"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{'vocab_file': 'spiece.model'}

lowercase__					       =	{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

lowercase__					       =	{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		PRETRAINED_VOCAB_FILES_MAP
      a__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		["""input_ids""", """attention_mask"""]
      a__        =		[]



      def __init__(						self    ,			lowercase    ,			lowercase="<unk>"    ,			lowercase="<s>"    ,			lowercase="</s>"    ,			lowercase="<pad>"    ,			lowercase="[SEP]"    ,			lowercase="[MASK]"    ,			lowercase="[CLS]"    ,			lowercase = None    ,			**lowercase    ,			)	->			None:





        '''simple docstring'''





        a__:     str										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else bos_token
        a__:     Optional[Any]										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else eos_token
        a__:     int										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else unk_token
        a__:     List[str]										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else pad_token
        a__:     int										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else cls_token
        a__:     Tuple										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        a__:     int										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else mask_token

        a__:     Dict										=	{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowercase    ,			eos_token=lowercase    ,			unk_token=lowercase    ,			pad_token=lowercase    ,			sep_token=lowercase    ,			mask_token=lowercase    ,			cls_token=lowercase    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**lowercase    ,			)

        a__:     List[str]										=	vocab_file

        a__:     Any										=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(lowercase)



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return self.sp_model.get_piece_size()



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{self.convert_ids_to_tokens(lowercase): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab



      def __getstate__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.__dict__.copy()
        a__:     str										=	None
        return state



      def __setstate__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'):
          a__:     int										=	{}

        a__:     List[Any]										=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        return self.sp_model.encode(lowercase    ,			out_type=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        return self.sp_model.piece_to_id(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	self.sp_model.IdToPiece(lowercase)
        return token



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     str										=	[]
        a__:     Dict										=	''
        a__:     List[str]										=	False
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            if not prev_is_special:
              out_string += " "
            out_string += self.sp_model.decode(lowercase) + token
            a__:     Union[str, Any]										=	True
            a__:     int										=	[]
          else:
            current_sub_tokens.append(lowercase)
            a__:     int										=	False
        out_string += self.sp_model.decode(lowercase)
        return out_string.strip()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = False    ,			lowercase = None    ,			lowercase = True    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	kwargs.pop('use_source_tokenizer'    ,			lowercase)

        a__:     Optional[int]										=	self.convert_ids_to_tokens(lowercase    ,			skip_special_tokens=lowercase)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        a__:     str										=	[]
        a__:     str										=	[]
        for token in filtered_tokens:
          if skip_special_tokens and token in self.all_special_ids:
            continue
          if token in self.added_tokens_encoder:
            if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(lowercase))
              a__:     Optional[Any]										=	[]
            sub_texts.append(lowercase)
          else:
            current_sub_text.append(lowercase)
        if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(lowercase))

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
          a__:     int										=	re.sub(r' (\[(MASK|SEP)\])'    ,			r'\1'    ,			' '.join(lowercase))
        else:
          a__:     Optional[int]										=	''.join(lowercase)

        a__:     Dict										=	(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
          a__:     str										=	self.clean_up_tokenization(lowercase)
          return clean_text
        else:
          return text



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			Tuple[str]:





        '''simple docstring'''





        if not os.path.isdir(lowercase):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory')
          return
        a__:     List[str]										=	os.path.join(
            lowercase    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase) and os.path.isfile(self.vocab_file):
          copyfile(self.vocab_file    ,			lowercase)
        elif not os.path.isfile(self.vocab_file):
          with open(lowercase    ,			'wb') as fi:
            a__:     str										=	self.sp_model.serialized_model_proto()
            fi.write(lowercase)

        return (out_vocab_file,)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        a__:     Any										=	[self.cls_token_id]
        a__:     List[Any]										=	[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None    ,			lowercase = False)	->			List[int]:





        '''simple docstring'''





        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=lowercase    ,			token_ids_a=lowercase    ,			already_has_special_tokens=lowercase)

        if token_ids_a is None:
          return [1] + ([0] * len(lowercase)) + [1]
        return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			List[int]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	[self.sep_token_id]
        a__:     Union[str, Any]										=	[self.cls_token_id]
        if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]



"""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    __snake_case       (     __lowerCAmelCase				):
      a__        =		42
      a__        =		jnp.floataa
      a__        =		True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        super().setup()
        a__:     int										=	nn.Dense(5    ,			dtype=self.dtype)



      def __call__(						self    ,			*lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





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







class    __snake_case       (     __lowerCAmelCase				):
      a__        =		FlaxBigBirdForNaturalQuestionsModule







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  def cross_entropy(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   ):
    a__:     Any										=	logits.shape[-1]
    a__:     List[Any]										=	(labels[..., None] == jnp.arange(_SCREAMING_SNAKE_CASE   )[None]).astype('f4'   )
    a__:     List[str]										=	jax.nn.log_softmax(_SCREAMING_SNAKE_CASE		,     axis=-1   )
    a__:     Dict										=	-jnp.sum(labels * logits		,     axis=-1   )
    if reduction is not None:
      a__:     str										=	reduction(_SCREAMING_SNAKE_CASE   )
    return loss

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




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

      a__        =		128
      a__        =		3

      a__        =		1
      a__        =		5

      # tx_args
      a__        =		3e-5
      a__        =		0.0
      a__        =		2_0000
      a__        =		0.0095

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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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







@dataclass
class    __snake_case       :
      a__        =		42
      a__        =		4096  # no dynamic padding on TPUs



      def __call__(						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.collate_fn(lowercase)
        a__:     Optional[int]										=	jax.tree_util.tree_map(lowercase    ,			lowercase)
        return batch



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__							,    a__:     Dict										=	self.fetch_inputs(features['input_ids'])
        a__:     List[Any]										=	{
            '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   lowerCamelCase_   (						self    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	[self._fetch_inputs(lowercase) for ids in input_ids]
        return zip(*lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Union[str, Any]										=	[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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:
  if seed is not None:
    a__:     int										=	dataset.shuffle(seed=_SCREAMING_SNAKE_CASE   )
  for i in range(len(_SCREAMING_SNAKE_CASE   ) // batch_size   ):
    a__:     Union[str, Any]										=	dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(_SCREAMING_SNAKE_CASE   )







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Any:
  def loss_fn(_SCREAMING_SNAKE_CASE   ):
    a__:     str										=	model_inputs.pop('start_labels'   )
    a__:     Dict										=	model_inputs.pop('end_labels'   )
    a__:     Optional[int]										=	model_inputs.pop('pooled_labels'   )

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

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

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







@partial(jax.pmap		,     axis_name='batch'   )
def 						__a   (	_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     Optional[int]										=	model_inputs.pop('start_labels'   )
  a__:     int										=	model_inputs.pop('end_labels'   )
  a__:     Dict										=	model_inputs.pop('pooled_labels'   )

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

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




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







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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	model.params
        a__:     Any										=	TrainState.create(
            apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			loss_fn=lowercase    ,			)
        if ckpt_dir is not None:
          a__							,    a__							,    a__							,    a__							,    a__:     Any										=	restore_checkpoint(lowercase    ,			lowercase)
          a__:     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,
          }
          a__							,    a__:     str										=	build_tx(**lowercase)
          a__:     Optional[Any]										=	train_state.TrainState(
              step=lowercase    ,			apply_fn=model.__call__    ,			params=lowercase    ,			tx=lowercase    ,			opt_state=lowercase    ,			)
          a__:     int										=	args
          a__:     Union[str, Any]										=	data_collator
          a__:     Any										=	lr
          a__:     Dict										=	params
        a__:     Tuple										=	jax_utils.replicate(lowercase)
        return state



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	self.args
        a__:     str										=	len(lowercase) // args.batch_size

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

              a__:     List[Any]										=	self.evaluate(lowercase    ,			lowercase)
              a__:     List[str]										=	{
                  '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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  print(F'RESTORING CHECKPOINT FROM {save_dir}'		,     end=' ... '   )
  with open(os.path.join(_SCREAMING_SNAKE_CASE		,     'flax_model.msgpack'   )		,     'rb'   ) as f:
    a__:     int										=	from_bytes(state.params		,     f.read()   )

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

  a__:     Optional[Any]										=	joblib.load(os.path.join(_SCREAMING_SNAKE_CASE		,     'args.joblib'   )   )
  a__:     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:
    a__:     Any										=	json.load(_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	training_state['step']

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







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  def weight_decay_mask(_SCREAMING_SNAKE_CASE   ):
    a__:     List[Any]										=	traverse_util.flatten_dict(_SCREAMING_SNAKE_CASE   )
    a__:     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   )

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

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


