest.raises(UpperCAmelCase__				):
                     locka.acquire(0				)
