


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 							_UpperCamelCase (						A						):

   '''simple docstring'''







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

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         super().setup()
         __lowerCamelCase     :      List[str]       						=						nn.Dense(5       ,  dtype=self.dtype							)


   def __call__(  self  : int       ,  *_lowerCamelCase  : Union[str, Any]       ,  **_lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						super().__call__(*_lowerCamelCase       ,  **_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						self.cls(outputs[2]							)
         return outputs[:2] + (cls_out,)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Tuple    = FlaxBigBirdForNaturalQuestionsModule





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Optional[int]       						=						partial(UpperCAmelCase						,	reduction=jnp.mean      )
      __lowerCamelCase     :      Tuple       						=						cross_entropy(UpperCAmelCase						,	UpperCAmelCase      )
      __lowerCamelCase     :      Tuple       						=						cross_entropy(UpperCAmelCase						,	UpperCAmelCase      )
      __lowerCamelCase     :      str       						=						cross_entropy(UpperCAmelCase						,	UpperCAmelCase      )
      return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							str    = "google/bigbird-roberta-base"
   a_				:							int    = 3000
   a_				:							int    = 1_0500

   a_				:							int    = 128
   a_				:							int    = 3

   a_				:							int    = 1
   a_				:							int    = 5

   # tx_args
   a_				:							float    = 3e-5
   a_				:							float    = 0.0
   a_				:							int    = 2_0000
   a_				:							float    = 0.00_95

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

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         os.makedirs(self.base_dir       ,  exist_ok=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						os.path.join(self.base_dir       ,  self.save_dir							)
         __lowerCamelCase     :      List[str]       						=						self.batch_size_per_device * jax.device_count()



@dataclass
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							int
   a_				:							int    = 4096  # no dynamic padding on TPUs

   def __call__(  self  : Dict       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.collate_fn(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						jax.tree_util.tree_map(_lowerCamelCase       ,  _lowerCamelCase							)
         return batch

   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						self.fetch_inputs(features["""input_ids"""]							)
         __lowerCamelCase     :      str       						=						{
             """input_ids""": jnp.array(_lowerCamelCase       ,  dtype=jnp.intaa							),
             """attention_mask""": jnp.array(_lowerCamelCase       ,  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  : Union[str, Any]       ,  _lowerCamelCase  : list							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						[self._fetch_inputs(_lowerCamelCase							) for ids in input_ids]
         return zip(*_lowerCamelCase							)


   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : list							):






         '''simple docstring'''



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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Dict=None      ):
      """simple docstring"""




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





@partial(jax.pmap						,	axis_name="""batch"""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			int						,	**UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




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

            __lowerCamelCase     :      Tuple       						=						state.apply_fn(**UpperCAmelCase						,	params=UpperCAmelCase						,	dropout_rng=UpperCAmelCase						,	train=UpperCAmelCase      )
            __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      int       						=						outputs

            return state.loss_fn(
                UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	)

      __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						jax.random.split(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[Any]       						=						jax.value_and_grad(UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      str       						=						grad_fn(state.params      )
      __lowerCamelCase     :      Any       						=						jax.lax.pmean({"""loss""": loss}						,	axis_name="""batch"""      )
      __lowerCamelCase     :      Optional[Any]       						=						jax.lax.pmean(UpperCAmelCase						,	"""batch"""      )

      __lowerCamelCase     :      int       						=						state.apply_gradients(grads=UpperCAmelCase      )
      return state, metrics, new_drp_rng





@partial(jax.pmap						,	axis_name="""batch"""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	**UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Optional[Any]       						=						state.apply_fn(**UpperCAmelCase						,	params=state.params						,	train=UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						outputs

      __lowerCamelCase     :      Union[str, Any]       						=						state.loss_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
      __lowerCamelCase     :      Optional[int]       						=						jax.lax.pmean({"""loss""": loss}						,	axis_name="""batch"""      )
      return metrics



class 							_UpperCamelCase (						train_state.TrainState						):

   '''simple docstring'''







   a_				:							Callable    = struct.field(pytree_node=A						)



@dataclass
class 							_UpperCamelCase :

   '''simple docstring'''







   a_				:							Args
   a_				:							Callable
   a_				:							Callable
   a_				:							Callable
   a_				:							Callable
   a_				:							wandb
   a_				:							Callable    = None

   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : int=None							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						model.params
         __lowerCamelCase     :      Optional[int]       						=						TrainState.create(
             apply_fn=model.__call__       ,  params=_lowerCamelCase       ,  tx=_lowerCamelCase       ,  loss_fn=_lowerCamelCase       ,  )
         if ckpt_dir is not None:
               __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						restore_checkpoint(_lowerCamelCase       ,  _lowerCamelCase							)
               __lowerCamelCase     :      Tuple       						=						{
                   """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						, __lowerCamelCase     :      List[Any]       						=						build_tx(**_lowerCamelCase							)
               __lowerCamelCase     :      int       						=						train_state.TrainState(
                   step=_lowerCamelCase       ,  apply_fn=model.__call__       ,  params=_lowerCamelCase       ,  tx=_lowerCamelCase       ,  opt_state=_lowerCamelCase       ,  )
               __lowerCamelCase     :      Optional[int]       						=						args
               __lowerCamelCase     :      Dict       						=						data_collator
               __lowerCamelCase     :      int       						=						lr
               __lowerCamelCase     :      Optional[int]       						=						params
         __lowerCamelCase     :      Any       						=						jax_utils.replicate(_lowerCamelCase							)
         return state

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						self.args
         __lowerCamelCase     :      Optional[int]       						=						len(_lowerCamelCase							) // args.batch_size

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

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

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

   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[int]							):






         '''simple docstring'''



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


   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




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

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

      __lowerCamelCase     :      Any       						=						joblib.load(os.path.join(UpperCAmelCase						,	"""args.joblib"""      )      )
      __lowerCamelCase     :      Optional[Any]       						=						joblib.load(os.path.join(UpperCAmelCase						,	"""data_collator.joblib"""      )      )

      with open(os.path.join(UpperCAmelCase						,	"""training_state.json"""      )						,	"""r"""      ) as f:
            __lowerCamelCase     :      Optional[Any]       						=						json.load(UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						training_state["""step"""]

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Union[str, Any]       						=						scheduler_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

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







def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						len(UpperCAmelCase      )
      for _ in range(UpperCAmelCase      ):
            for i in range(_ % 2						,	arr_size - 1						,	2      ):
                  if arr[i + 1] < arr[i]:
                        __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						arr[i + 1], arr[i]

      return arr


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							list(range(10, 0, -1))
    print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')



