
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowerCamelCase__     :		List[Any]        = pytest.mark.integration


lowerCamelCase__     :		Dict        = {"""comet"""}
lowerCamelCase__     :		Optional[int]        = importlib.util.find_spec("""fairseq""") is not None

lowerCamelCase__     :		Tuple        = {"""code_eval"""}
lowerCamelCase__     :		str        = os.name == """nt"""

lowerCamelCase__     :		int        = {"""bertscore""", """frugalscore""", """perplexity"""}
lowerCamelCase__     :		Tuple        = importlib.util.find_spec("""transformers""") is not None

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  @wraps(lowercase_ )
  def wrapper(self			,			lowercase_ ):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest("""\"test requires Fairseq\"""" )
    else:
      test_case(self			,			lowercase_ )

  return wrapper

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  @wraps(lowercase_ )
  def wrapper(self			,			lowercase_ ):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest("""\"test requires transformers\"""" )
    else:
      test_case(self			,			lowercase_ )

  return wrapper

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  @wraps(lowercase_ )
  def wrapper(self			,			lowercase_ ):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest("""\"test not supported on Windows\"""" )
    else:
      test_case(self			,			lowercase_ )

  return wrapper

def 		UpperCamelCase						(   )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
  return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished



@parameterized.named_parameters(get_local_metric_names()				)
@for_all_test_methods(
    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_				)
@local
class    _snake_case (						parameterized.TestCase				):
 __lowerCAmelCase	:  Any	   =      {}
 __lowerCAmelCase	:  str	   =      None


 @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""")
 @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""")
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   """[...]"""
   lowercase__	: Optional[Any]     		=   importlib.import_module(
       datasets.load.metric_module_factory(os.path.join("""metrics"""       ,	SCREAMING_SNAKE_CASE_)).module_path)
   lowercase__	: List[str]     		=   datasets.load.import_main_class(metric_module.__name__       ,	dataset=SCREAMING_SNAKE_CASE_)
   # check parameters
   lowercase__	: Any     		=   inspect.signature(metric._compute).parameters
   self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
   # run doctest
   with self.patch_intensive_calls(SCREAMING_SNAKE_CASE_       ,	metric_module.__name__):
     with self.use_local_metrics():
       try:
         lowercase__	: int     		=   doctest.testmod(SCREAMING_SNAKE_CASE_       ,	verbose=SCREAMING_SNAKE_CASE_       ,	raise_on_error=SCREAMING_SNAKE_CASE_)
       except doctest.UnexpectedException as e:
         raise e.exc_info[1]  # raise the exception that doctest caught
   self.assertEqual(results.failed       ,	0)
   self.assertGreater(results.attempted       ,	1)


 @slow
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   """[...]"""
   lowercase__	: Union[str, Any]     		=   importlib.import_module(
       datasets.load.metric_module_factory(os.path.join("""metrics"""       ,	SCREAMING_SNAKE_CASE_)).module_path)
   # run doctest
   with self.use_local_metrics():
     lowercase__	: Union[str, Any]     		=   doctest.testmod(SCREAMING_SNAKE_CASE_       ,	verbose=SCREAMING_SNAKE_CASE_       ,	raise_on_error=SCREAMING_SNAKE_CASE_)
   self.assertEqual(results.failed       ,	0)
   self.assertGreater(results.attempted       ,	1)


 @contextmanager
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if metric_name in self.INTENSIVE_CALLS_PATCHER:
     with self.INTENSIVE_CALLS_PATCHER[metric_name](SCREAMING_SNAKE_CASE_):
       yield
   else:
     yield


 @contextmanager
 def   lowercase__  (       self):


   '''simple docstring'''




   def load_local_metric(SCREAMING_SNAKE_CASE_       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):
     return load_metric(os.path.join("""metrics"""       ,	SCREAMING_SNAKE_CASE_)       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   with patch("""datasets.load_metric""") as mock_load_metric:
     lowercase__	: Union[str, Any]     		=   load_local_metric
     yield



 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   def wrapper(SCREAMING_SNAKE_CASE_):
     lowercase__	: Any     		=   contextmanager(SCREAMING_SNAKE_CASE_)
     lowercase__	: Union[str, Any]     		=   patcher
     return patcher

   return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

  tf.flags.DEFINE_string("""sv"""			,			""""""			,			"""""" )  # handle pytest cli flags



  class    _snake_case (						UpperCAmelCase_				):


   def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


     '''simple docstring'''




     assert len(input_dict["""input_ids"""]) == 2
     return np.array([1.0_3, 1.0_4])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
  with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
    lowercase__	: str     		=   MockedPredictor()
    yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  import torch

  def bert_cos_score_idf(lowercase_			,			lowercase_			,			*lowercase_			,			**lowercase_ ):
    return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_ ) )

  # mock get_model which is supposed to do download a bert model
  # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
  with patch("""bert_score.scorer.get_model""" ), patch(
      """bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
    lowercase__	: int     		=   bert_cos_score_idf
    yield

@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  def load_from_checkpoint(lowercase_ ):



    class    _snake_case :


     def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


       '''simple docstring'''




       assert len(SCREAMING_SNAKE_CASE_) == 2
       lowercase__	: Optional[Any]     		=   [0.1_9, 0.9_2]
       return scores, sum(SCREAMING_SNAKE_CASE_) / len(SCREAMING_SNAKE_CASE_)

    return Model()

  # mock load_from_checkpoint which is supposed to do download a bert model
  # mock load_from_checkpoint which is supposed to do download a bert model
  with patch("""comet.download_model""" ) as mock_download_model:
    lowercase__	: Union[str, Any]     		=   None
    with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
      lowercase__	: List[Any]     		=   load_from_checkpoint
      yield



def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   load_metric(os.path.join("""metrics"""			,			"""seqeval""" ) )
  lowercase__	: int     		=   """ERROR"""
  lowercase__	: Tuple     		=   F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
  with pytest.raises(lowercase_			,			match=re.escape(lowercase_ ) ):
    metric.compute(predictions=[]			,			references=[]			,			scheme=lowercase_ )








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 (						UpperCAmelCase_				):
 __lowerCAmelCase	:  BigBirdConfig
 __lowerCAmelCase	:  jnp.dtype	   =      jnp.floataa
 __lowerCAmelCase	:  bool	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setup()
   lowercase__	: Dict     		=   nn.Dense(5       ,	dtype=self.dtype)



 def __call__(       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   super().__call__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      FlaxBigBirdForNaturalQuestionsModule

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






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

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



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

 __lowerCAmelCase	:  int	   =      128
 __lowerCAmelCase	:  int	   =      3

 __lowerCAmelCase	:  int	   =      1
 __lowerCAmelCase	:  int	   =      5

 # tx_args
 __lowerCAmelCase	:  float	   =      3e-5
 __lowerCAmelCase	:  float	   =      0.0
 __lowerCAmelCase	:  int	   =      20_000
 __lowerCAmelCase	:  float	   =      0.0_095

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


 def   lowercase__  (       self):


   '''simple docstring'''




   os.makedirs(self.base_dir       ,	exist_ok=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   os.path.join(self.base_dir       ,	self.save_dir)
   lowercase__	: str     		=   self.batch_size_per_device * jax.device_count()



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  int
 __lowerCAmelCase	:  int	   =      4_096  # no dynamic padding on TPUs


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.collate_fn(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.fetch_inputs(features["""input_ids"""])
   lowercase__	: str     		=   {
       """input_ids""": jnp.array(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.intaa),
       """attention_mask""": jnp.array(SCREAMING_SNAKE_CASE_       ,	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       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  if seed is not None:
    lowercase__	: Any     		=   dataset.shuffle(seed=lowercase_ )
  for i in range(len(lowercase_ ) // batch_size ):
    lowercase__	: List[str]     		=   dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(lowercase_ )

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			**lowercase_ )		->						int:


  '''simple docstring'''






  def loss_fn(lowercase_ ):
    lowercase__	: Dict     		=   model_inputs.pop("""start_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""end_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""pooled_labels""" )

    lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=lowercase_			,			dropout_rng=lowercase_			,			train=lowercase_ )
    lowercase__		,     lowercase__		,     lowercase__	: Any     		=   outputs

    return state.loss_fn(
        lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

  lowercase__		,     lowercase__	: Optional[int]     		=   jax.random.split(lowercase_ )
  lowercase__	: Tuple     		=   jax.value_and_grad(lowercase_ )
  lowercase__		,     lowercase__	: Optional[int]     		=   grad_fn(state.params )
  lowercase__	: Tuple     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  lowercase__	: Any     		=   jax.lax.pmean(lowercase_			,			"""batch""" )

  lowercase__	: str     		=   state.apply_gradients(grads=lowercase_ )
  return state, metrics, new_drp_rng

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			**lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Tuple     		=   model_inputs.pop("""start_labels""" )
  lowercase__	: List[str]     		=   model_inputs.pop("""end_labels""" )
  lowercase__	: int     		=   model_inputs.pop("""pooled_labels""" )

  lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=state.params			,			train=lowercase_ )
  lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   outputs

  lowercase__	: Optional[Any]     		=   state.loss_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: List[str]     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  return metrics



class    _snake_case (						train_state.TrainState				):
 __lowerCAmelCase	:  Callable	   =      struct.field(pytree_node=UpperCAmelCase_				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Args
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  wandb
 __lowerCAmelCase	:  Callable	   =      None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.args
   lowercase__	: List[str]     		=   len(SCREAMING_SNAKE_CASE_) // args.batch_size

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

         lowercase__	: Tuple     		=   self.evaluate(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
         lowercase__	: List[Any]     		=   {
             """step""": state_step.item(),
             """eval_loss""": eval_loss.item(),
             """tr_loss""": tr_loss,
             """lr""": lr.item(),
         }
         tqdm.write(str(SCREAMING_SNAKE_CASE_))
         self.logger.log(SCREAMING_SNAKE_CASE_       ,	commit=SCREAMING_SNAKE_CASE_)

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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






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

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

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

  with open(os.path.join(lowercase_			,			"""training_state.json""" )			,			"""r""" ) as f:
    lowercase__	: int     		=   json.load(lowercase_ )
  lowercase__	: Optional[Any]     		=   training_state["""step"""]

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   num_train_steps - warmup_steps
  lowercase__	: int     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=lowercase_			,			transition_steps=lowercase_ )
  lowercase__	: Optional[int]     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=1E-7			,			transition_steps=lowercase_ )
  lowercase__	: Any     		=   optax.join_schedules(schedules=[warmup_fn, decay_fn]			,			boundaries=[warmup_steps] )
  return lr



def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






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

  lowercase__	: Optional[int]     		=   scheduler_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: int     		=   optax.adamw(learning_rate=lowercase_			,			weight_decay=lowercase_			,			mask=lowercase_ )
  return tx, lr






