
import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()


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		:						BigBirdConfig
    _a		:						jnp.dtype=				jnp.floataa
    _a		:						bool=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setup()
           lowercase							:     List[str]       	=      nn.Dense(5		,dtype=self.dtype      )
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      super().__call__(*snake_case		,**snake_case      )
           lowercase							:     Any       	=      self.cls(outputs[2]      )
           return outputs[:2] + (cls_out,)






class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[Any]=				FlaxBigBirdForNaturalQuestionsModule







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

       lowercase							:     Optional[Any]       	=      partial(SCREAMING_SNAKE_CASE__					,	reduction=jnp.mean				)
       lowercase							:     Optional[int]       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       return (start_loss + end_loss + pooled_loss) / 3

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

    _a		:						int=				128
    _a		:						int=				3

    _a		:						int=				1
    _a		:						int=				5

    # tx_args
    _a		:						float=				3E-5
    _a		:						float=				0.0
    _a		:						int=				2_0000
    _a		:						float=				0.00_95

    _a		:						str=				"bigbird-roberta-natural-questions"
    _a		:						str=				"training-expt"
    _a		:						str=				"data/nq-training.jsonl"
    _a		:						str=				"data/nq-validation.jsonl"
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           os.makedirs(self.base_dir		,exist_ok=snake_case      )
           lowercase							:     Optional[int]       	=      os.path.join(self.base_dir		,self.save_dir      )
           lowercase							:     Optional[int]       	=      self.batch_size_per_device * jax.device_count()






@dataclass
class __snake_case		:
    _a		:						int
    _a		:						int=				4096  # no dynamic padding on TPUs
    def __call__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.collate_fn(snake_case      )
           lowercase							:     Union[str, Any]       	=      jax.tree_util.tree_map(snake_case		,snake_case      )
           return batch
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Union[str, Any]       	=      self.fetch_inputs(features["""input_ids"""]      )
           lowercase							:     Tuple       	=      {
               """input_ids""": jnp.array(snake_case		,dtype=jnp.intaa      ),
               """attention_mask""": jnp.array(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       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      [self._fetch_inputs(snake_case      ) for ids in input_ids]
           return zip(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [1 for _ in range(len(snake_case      )      )]
           while len(snake_case      ) < self.max_length:
                  input_ids.append(self.pad_id      )
                  attention_mask.append(0      )
           return input_ids, attention_mask







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    Any:
       if seed is not None:
              lowercase							:     Optional[int]       	=      dataset.shuffle(seed=SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				) // batch_size				):
              lowercase							:     Optional[Any]       	=      dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(SCREAMING_SNAKE_CASE__				)







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

              lowercase							:     Union[str, Any]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=SCREAMING_SNAKE_CASE__					,	dropout_rng=SCREAMING_SNAKE_CASE__					,	train=SCREAMING_SNAKE_CASE__				)
              lowercase	,			lowercase	,			lowercase							:     List[str]       	=      outputs

              return state.loss_fn(
                  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	)

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

       lowercase							:     str       	=      state.apply_gradients(grads=SCREAMING_SNAKE_CASE__				)
       return state, metrics, new_drp_rng







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

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

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

class __snake_case		(      train_state.TrainState     ):
    _a		:						Callable=				struct.field(pytree_node=lowerCAmelCase     )






@dataclass
class __snake_case		:
    _a		:						Args
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						wandb
    _a		:						Callable=				None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      model.params
           lowercase							:     Any       	=      TrainState.create(
               apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,loss_fn=snake_case		,)
           if ckpt_dir is not None:
                  lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      restore_checkpoint(snake_case		,snake_case      )
                  lowercase							:     List[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							:     Tuple       	=      build_tx(**snake_case      )
                  lowercase							:     str       	=      train_state.TrainState(
                      step=snake_case		,apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,opt_state=snake_case		,)
                  lowercase							:     Any       	=      args
                  lowercase							:     Optional[Any]       	=      data_collator
                  lowercase							:     List[str]       	=      lr
                  lowercase							:     str       	=      params
           lowercase							:     Tuple       	=      jax_utils.replicate(snake_case      )
           return state
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.args
           lowercase							:     Optional[Any]       	=      len(snake_case      ) // args.batch_size

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

                                lowercase							:     int       	=      self.evaluate(snake_case		,snake_case      )
                                lowercase							:     Tuple       	=      {
                                    """step""": state_step.item(),
                                    """eval_loss""": eval_loss.item(),
                                    """tr_loss""": tr_loss,
                                    """lr""": lr.item(),
                                }
                                tqdm.write(str(snake_case      )      )
                                self.logger.log(snake_case		,commit=snake_case      )

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



           '''simple docstring'''







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



           '''simple docstring'''







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       print(f"RESTORING CHECKPOINT FROM {save_dir}"					,	end=""" ... """				)
       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""flax_model.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     str       	=      from_bytes(state.params					,	f.read()				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""opt_state.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     Optional[int]       	=      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							:     Tuple       	=      json.load(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      training_state["""step"""]

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     List[str]       	=      num_train_steps - warmup_steps
       lowercase							:     Dict       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=SCREAMING_SNAKE_CASE__					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=1e-7					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      optax.join_schedules(schedules=[warmup_fn, decay_fn]					,	boundaries=[warmup_steps]				)
       return lr







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       def weight_decay_mask(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[Any]       	=      traverse_util.flatten_dict(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      {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							:     List[str]       	=      optax.adamw(learning_rate=SCREAMING_SNAKE_CASE__					,	weight_decay=SCREAMING_SNAKE_CASE__					,	mask=SCREAMING_SNAKE_CASE__				)
       return tx, lr
