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 __snake_case    (						lowerCamelCase__		):
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							jnp.floataa
			lowerCAmelCase_														=							True




			def      __a						(   self      :    Any				):



					"""simple docstring"""






					super().setup()
					SCREAMING_SNAKE_CASE__      							=				nn.Dense(5	,     dtype=self.dtype				)




			def __call__(   self      :    List[str]	,     *_lowercase      :    List[Any]	,     **_lowercase      :    Optional[int]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				super().__call__(*_lowercase	,     **_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				self.cls(outputs[2]				)
					return outputs[:2] + (cls_out,)







class __snake_case    (						lowerCamelCase__		):
			lowerCAmelCase_														=							FlaxBigBirdForNaturalQuestionsModule





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	List[Any]     )							-> int:







		"""simple docstring"""





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

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







@dataclass
class __snake_case    :
			lowerCAmelCase_														=							"google/bigbird-roberta-base"
			lowerCAmelCase_														=							30_00
			lowerCAmelCase_														=							1_05_00

			lowerCAmelCase_														=							1_28
			lowerCAmelCase_														=							3

			lowerCAmelCase_														=							1
			lowerCAmelCase_														=							5

			# tx_args
			lowerCAmelCase_														=							3E-5
			lowerCAmelCase_														=							0.0
			lowerCAmelCase_														=							2_00_00
			lowerCAmelCase_														=							0.0095

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




			def      __a						(   self      :    List[str]				):



					"""simple docstring"""






					os.makedirs(self.base_dir	,     exist_ok=_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				os.path.join(self.base_dir	,     self.save_dir				)
					SCREAMING_SNAKE_CASE__      							=				self.batch_size_per_device * jax.device_count()







@dataclass
class __snake_case    :
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							40_96  # no dynamic padding on TPUs




			def __call__(   self      :    Any	,     _lowercase      :    Optional[Any]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.collate_fn(_lowercase				)
					SCREAMING_SNAKE_CASE__      							=				jax.tree_util.tree_map(_lowercase	,     _lowercase				)
					return batch




			def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.fetch_inputs(features["""input_ids"""]				)
					SCREAMING_SNAKE_CASE__      							=				{
					    """input_ids""": jnp.array(_lowercase	,     dtype=jnp.intaa				),
					    """attention_mask""": jnp.array(_lowercase	,     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      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				[self._fetch_inputs(_lowercase				) for ids in input_ids]
					return zip(*_lowercase				)




			def      __a						(   self      :    List[str]	,     _lowercase      :    List[Any]				):



					"""simple docstring"""






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





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	Any=None     )							-> Tuple:







		"""simple docstring"""





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



@partial(jax.pmap			,					axis_name="""batch"""     )
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Dict			,					**__UpperCamelCase   :	Dict     )							-> Optional[Any]:







		"""simple docstring"""





		def loss_fn(__UpperCamelCase   :	Any     ):
				SCREAMING_SNAKE_CASE__      							=				model_inputs.pop("""start_labels"""     )
				SCREAMING_SNAKE_CASE__      							=				model_inputs.pop("""end_labels"""     )
				SCREAMING_SNAKE_CASE__      							=				model_inputs.pop("""pooled_labels"""     )

				SCREAMING_SNAKE_CASE__      							=				state.apply_fn(**__snake_case			,					params=__snake_case			,					dropout_rng=__snake_case			,					train=__snake_case     )
				SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				outputs

				return state.loss_fn(
				    __snake_case			,					__snake_case			,					__snake_case			,					__snake_case			,					__snake_case			,					__snake_case			,					)

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

		SCREAMING_SNAKE_CASE__      							=				state.apply_gradients(grads=__snake_case     )
		return state, metrics, new_drp_rng



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







		"""simple docstring"""





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

		SCREAMING_SNAKE_CASE__      							=				state.apply_fn(**__snake_case			,					params=state.params			,					train=__snake_case     )
		SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				outputs

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







class __snake_case    (						train_state.TrainState		):
			lowerCAmelCase_														=							struct.field(pytree_node=lowerCamelCase__		)







@dataclass
class __snake_case    :
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							42
			lowerCAmelCase_														=							None




			def      __a						(   self      :    Optional[int]	,     _lowercase      :    int	,     _lowercase      :    int	,     _lowercase      :    Tuple	,     _lowercase      :    List[str]=None				):



					"""simple docstring"""






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




			def      __a						(   self      :    int	,     _lowercase      :    Dict	,     _lowercase      :    int	,     _lowercase      :    Tuple				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.args
					SCREAMING_SNAKE_CASE__      							=				len(_lowercase				) // args.batch_size

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

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

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




			def      __a						(   self      :    Dict	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Dict				):



					"""simple docstring"""






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




			def      __a						(   self      :    List[str]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    str				):



					"""simple docstring"""






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





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	List[str]     )							-> Union[str, Any]:







		"""simple docstring"""





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

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

		SCREAMING_SNAKE_CASE__      							=				joblib.load(os.path.join(__snake_case			,					"""args.joblib"""     )     )
		SCREAMING_SNAKE_CASE__      							=				joblib.load(os.path.join(__snake_case			,					"""data_collator.joblib"""     )     )

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

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str     )							-> Tuple:







		"""simple docstring"""





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






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	str     )							-> Dict:







		"""simple docstring"""





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

		SCREAMING_SNAKE_CASE__      							=				scheduler_fn(__snake_case			,					__snake_case			,					__snake_case			,					__snake_case     )

		SCREAMING_SNAKE_CASE__      							=				optax.adamw(learning_rate=__snake_case			,					weight_decay=__snake_case			,					mask=__snake_case     )
		return tx, lr

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


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