'''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 (					UpperCamelCase__      ):
    a__			:		BigBirdConfig
    a__			:		jnp.dtype			  =    jnp.floataa
    a__			:		bool			  =    True



    def    _lowercase		(self       :       Dict					):
         super().setup()
         UpperCAmelCase_			       =  nn.Dense(5  ,     dtype=self.dtype					)






    def __call__(self       :       Optional[Any]  ,     *__a       :       Tuple  ,     **__a       :       List[Any]					):
         UpperCAmelCase_			       =  super().__call__(*__a  ,     **__a					)
         UpperCAmelCase_			       =  self.cls(outputs[2]					)
         return outputs[:2] + (cls_out,)




class 			__A (					UpperCamelCase__      ):
    a__			:		str			  =    FlaxBigBirdForNaturalQuestionsModule





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Any   ,       snake_case_			:					List[Any]   ,       snake_case_			:					str   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					List[str]     )       -> str:


     '''simple docstring'''




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

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




@dataclass
class 			__A :
    a__			:		str			  =    "google/bigbird-roberta-base"
    a__			:		int			  =    3_000
    a__			:		int			  =    10_500

    a__			:		int			  =    128
    a__			:		int			  =    3

    a__			:		int			  =    1
    a__			:		int			  =    5

    # tx_args
    a__			:		float			  =    3e-5
    a__			:		float			  =    0.0
    a__			:		int			  =    20_000
    a__			:		float			  =    0.0_0_9_5

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



    def    _lowercase		(self       :       str					):
         os.makedirs(self.base_dir  ,     exist_ok=__a					)
         UpperCAmelCase_			       =  os.path.join(self.base_dir  ,     self.save_dir					)
         UpperCAmelCase_			       =  self.batch_size_per_device * jax.device_count()




@dataclass
class 			__A :
    a__			:		int
    a__			:		int			  =    4_096  # no dynamic padding on TPUs



    def __call__(self       :       List[Any]  ,     __a       :       Any					):
         UpperCAmelCase_			       =  self.collate_fn(__a					)
         UpperCAmelCase_			       =  jax.tree_util.tree_map(__a  ,     __a					)
         return batch



    def    _lowercase		(self       :       Tuple  ,     __a       :       Tuple					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.fetch_inputs(features["input_ids"]					)
         UpperCAmelCase_			       =  {
             "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    _lowercase		(self       :       str  ,     __a       :       list					):
         UpperCAmelCase_			       =  [self._fetch_inputs(__a					) for ids in input_ids]
         return zip(*__a					)






    def    _lowercase		(self       :       List[Any]  ,     __a       :       list					):
         UpperCAmelCase_			       =  [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_							(   snake_case_			:					str   ,       snake_case_			:					Any   ,       snake_case_			:					List[Any]=None     )       -> Any:


     '''simple docstring'''




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





@partial(jax.pmap   ,       axis_name="batch"     )
def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					List[Any]   ,       **snake_case_			:					Any     )       -> Optional[int]:


     '''simple docstring'''




     def loss_fn(snake_case_			:					Tuple     ):
          UpperCAmelCase_			       =  model_inputs.pop("start_labels"     )
          UpperCAmelCase_			       =  model_inputs.pop("end_labels"     )
          UpperCAmelCase_			       =  model_inputs.pop("pooled_labels"     )

          UpperCAmelCase_			       =  state.apply_fn(**snake_case_   ,       params=snake_case_   ,       dropout_rng=snake_case_   ,       train=snake_case_     )
          UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  outputs

          return state.loss_fn(
              snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       )

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

     UpperCAmelCase_			       =  state.apply_gradients(grads=snake_case_     )
     return state, metrics, new_drp_rng





@partial(jax.pmap   ,       axis_name="batch"     )
def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       **snake_case_			:					Any     )       -> str:


     '''simple docstring'''




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

     UpperCAmelCase_			       =  state.apply_fn(**snake_case_   ,       params=state.params   ,       train=snake_case_     )
     UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  outputs

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




class 			__A (					train_state.TrainState      ):
    a__			:		Callable			  =    struct.field(pytree_node=UpperCamelCase__      )




@dataclass
class 			__A :
    a__			:		Args
    a__			:		Callable
    a__			:		Callable
    a__			:		Callable
    a__			:		Callable
    a__			:		wandb
    a__			:		Callable			  =    None



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



    def    _lowercase		(self       :       Tuple  ,     __a       :       Dict  ,     __a       :       str  ,     __a       :       Any					):
         UpperCAmelCase_			       =  self.args
         UpperCAmelCase_			       =  len(__a					) // args.batch_size

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

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






    def    _lowercase		(self       :       Optional[int]  ,     __a       :       List[str]  ,     __a       :       int					):
         UpperCAmelCase_			       =  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_							(   snake_case_			:					Dict   ,       snake_case_			:					Any     )       -> Optional[int]:


     '''simple docstring'''




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

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

     UpperCAmelCase_			       =  joblib.load(os.path.join(snake_case_   ,       "args.joblib"     )     )
     UpperCAmelCase_			       =  joblib.load(os.path.join(snake_case_   ,       "data_collator.joblib"     )     )

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

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





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					Any   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					str     )       -> Union[str, Any]:


     '''simple docstring'''




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






def       lowerCAmelCase_							(   snake_case_			:					Tuple   ,       snake_case_			:					Tuple   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					int     )       -> Optional[Any]:


     '''simple docstring'''




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

     UpperCAmelCase_			       =  scheduler_fn(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )

     UpperCAmelCase_			       =  optax.adamw(learning_rate=snake_case_   ,       weight_decay=snake_case_   ,       mask=snake_case_     )
     return tx, lr








'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							len(_A       )
       for i in range(length - 1       ):
              _lowerCAmelCase     :				List[Any]       =							i
              for k in range(i + 1 ,    _A       ):
                     if collection[k] < collection[least]:
                            _lowerCAmelCase     :				List[str]       =							k
              if least != i:
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							(collection[i], collection[least])
       return collection


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Tuple											=    [int(item) for item in user_input.split(""",""")]
       print(selection_sort(unsorted))

