
"""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__						(      __lowerCAmelCase						):
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				jnp.floataa
   __lowerCAmelCase            =				True
   def 			__magic_name__			(			self				):
          super().setup()
          lowercase    :				int       		=							nn.Dense(5	,    dtype=self.dtype				)

   def __call__(			self	,    *_a	,    **_a				):
          lowercase    :				Dict       		=							super().__call__(*_a	,    **_a				)
          lowercase    :				str       		=							self.cls(outputs[2]				)
          return outputs[:2] + (cls_out,)


class 		a__						(      __lowerCAmelCase						):
   __lowerCAmelCase            =				FlaxBigBirdForNaturalQuestionsModule


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

       lowercase    :				Tuple       		=							partial(_SCREAMING_SNAKE_CASE     , reduction=jnp.mean  )
       lowercase    :				List[str]       		=							cross_entropy(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
       lowercase    :				Union[str, Any]       		=							cross_entropy(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
       lowercase    :				Any       		=							cross_entropy(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
       return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class 		a__						:
   __lowerCAmelCase            =				"""google/bigbird-roberta-base"""
   __lowerCAmelCase            =				3000
   __lowerCAmelCase            =				1_0500

   __lowerCAmelCase            =				128
   __lowerCAmelCase            =				3

   __lowerCAmelCase            =				1
   __lowerCAmelCase            =				5

   # tx_args
   __lowerCAmelCase            =				3E-5
   __lowerCAmelCase            =				0.0
   __lowerCAmelCase            =				2_0000
   __lowerCAmelCase            =				0.0095

   __lowerCAmelCase            =				"""bigbird-roberta-natural-questions"""
   __lowerCAmelCase            =				"""training-expt"""
   __lowerCAmelCase            =				"""data/nq-training.jsonl"""
   __lowerCAmelCase            =				"""data/nq-validation.jsonl"""
   def 			__magic_name__			(			self				):
          os.makedirs(self.base_dir	,    exist_ok=_a				)
          lowercase    :				str       		=							os.path.join(self.base_dir	,    self.save_dir				)
          lowercase    :				List[str]       		=							self.batch_size_per_device * jax.device_count()


@dataclass
class 		a__						:
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				4096  # no dynamic padding on TPUs
   def __call__(			self	,    _a				):
          lowercase    :				int       		=							self.collate_fn(_a				)
          lowercase    :				Optional[int]       		=							jax.tree_util.tree_map(_a	,    _a				)
          return batch
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Dict       		=							self.fetch_inputs(features["input_ids"]				)
          lowercase    :				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 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[self._fetch_inputs(_a				) for ids in input_ids]
          return zip(*_a				)

   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, 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   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       List[Any]     , __snake_case				:       Tuple=None  ) ->				List[Any]:
       if seed is not None:
              lowercase    :				int       		=							dataset.shuffle(seed=_SCREAMING_SNAKE_CASE  )
       for i in range(len(_SCREAMING_SNAKE_CASE  ) // batch_size  ):
              lowercase    :				Union[str, Any]       		=							dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(_SCREAMING_SNAKE_CASE  )


@partial(jax.pmap     , axis_name="batch"  )
def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       int     , **__snake_case				:       str  ) ->				Any:
       def loss_fn(__snake_case				:       Any  ):
              lowercase    :				str       		=							model_inputs.pop("start_labels"  )
              lowercase    :				Dict       		=							model_inputs.pop("end_labels"  )
              lowercase    :				Optional[int]       		=							model_inputs.pop("pooled_labels"  )

              lowercase    :				Optional[Any]       		=							state.apply_fn(**_SCREAMING_SNAKE_CASE     , params=_SCREAMING_SNAKE_CASE     , dropout_rng=_SCREAMING_SNAKE_CASE     , train=_SCREAMING_SNAKE_CASE  )
              lowercase    :				Optional[int]       		=							outputs

              return state.loss_fn(
                  _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , )

       lowercase    :				Union[str, Any]       		=							jax.random.split(_SCREAMING_SNAKE_CASE  )
       lowercase    :				List[Any]       		=							jax.value_and_grad(_SCREAMING_SNAKE_CASE  )
       lowercase    :				str       		=							grad_fn(state.params  )
       lowercase    :				Optional[int]       		=							jax.lax.pmean({"loss": loss}     , axis_name="batch"  )
       lowercase    :				int       		=							jax.lax.pmean(_SCREAMING_SNAKE_CASE     , "batch"  )

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


@partial(jax.pmap     , axis_name="batch"  )
def   __magic_name__	(      __snake_case				:       Union[str, Any]     , **__snake_case				:       List[str]  ) ->				Optional[Any]:
       lowercase    :				Optional[int]       		=							model_inputs.pop("start_labels"  )
       lowercase    :				int       		=							model_inputs.pop("end_labels"  )
       lowercase    :				Dict       		=							model_inputs.pop("pooled_labels"  )

       lowercase    :				Union[str, Any]       		=							state.apply_fn(**_SCREAMING_SNAKE_CASE     , params=state.params     , train=_SCREAMING_SNAKE_CASE  )
       lowercase    :				int       		=							outputs

       lowercase    :				Optional[int]       		=							state.loss_fn(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
       lowercase    :				Tuple       		=							jax.lax.pmean({"loss": loss}     , axis_name="batch"  )
       return metrics

class 		a__						(      train_state.TrainState						):
   __lowerCAmelCase            =				struct.field(pytree_node=__lowerCAmelCase						)


@dataclass
class 		a__						:
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				42
   __lowerCAmelCase            =				None
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a=None				):
          lowercase    :				Dict       		=							model.params
          lowercase    :				Any       		=							TrainState.create(
              apply_fn=model.__call__	,    params=_a	,    tx=_a	,    loss_fn=_a	,    )
          if ckpt_dir is not None:
                 lowercase    :				Any       		=							restore_checkpoint(_a	,    _a				)
                 lowercase    :				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,
                 }
                 lowercase    :				str       		=							build_tx(**_a				)
                 lowercase    :				Optional[Any]       		=							train_state.TrainState(
                     step=_a	,    apply_fn=model.__call__	,    params=_a	,    tx=_a	,    opt_state=_a	,    )
                 lowercase    :				int       		=							args
                 lowercase    :				Union[str, Any]       		=							data_collator
                 lowercase    :				Any       		=							lr
                 lowercase    :				Dict       		=							params
          lowercase    :				Tuple       		=							jax_utils.replicate(_a				)
          return state
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				int       		=							self.args
          lowercase    :				str       		=							len(_a				) // args.batch_size

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

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

   def 			__magic_name__			(			self	,    _a	,    _a				):
          lowercase    :				List[Any]       		=							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   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       List[Any]  ) ->				Optional[Any]:
       print(f"""RESTORING CHECKPOINT FROM {save_dir}"""     , end=" ... "  )
       with open(os.path.join(_SCREAMING_SNAKE_CASE     , "flax_model.msgpack"  )     , "rb"  ) as f:
              lowercase    :				int       		=							from_bytes(state.params     , f.read()  )

       with open(os.path.join(_SCREAMING_SNAKE_CASE     , "opt_state.msgpack"  )     , "rb"  ) as f:
              lowercase    :				Optional[Any]       		=							from_bytes(state.opt_state     , f.read()  )

       lowercase    :				Optional[Any]       		=							joblib.load(os.path.join(_SCREAMING_SNAKE_CASE     , "args.joblib"  )  )
       lowercase    :				int       		=							joblib.load(os.path.join(_SCREAMING_SNAKE_CASE     , "data_collator.joblib"  )  )

       with open(os.path.join(_SCREAMING_SNAKE_CASE     , "training_state.json"  )     , "r"  ) as f:
              lowercase    :				Any       		=							json.load(_SCREAMING_SNAKE_CASE  )
       lowercase    :				Optional[Any]       		=							training_state['step']

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


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       str     , __snake_case				:       Any     , __snake_case				:       int  ) ->				Optional[int]:
       lowercase    :				str       		=							num_train_steps - warmup_steps
       lowercase    :				str       		=							optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE     , end_value=_SCREAMING_SNAKE_CASE     , transition_steps=_SCREAMING_SNAKE_CASE  )
       lowercase    :				List[Any]       		=							optax.linear_schedule(init_value=_SCREAMING_SNAKE_CASE     , end_value=1E-7     , transition_steps=_SCREAMING_SNAKE_CASE  )
       lowercase    :				int       		=							optax.join_schedules(schedules=[warmup_fn, decay_fn]     , boundaries=[warmup_steps]  )
       return lr


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       Optional[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       List[Any]     , __snake_case				:       List[Any]  ) ->				Tuple:
       def weight_decay_mask(__snake_case				:       int  ):
              lowercase    :				List[Any]       		=							traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE  )
              lowercase    :				List[str]       		=							{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
              return traverse_util.unflatten_dict(_SCREAMING_SNAKE_CASE  )

       lowercase    :				List[str]       		=							scheduler_fn(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )

       lowercase    :				Any       		=							optax.adamw(learning_rate=_SCREAMING_SNAKE_CASE     , weight_decay=_SCREAMING_SNAKE_CASE     , mask=_SCREAMING_SNAKE_CASE  )
       return tx, lr



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


